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Heyko Stöber

Conceptual Design Report for a Research

InfrastructureFInal RepoRt FooDmanuFutuRe

This report was developed by all beneficiaries of the FP7-funded project “FoodManufuture - Conceptual Design of a Food Manufacturing Research Infrastructure to boost innovation in the Food

Industry” (Grant Agreement No 289327) under coordination of Aalborg University, Denmark.

FoodManufuture is a joint initiative of ETP Food for Life and ETP MANUFUTURE

Contacts

lisbeth munksgaard (aau)

Coordinator and lead WP 1 Management

[email protected]

José Carlos Caldeira (IneSC poRto)

Lead WP 2 Vision Scenario

[email protected]

andras Sebok (Campden BRI Hu)

Lead WP 3 Gap Analysis

[email protected]

Kerstin lienemann (DIl)

Lead WP 4 Conceptual Design Report

[email protected]

Simon Berner (Fraunhofer ISI)

WP 4 Model Building and Road Mapping

[email protected]

Daniele Rossi (Federalimentare)

Lead WP 5 Communication & Dissemination

[email protected]

www.foodmanufuture.eu

http://etp.fooddrinkeurope.eu/ http://www.manufuture.org/manufacturing/

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taBle oF ContentS

I. exeCutIve SummaRy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

1.1. Why this Conceptual Design Report? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

1.2. Strategy and concept of the proposed Research Infrastructure Food Factory of the Future . . . . . . 3

1.3. Value proposition for stakeholders . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

1.4. Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

1.5. What is it for? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

II. StRateGIC appRoaCH ReSeaRCH InFRaStRuCtuRe FooD

FaCtoRy oF tHe FutuRe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

2.1. Aim of the CDR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

2.2. The EU food and manufacturing industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

2.3. Definition research infrastructure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

2.4. The necessity of CDR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

2.5. Assessment criteria for new research infrastructure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

III. toWaRDS a neW InFRaStRuCtuRe FooD FaCoRty oF tHe FutuRe . . . . . . . . . . . . . .11

3.1. Overall conceptual approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .11

3.2. Vision scenarios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

3.2.1. The Global Micro System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

3.2.2. Ingredients Based Food . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

3.2.3. The Integrated Value Chain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

3.2.4. The Disintegrated Value Chain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

3.2.5. Conclusions for scenarios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

3.3. Gap analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

3.3.1. The methodological approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

3.3.2. Long and short term needs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

3.3.3. Current research infrastructure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

3.3.4. Gaps in food manufacturing research infrastructure . . . . . . . . . . . . . . . . . . . . . . . . . . 22

3.4. Model building . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

3.4.1. Infrastructure Model Drafting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

3.4.2. Methodology of the Success Model Building Process . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

3.4.3. Methodology of the Road Mapping Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

Iv. StRateGIC appRoaCH FoR tHe RI FooD FaCtoRy oF tHe FutuRe . . . . . . . . . . . . . . . 35

4.1. Strategic development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

4.2. Value proposition and expected impact . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

4.2.1. Value proposition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

4.2.2. The expected impact . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

4.3. Synergistic effects in the European environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

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v. ConCeptual DeSIGn oF tHe FooD FaCtoRy oF tHe FutuRe . . . . . . . . . . . . . . . . . . . 42

5.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

5.2.Setting up the Food Factory of the Future . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

5.2.1. Operational and managerial design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

5.2.1.1. Type of research and training . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

5.2.1.2. Membership . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

5.2.1.3. Legal structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

5.2.1.4. Management structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

5.2.1.5. Decision-making rules . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

5.2.1.6. Operational structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

5.2.1.7. Location . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

5.2.1.8. Sources and distribution of funding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

5.2.1.9. Forms of funding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

5.2.1.10. Access to infrastructure and services . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

5.2.1.11. Access to information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

5.2.2. Initialisation and Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

5.2.2.1. Membership . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

5.2.2.2. Governance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

5.2.2.3. Funding and Finanding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

5.2.3. Technical design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

5.3. Financial contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

vI. annex: teCHnICal DeSCRIptIonS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58

5.1. Advanced Processing Technologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59

5.2. Hyperspectral and multispectral imaging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82

5.3. Intelligent wireless sensor network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94

5.4. Enhanced traceability and Radio-frequency identification . . . . . . . . . . . . . . . . . . . . . . . . . .110

5.5. Advanced membrane technologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113

5.6. Management systems for Lean manufacturing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118

5.7. Supporting e-infrastructure to run cloud services . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124

5.8. Pilot plants for implementation of robotics and automation in food production . . . . . . . . . . . .127

5.9. Virtual/augmented reality for simulation and training. . . . . . . . . . . . . . . . . . . . . . . . . . . . .132

5.10. Business models and food manufacturing strategies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .142

5.11. Sustainability Assessment of Food technologies, products and value chains (SAF) . . . . . . . . . 150

vII. annex: aBBRevIatIon . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .156

vIII. annex: aCKnoWleDGement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .158

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I. exeCutIve SummaRy

1.1. Why this Conceptual Design Report?

ContextEuropean Food Manufacturing in 2025 may be far more automatised, sustainable, flexible, intelligent and based on novel business models. How and why? What are the needs and which needs may be cov-ered from inventions made for other sectors? These are some of the questions in focus. Based on visions, mapping and analyses made with and by stakeholders from the food sector and from the manufac-turing technologies sector, FoodManufuture has performed a Conceptual Design Study for a European Food Factory of the Future. This new (virtual) research infrastructure should build on existing infra-structures and give access from any part of Europe. The research infrastructure will provide easy access to state of the art test facilities and thus aims at boosting competitiveness and innovativeness of the food manufacturing sector through cutting-edge research, dedicated and involving knowledge transfer and motivating education. Based on the input received from the consortium of FoodManufuture and the outcomes of two European workshops, colleagues from DIL and Fraunhofer contributed to draft an overall conceptual design report (CDR) which will be presented in the Final Conference on 18th Decem-ber 2013.

aim of the Conceptual Design ReportThe conceptual design report (CDR) aims to present a valuable, relevant concept for a new research in-frastructure to serve the current and future needs of the European food industry: the Food Factory of the Future (FFoF). It should not be considered to be a ready-made solution for the food processing and manufacturing sectors, but should act as basis for further discussion with private and political decision makers in order to implement complementary ideas or suggestions. The CDR will visualise that our approach has a high potential for future Horizon 2020 Research Infrastructure actions by proposing in-tegration of and access to existing national research infrastructures. Where limitations are recognized, alternative strategies have been proposed.

The Food Factory of the Future will present itself as a privileged field for testing, piloting, and demon-strating new and emerging food production innovations. In order to meet the need to extract more value from the R&D and innovation investments, a critical aspect in the current period of severe economic crisis, Horizon2020 will offer stronger support to the market take-up of innovation. Moreover, from a global perspective the development of new markets is for the food industry with increasing export rates, of high importance. This will imply a higher focus on the realization of proof-of-concepts, pilot lines and demonstration plants, as well as new business models for the sector. It will imply as well a better use of the potential of research infrastructures, as well as setting technical standards, and pre-commercial procurement as will be provided by this research infrastructure. The pilots and demonstrators network provided by the FFoF aims at fostering synergies between food researchers and production technologies developers, where technologies are integrated and demonstrated in real or quasi-real settings.

1.2. Strategy and concept of the proposed Research Infrastructure Food Factory of the Future

The main objective of the FFoF is to improve the competitiveness in the European food and manufactur-ing sectors. Therefore we propose that a new research infrastructure be designed that offers appropriate capabilities, services and activities that can be utilised by the industry (large and small) and researchers and will finally boost innovation in the food processing and the manufacturing technologies sectors.

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The main beneficiary of the new research infrastructure will be the food processing industry and manufacturing technologies industry dealing with food related issues, which explicitly includes SMEs. These main beneficiaries can be members or customers of the FFoF. To achieve the aim and to meet the challenges, the FFoF will:

• formaninspiring network of innovative existing pilot size factories and pilot plants to develop and demonstrate manufacturing solutions for the food processing industry

• build on existing RI elements (e.g. research facilities, test bed facilities, services, etc.) as a basis to develop a new generation of combined facilities, resources and related services to provide new manu-facturing solutions

• beadistributed location

• facilitatetheutilisation and maximisation of capacities, knowledge and know-how by industry; and meet expectations and needs of the main RI beneficiaries in a long term perspective

• focusonapplied research, transfer cutting edge technologies, information from the manufacturing sector to the food sector, supporting basic research to applied research by considering education and training have an inclusive membership which is open to a broad range of members

• giveopen access to industry especially to small and medium sized enterprises to utilise the FFoF

• haveaflexible structure to adapt its focus on future demands and challenges and create trust among involved stakeholders in the food manufacturing sector

• provideabalance between confidentiality and exploitation

• beindustry driven. This will be reflected in the overall management structure, in the decision mak-ing rules and the operational structure

strategies

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• befunded by a mixed funding scheme from public and private interested parties

• haveplatforms dealing with key topics identified as gaps of the existing research infrastructure in Europe such as FFoF platforms

FFoF platforms will deal with key topics identified as gaps in current research infrastructures (see above). These platforms will consist of an own sub-network with stakeholders from science and indus-try, if needed, also, from society and public authorities. Each platform will describe how they can give added value to the industry as a main beneficiary of the FFoF by respective key activities and services. The main part of this exercise is the description of new FFoF elements which are currently missing at research institutions and will allow for completion of the overall picture.

Additionally, education, training, knowledge & technology transfer units will be set up to facilitate in-novation processes. These units can be platform specific or serve all platforms.

1.3. value proposition for stakeholders The new Food Factory of the Future will give added value to the food processing and manufacturing industry by:

• easyandaffordableaccesstocuttingedgetechnologiesandfacilities

• speedingupproductdevelopmentbyfoodindustryandspeedingupdevelopmentofequipmentandsolutions suitable for food industry

• providingameetingplaceforthefoodproducingsector,themachineriesandequipmentmanufac-turing sector to foster the dialogue between both sectors, in order to identify new ways to interact and to boost interdisciplinary research activities

• identifyingforthemanufacturingsectornewfieldsofapplicationwithinthefoodsectorandhence,giving both sectors access to new customers and business opportunities in Europe

• givingaccesstotechnologiesanddemonstrationactivitiestomaximisetheutilisationofknowledgegenerated in academia

• offeringtheindustrycustomisedstafftrainingactivities

• havingatransparentandfairintellectualpropertyrights(IPR)regime

The new FFoF will meet and take into account the social, economic and ecological challenges the food and manufacturing sectors are facing today (e.g. sustainable food production, healthy and safe food and reduction of food waste) according to its policy and activities. The impact of the new RI is expected to foster the following areas:

• employment:sustainandcreatejobs

• consumerexpectations:healthyandsafefoods

• economy:createnewbusiness throughproducts,processesandserviceswithhigheraddedvalueconsidering the pricing pressure in the sector

• environment: promoting sustainability in production, respecting the environment and ensure amore effective use of resources

• business:buildinguptrustamongstakeholdersinthefoodprocessingchain

• entrepreneurship

• knowledgeandeducation:createandmaintainnewadvancedknowledgeandskills inEuropeonhigh added-value processes and technologies, as well as entrepreneurial skills

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Consequently, the new Food Factory of the Future will give added value to European consumers who will have better food produced in a more sustainable way and the European food industry which will be more competitive.

1.4. approachThe process towards the CDR has involved stakeholders from the food and manufacturing technologies industry sector in a bottom-up process in order to present a FFoF that builds upon analyses of cutting-edge visions and gaps regarding manufacturing technologies, food chain management, sustainability, business models, technology transfer and education. In this way, the novelty, research quality and effect on innovation of this targeted CDR will enhance the scientific performance of the European Research Area and – through bridging and bonding existing facilities and excellence of food science and manufac-turing solutions science – further increase its attractiveness, visibility and impacts.

The resulting infrastructure description addresses Europe’s urgent need to achieve and maintain an in-ternationally leading position in the food industry and related research areas. The suggested infrastruc-ture focuses on novel trans-disciplinary approaches. This CDR will foster the science based innovation capacity of the sectors. Details about organisation and functioning, such as initialisation and implemen-tation phase of the new RI, membership and type of involvement, governance rules, access to FFoF and IPR, funding options and administrative management are presented one by one.

At policy level, the CDR and the assessment of its technical and economic feasibility in particular will provide a sound basis for agreeing on the most promising concept of a research infrastructure with Eu-ropean dimensions that will speed up innovation in the food sector. In this way, it will give directions to decision makers in research policy with regard to the design and activities of Horizon 2020 and national, regional research and innovation programmes. Partners of FoodManufuture will give wide dissemina-tion of the CDR even after the project end (this will ensure sustainability in the future) and will further create awareness and increase involvement of the stakeholders (in particular within both ETP Food For Life and Manufuture meetings).

1.5. What is it for?In summary, the design of an efficient, flexible and targeted research driven Food Factory of the Future will enable the food sector to obtain an increased benefit from the European research results. It will hereby fulfill the crucial need of adding value to the food sector in Europe through increased growth and competitiveness in both the food and manufacturing sectors.

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II. StRateGIC appRoaCH ReSeaRCH InFRaStRuCtuRe FooD FaCtoRy oF tHe FutuRe

2.1. aim of the CDR

The aim of this conceptual design report (CDR) is the description of a new Research Infrastructure Food Factory of the Future (FFoF).

The CDR is a report prepared by scientists and stakeholders from the food and manufacturing academia and the industry sector. The objective is to present a valuable and relevant strategy and concept for a new research infrastructure to serve the current and future needs of the European food industry, the Food Factory of the Future. This report will explicitly state the value of the propositions of its targeted stakeholders. Experts from other existing non-food research infrastructures have also been involved.

It is important to note that the present report is not considered to be a ready-made solution for the food processing and manufacturing sectors, but should act as basis for further discussion with private and political decision makers in order to implement complementary ideas or suggestions. Therefore, oppor-tunities how to implement the new research infrastructure are discussed in this report.

Heyko Stöber

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2.2. the eu food and manufacturing industry

about the eu food and drink industry The EU food and drink industry is the largest manufacturing sector in terms of turnover, employment and value added. In spite of the current economic crisis, the industry is a healthy and strong pillar of the EU economy.

The EU food and drink industry brings in an annual turnover of €1,017 billion and value added of €203 billion while it generates 4.25 million jobs [Eurostat 2011]. This represents 14.9% of the total manufactur-ing sector turnover and 15% of the total manufacturing sector employment in the EU. This high employ-ment rate also makes the food and drink industry the leading employer in the EU manufacturing sector, with jobs spread across all Member States and, in particular, in rural areas.

The industry is highly diverse with over 287,000 companies, 285,000 of which are small and medium-sized enterprises (SMEs), accounting for almost half of the total industry turnover and two-thirds of overall industry employment [Eurostat 2010]. The food and drink manufacturing industry has also a very strong local element, using 70% of agricultural raw materials produced in the EU.

The EU is also the largest food and drink global exporter and the second largest importer, with the total value of exports reaching €86.2 billion and the value of imports reaching €63.2 billion [Eurostat 2012]. However, the EU share of global food and drink trade has been decreasing for the past years (16.1% in 2012 and 16.5% in 2011 [UN Comtrade 2012] while, at the same time, a number of emerging companies increased their own global export market share. In terms of private R&D investment, the EU has sus-tained its levels for the past years but it still trails behind its international peers. Private R&D invest-ment in the EU was €1.9 billion in 2011, whilst elsewhere it was €5.6 billion for the same year [2012 EU Industrial R&D Investment Scoreboard, JRC and DG RTD]. In the EU manufacturing sector as low as 1.5% private R&D investment can be seen for food and drink products, in comparison to that of 23.2% for automobile [Comparisons of EU top 1,000 companies, 2012 EU Industrial R&D Investment Scoreboard, JRC and DG RTD].

The EU food and drink industry is highly committed to solving adverse environmental impacts while maintaining food safety, quality, satisfying consumer demands and reducing food waste. Numerous food and drink companies are working towards a zero waste to landfill in the coming years. The in-dustry leads a worldwide movement towards a greener economy through investment in low-carbon technologies, and resource efficient solutions. For example, in the space of 9 years, 1998-2008, industry cut greenhouse gas emissions by 18% and now strives to use 100% of agricultural resources they put into food production [EEA, Eurostat, 2008].

A new research infrastructure in food manufacturing could foster innovation, competitiveness and productivity while also helping the industry bring new and improved products to the market.

The objective of this report is to promote the value of research and innovation and present a conceptual design study on how a European Infrastructure can be implemented in the EU.

2.3. Definition of research infrastructure The definition of research infrastructure in this CDR corresponds to the one used for the research infra-structure action within the Seventh Framework Programme of the European Community: “Research infrastructure” (RI) means facilities, resources and related services that are used by the scientific com-munity to conduct top-level research in their respective fields and covers major scientific equipment or sets of instruments and knowledge-based resources such as collections, archives or structures for scien-tific information. Such infrastructures may be “single-sited” or “distributed” like an organised network of resources.

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Typical physical assets of a research infrastructure are:

• uniqueness,noteasilyreplaceablebyanothertechnologyplatform

• investmentcostsareabove2M€(2–110M€)andoperationalcostsareabove1.4M€(1.5–120M€/a).

• alongtermperspectiveofatleastdecadestobeeffective.

The profile of “Research Infrastructure” in Food Processing Industry should enable unique research, development and innovation both in the food processing and in the manufacturing sector. This should include various focuses of the sector such as sustainability, economics, waste handling or information and communication technologies (ICT).

2.4. the necessity of CDR The Conceptual Design Report (CDR) is an important tool to highlight the significant role of R&D in the food and manufacturing sector and to provide solutions and innovative ideas for obviating the cur-rent and future challenges in the food processing sector. This CDR expands upon the past efforts by combining resources, capabilities and competences of the ETP’s Food for Life and ETP MANUFUTURE at research, business and technology transfer level. It does involve those stakeholders in a bottom-up process in order to present a research infrastructure that builds upon analyses of cutting-edge visions and gaps regarding manufacturing technologies, food chain management, sustainability, business mod-els, technology transfer and education. In this way, the novelty, the research quality and the effect on innovation of this targeted CDR will enhance the scientific performance of the European Research Area and – through bridging and bonding existing research infrastructure of excellence in food science and manufacturing solutions science – further increase its attractiveness, visibility and impact.

The resulting infrastructure description will incorporate the construction and facilities to assist Euro-pean manufacturers in the food and manufacturing technology sectors, and SMEs in particular, to adapt more quickly to the global competitive challenges. As such, this conceptual design for a cross disciplinary research infrastructure will boost innovation, competitiveness and productivity of the European food sector and manufacturing solutions sector and address Europe’s urgent need to achieve and maintain an internationally leading position in the food industry and related research areas. As the suggested infrastructure is built on scientific cross-disciplinary excellence, it focuses on novel trans-disciplinary approaches. Together with a high degree of flexibility highlights, this CDR will foster the science based innovation capacity of the sectors.

In addition to providing criteria for the general basis of the design and concepts for a research infra-structure, this CDR includes relevant managerial as well as technical information, which will serve as committed and efficient technology transfer and cooperation with SMEs.

At policy level, the CDR and the assessment of its technical and economic feasibility in particular will provide a sound basis for agreeing on the most promising concept of a research infrastructure with Eu-ropean dimension that will speed up innovation in the food sector. In this way, it will give directions to decision makers in research policy with regard to the design and activities of Horizon 2020 and national, regional research and innovation programmes. The dissemination plan of this CDR will further create awareness and increase involvement of the stakeholders.

In summary, the design of an efficient, flexible and targeted research driven innovation structure will enable the food sector to obtain an increased benefit from the European research results and hereby ful-fill the crucial need of adding value to the food sector development in Europe through increased growth and competitiveness in both the food and manufacturing sectors.

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2.5. assessment criteria for new research infrastructureThe new research infrastructure (RI) should serve the needs of the food processing and the manufac-turing industries for establishing the food factory of the future. It will improve the development and implementation of relevant manufacturing solutions in the food processing sector. Consequently, the following questions need to be answered to achieve this goal:

How does the research infrastructure…

• increasethepotentialforinnovationbytechnologytransfer?

• generate integration effects on the research infrastructures concerned at European level, and how does it contribute to structuring the European Research Area?

• fosteradvancingscienceinEuropeandenablethedevelopment of new enabling technologies for indus-try applications?

• improveaccess to state of the art technologies for researchers from industry and science in food pro-cessing and manufacturing?

• develop appropriate skills specifically required for using or operating this new research infrastructure in Europe?

• contributetoappropriate skills for industry staff and operators of the research infrastructure to imple-ment results generated in the industry practice?

• harmoniseandorganisethefluxofcollectedorproduceddata?

The CDR should visualise that our approach has high potential for future Horizon 2020 Research Infra-structures actions for integration of and access to existing national research infrastructures. If limita-tions are recognised, we need to explain how it could be overcome.

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III. toWaRDS a neW InFRaStRuCtuRe FooD FaCoRty oF tHe FutuRe

3.1. overall conceptual approach The basic idea of the FP7-funded project FoodManufuture is that a systematic combination of resources, capabilities and competences of the European Technology Platforms for Food (ETP Food for Life) and for Manufacturing technologies (ETP MANUFUTURE) at research, at business and at technology transfer level, will create new ideas for inter-disciplinary research and innovation and new tools for chain and business management. 13 partners from both ETPs drafted an overall methodic approach to achieve a conceptual design study of a research infrastructure for the Food Factory of the Future this was imple-mented by project beneficiaries. Input and feedback was given by several further experts (Figure 1).

Figure 1: Overall conceptual approach to design the research infrastructure Food Factory of the Future

This report refers to deliverables of the project FoodManufuture which are available under http://www.foodmanufuture.eu/deliverables

The logic sequence to distill this CDR consists of 3 consecutive Work Packages (WPs):

• inWorkPackage2,andasastartingpointforthisCDR,themainvisionscenarioshavebeencom-piled describing the general conditions the food manufacturing industry is seen to operate in the future. They were discussed and formulated by a substantial group of European Food and Manufac-turing representatives during the course of a large workshop

• inWorkPackage3,thefinalaimwastheidentificationofgaps inresearchinfrastructureswhichhave been deduced by comparison of an inventory of necessary research infrastructures with an inventory of available, already existing research infrastructures, based on identified challenges in the food industry. Consultation of stakeholders was again mandatory

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• workPackage4paved theway fordefining the successmodel in termsof location,membership,management structure. Other critical dimensions were derived out of an process aided again by ex-perts out of wide fields of knowledge

This conceptual approach was completed by the work packages 1 (Project Management) and 5 (European Dimension Support and Dissemination)

3.2. vision scenariosA point of departure for this CDR are four future vision scenarios for the food-manufacturing sector, which were developed at the beginning of the project. The purpose of the vision scenarios is to create a palette of possible future scenarios illustrating the conditions under which the food manufacturing industry will have to operate in the future.

The vision scenarios depart from a list of manufacturing key research and innovation areas defined by the project beneficiaries. The eight research and innovation areas were:

1. Energy and material saving, alternative material sources

2. Cost efficiency sources

3. Flexible production and services, automation and/or monitoring systems sources

4. Food chain management, logistics and retail sources

5. New functionalities including smart packaging, hygiene control, etc. sources

6. Development of innovative and high quality food products sources

7. Business model sources

8. Technology transfer and education sources

Based on these key research and innovation areas, the vision scenarios were developed based on nu-merous inputs from a broad variety of stakeholders within the Food and Manufacturing sector. The most important has been the large workshop in Copenhagen in March 2012, where more than 60 repre-sentatives from the European Food and Manufacturing sectors joined forces in an effort to discuss and formulate the vision scenarios for the common future of the Food and Manufacturing sectors in Europe.

The full description of the vision scenarios can be found in Deliverable 2.4 ‘Final report on food manu-facturing vision scenarios’.

The vision scenarios are presented as illustrations in Figures 2 - 5 and are supported by text.

The key elements in the four vision scenarios are described below:

3.2.1. the Global micro SystemThe Global Micro System can be seen as a self-supplying partnership among a number of partners/en-tities in a defined narrow regional area. The Global Micro System is a partnership among partners all along the value chain, also embracing energy suppliers, waste handling, recycle facilities, logistics, etc.

Key characteristics:

• narrowregionalanchoring

• self-sustainingsystem

• closecollaborationandadaptionbetweenparties

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Figure 2: The global micro system vision scenario

3.2.2. Ingredients Based FoodThe basic assumption in this scenario is that raw materials for food and drinks will be produced at the most optimal location and then will be processed into a set of ingredients. Then, these ingredients will then be transported to other locations, where they will be mixed into different Ingredients Based Food products.

The optimal location for the production of raw materials would be determined by the availability of the natural resources, climate, wage level etc. This could be cattle farming in the most fertile areas or the growing of fruits at the warmest locations.

In the Ingredients Based Food scenario, the process of breaking down the raw materials will be refined significantly. It will be possible to process in a more advanced way meat, vegetables, seafood, etc., than today, and thus, to create a much larger variety of ingredients, not only in terms of flavour but also with different nutritional. In time it might be possible to produce many of the ingredients in a laboratory and thus will be independent of actual farming, etc.


• amixofingredients“extracted”fromrawmaterialscreatesendproduct

• ingredientsareatradableglobalcommodity

• endusermightdefinethemealsolution(flavour,nutrition,volume,etc.)

Figure 3: The ingredients based food vision scenario

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3.2.3. the Integrated value ChainIn the Integrated Value Chain the different partners have adapted their systems and operations to fit into the rest of the value chain. It can be optimised to an extent where the entire value chain can be perceived as one sole system.

The Integrated Value Chain can be organised on different geographical levels from large global value chains to small regional value chains. Strong multinational brand owners might own large parts of the value chain and thus dominate the Integrated Value Chain. But it can also be a close collaboration be-tween smaller and more equal partners.


• partiesinthevaluechainarelinkedtogether

• highlevelofknowledgesharing

• valuechaincanbegloballydispersed

Figure 4: The integrated value chain vision scenario

3.2.4. the Disintegrated value ChainThe basic assumptions in The Disintegrated Value Chain are that the market is based on pure and open trade. Materials and processes are perceived as pure and well-defined commodities that can be traded worldwide.

There is no static value chain based on long last-ing relations. Instead the value chain is split into a number of independent entities that trade com-modities in partnership can deliver the wanted products under the right conditions.


• products and services will be commoditisedand traded in a global market

• thereisnostaticvaluechain

• volumeflexibilityishigh

Figure 5: The disintegrated value chain vision scenario

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3.2.5. Conclusions for scenariosThe complexity of the food industry and its dynamic context make almost endless number of scenarios possible. More scenarios could be developed and could be valid. The four vision scenarios presented here are generic and were derived from the experts’ opinion; they can, however, overlap and can be combined in numerous ways. It is important to stress that these scenarios should not be seen as the final and only solutions for the future of European Food Manufacturing sector. We believe that the future vi-sions for the European Food Manufacturing sector should be developed within the boundaries of these four scenarios.

3.3. Gap analysis

3.3.1. the methodological approach In order to draft a model for a new research infrastructure, including facilities, resources and services, a stepwise methodological approach has been followed to identify current and future needs of the Euro-pean food industry and gaps in research infrastructures to fulfill these needs (Figure 6). As shown in the flow chart, this process consists of three key pillars:

• Identification of the future needs for research infrastructure (steps 1-11): The four vision scenarios, as defined in WP2, as well as available documents describing current and future needs are analysed by four working groups within WP3 made up of representatives of the two (food and manufacturing) sectors to identify the overall needs, available options, challenges and gaps. These working groups are organised to assign specific responsibilities for considering different aspects of research infrastructures to different WP beneficiaries to ensure that all these aspects are assessed systematically.

• Review of available research infrastructures and identification of the gaps (steps 12-18): Available, relevant (elements of) research infrastructures in Europe are collected and compared with the list of necessary research infrastructures produced in the previous step. By evaluating the necessity and feasibility of the missing elements of the research infrastructures, a list of gaps in research infra-structures are defined. After screening this preliminary list for its feasibility and potency to improve competitiveness, the priorities, the potential obstacles and the required resources/infrastructures are determined and listed.

• verification of the findings on gaps (steps 19-21): The list of suggested gaps are further reviewed in the national consultations held in nine countries (Denmark, France, Hungary, Sweden, Germany, Portugal, Italy, Spain, and The Netherlands) during the autumn 2012, and at the 1st Consultation European Stakeholder Event held in Brussels in October 2012. The outcomes of these consultation events, as well as further work within the WGs to characterise the gaps, were used to update the research infrastructure list, and verify and update the list of gaps and priorities.

This methodology aggregates a strong scientific and industrial involvement across Europe. By exploit-ing excellence from food and manufacturing solutions research areas, the suggested infrastructure will build on scientific excellence which will be fostered by novel trans-disciplinary approaches. Through a bottom up, user-driven european stakeholder approach, the commitment and feedback from the industries, the infrastructure will meet the needs of the food sector and the manufacturing solutions sector and thus increase their science based innovation capacity.

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Figure 6: Flowchart of identification of gaps in research infrastructures

3.3.2. long and short term needs The future needs cannot be established without identifying of the current needs of the food sector which are yet to be served with appropriate manufacturing solutions. This provides an opportunity for identification of the currently available manufacturing solutions which have already been developed for other sectors but not adapted to the food manufacturing industry.

To follow the methodological approach elaborated in the project, long and short-term future needs were collected and analysed. The results were discussed with stakeholders on national workshops in 9 coun-tries and at an international stakeholder event.

Inventories describing the needs and the solutions were prepared; almost 80 needs and challenges of the food sector and 70 potential manufacturing solutions were identified. The priorities, the potential obstacles, required resources and the infrastructure for the Food Factory of the Future to transfer the available manufacturing solutions were also collected and published in Deliverable D3.6 ‘Integrated

1. Identification of problems of the food sector to be solved by manufacturing solutions

3. Exchange of views in several rounds

4. Needs of the food sector

6. Compare, match problems and solutions

5. Inventory of long and short term future needs of the food sector

15. Gap in the infrastructure

16. Preliminary inventory of necessary infrastructures

17. Screening of feasibility, potential to improve competiveness

18. Priorities, potential obstacles and required resources/infrastructures

19. Verification of all documents through national discussions (9 countries)

20. Verification of all documents at European Level

21. Finalisation of all documents

8. Inventory of available solutions

10. Inventory of solutions available in short term

13. Current research infrastructure

Stop

11. Identification of necessary research infrastructure

2. Identification of manufacturing solutions which can be used to solve problems for the food sector

7. Are solutionsavailable/can be

adapted soon?

9. Is research necessary to

develop a solution?

12. Is this infrastructure

available?

14. Is this infrastructure

necessary/ feasible?

Yes

Yes

Yes

Yes

No

No

No

No

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summary of long and ort term future needs for research infrastructures’.

The research needs of the food processing industry, for manufacturing solutions, and the manufacturing sector, for developing and adapting these solutions, were grouped around the following main subjects:

Sustainable food manufacturing

• Newmachineryandplantwithefficientuseofresourcesandreductionofenvironmentalimpact:energy, water, material (increasing yield) and time

• Adaptationofleantechnology

• Wasteutilisationandvalorisationcoupledwithpurchaseofrawmaterial – heat recovery – cleaning of waste water – recovery of valuable materials – manufacturing solutions for improved waste management · reducing the environmental impact of cleaning, alternatives for using chemicals · solutions to support sustainability of business activities of SMEs, local food manufacturers

• Businessmodelsforreductionofinvestmentandmaintenancecostsofmachinery – payment proportional to their use – shared use of machinery – cost effective machinery provision for seasonal production – modular design – cost information considering investment and maintenance costs

• ICT,logisticsolutionstosupportthemarketaccess(local,nationalandinternational)ofSMEs – design and measure sustainability performance

• Simple,practicaltoolsforcalculationandevaluationofenvironmentalimpact,alternativesinpro-duction methods, systems, technologies, changes, along the whole chain

• Databasesforcalculationofenvironmentalimpact

• Applicationofsustainabilityapproachforthewholeproductdevelopmentcycle – optimising food package volume and light weight packages

Smart process design, process control, ICt enabled manufacturing – improving productivity

• ICTenabled intelligentmanufacturingand tools for transparency,process control systems, sensors,data transfer systems and computing facilities for large amount of data, activators and expert systems

• Self-learning,self-adaptivetechniques(machinelearning,artificialintelligence) - automatisation - robotisation of activities along the food chain

• Adaptedtodifferentsizesofoperation - Intelligent network of equipment within a processing line and along the food supply chain - Virtual design for simulation and modelling of processes, whole systems and chains - ICT solutions to optimise food supply and purchasing in the food chain - ICT solutions for improving logistic systems – flexibility and control of time, temperature and humidity

advanced food processing equipment and technologies

• Technologiesfordevelopmentofnewsensoryproperties,foodmicrostructureandedible-films

• Flexible,easilyreconfigurable,upgradableequipmentandmanufacturingsystems – fast adaptation to new variations – agile manufacturing

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– better exploitation of operation time – effective maintenance systems – novel packaging materials and forms, edible packaging, biodegradable packaging, intelligent and

active packaging, optimisation of package volume and, light weight packaging – model foods for testing equipment

Food hygiene, food safety and quality

• Moresensitiveforeignbodydetectionandremovingsystemsapplicableforawiderscope,multifunc-tional solutions

• Rapiddetectionandtestingmethods

• Newsurfaces:fromnanotechnologyandothertechniques – dry and water free, water saving cleaning, self-cleaning surface of equipment, walls, ceilings,

floors, drains, surfaces appropriate for ultrasonic cleaning – heavy duty and organic acid resistant floors – reduction of friction, elimination of lubricants and leakage of food – antimicrobial protective clothing

• Easilydismountableequipment,improvedsurfaces,forbettercleaninganddisinfection – smart use of cleaning chemicals – flexible and mobile high risk area units – serving consumer’s needs, transparency systems and solutions based on ICT, RFID for informed

decisions – web applications and services for the end consumers

• Intelligenthypermarket–consumerinformationsystems

• Purchasing,virtualhypermarket – ICT solutions for improved traceability for complex traceability problems (cut meat, grain, bulk

food) – application of the new functions of the Future Internet for focused, screened information – con-

tent based browsing

• Effectiveremovalofcontaminationfromfoodpowdersandfreshproduce

ensuring freshness, increasing shelf-life with hygiene and packaging solutions

• Solutionsforextendingshelf-life

• Rapiddetectionoffreshness

Innovation methods, knowledge and technology transfer

• Practicaltrainingfacilities,thelearningfactory

• ICTbasedandvirtualtrainingsystems

• Knowledgemanagement – portals for collection, structuring and sharing of reproducible knowledge – new, focused information collection, structuring systems based on the functions of the FI and

content based browsing

For the Research Infrastructure for the Food Factory of the Future was found important to ensure that they:

• areattractiveforboththeresearchersandtheindustryandenabletestingofthepracticalapplicabil-ity of new results and solutions. Thus they stimulate closer collaboration between the research and development providers and the industry

• enablecollaborationoftransdisciplinaryresearchanddevelopmentteams

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• madeofsharedphysicalandvirtualpilotproductionandtestingfacilitieswithhighflexibility,whereseveral variations of new ideas can be explored at reasonable effort and cost.

3.3.3. Current research infrastructure An inventory of the available RIs in Europe was done during the initial phase of Work Package 2. Soon, it was realised that a review of all existing RIs is a tremendous challenge that could not be finalised within the project lifetime. Nevertheless, the consortium was ambitious to continuously update the list of existing RIs during the subsequent consultation processes in Europe. The still incomplete review of the existing RIs is summarised in the Deliverable 3.11 “Draft summary inventory of gaps in research infrastructure”. However, to overcome the current non-transparent overview, from a global perspective of existing RIs in food manufacturing in Europe, beneficiaries of the consortium as well as other stake-holders were asked to continue with this exercise.

The online knowledge database Food Tech Innovation Portal (Food TIP), set up by the FP7 funded NoE HighTech Europe (www.foodtech-portal.eu), serves as an excellent basis to get an overview of existing research institutions, companies and facilities in the food processing and food manufacturing sector in the long-term. To achieve this comprehensive overview it is highly recommended to describe missing RIs in the free accessible Food TIP.

Exemplary collected RIs were divided into four categories in the Deliverable 3.11:

• RIswithcoreactivitiesinthefoodsector

• RIswithcoreactivitiesinthemanufacturingsector

• RIsinICTsolutions

• RIsinEnvironmentalissuesandSustainablemanufacturing

A future RI will probably consist of various elements. An analysis of missing RIs respectively RI ele-ments in the current research landscape is part of the gap analysis. As a preparatory work for this gap analysis elements of existing RIs were categorised into the following:

• pilotplants:foodprocessingequipmentandtheirassemblies

• advanced(analytical)equipment,sensorsforfoodprocessingresearchandprocesscontrol

• databasesandcollections

• e-infrastructure,networks,computingfacilities,screeningandstructuringofinformation

• knowledgeandtechnologytransfer

• newbusinessmodelsformanufacturingsolutions,machineryandoperation

• competencecentre/trainedandskilledstaff

An exemplary extract from the inventory of Deliverable 3.11 is given in the following chapters.

RIs with core activities in the food sector

A range of technical labs, sensory labs and industrial pilot plants are available throughout Europe. They are distributed around Europe and cover a wide range of topics, such as process efficiency, product and process innovation, product safety and quality. Few have full pilot plants able to produce food products in sufficient amounts for sensorial evaluation or shelf-life studies.

Much of the research infrastructure become specialised in few core technologies. For example the Neth-erlands (Netherlands Organisation for Applied Scientific Research, TNO) has unique sets of equipment such as super-heated steam and monodisperse powder 3D printing. Campden BRI constitutes a research infrastructure in microbiology, hygiene, and chemistry, and has a leading-edge sensory analysis suite. Thermal and non-thermal conservation and modification processes are in focus at the Berlin Institute

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of Technology (TU Berlin) where a wide range of equipment can be found. SIK – The Swedish Institute for Food and Biotechnology has a unique set of microwave and infrared equipment for food applications at different size scale, in addition to capacities to evaluate food properties and environmental impact of food processing. It has also a fully equipped meat processing plant. A “from farm to fork” perspec-tive is implemented at the Institute for Food and Agriculture Research and Technology (IRTA) in the areas of food technology, product quality and food safety, as well as functionality and nutrition. In the agro-industrial production sector ENEA - the Italian National Agency for New Technologies, Energy and Sustainable Economic Development is an important research infrastructure to help the food in-dustry to innovate and evolve. Leatherhead Food Research offers expertise in food legislation, market intelligence, business and technical information, and training. It has a pilot plant with a wide range of general processing equipment. DIL-German Institute of Food Technologies has facilities for ultrasound, ohmic heating, pulsed electric fields, high pressure technology, runs a platform to adapt robotics to food processing and provides full-scale industrial test beds or emulated industrial laboratory facilities.

An e-Infrastructure (HighTech Europe) aims at establishing a first European Institute for Food Process-ing. The research infrastructure facilitates implementation of high-tech processing solutions in the food sector by building a portal where food companies can search for and learn about innovations and tech-nical solutions (www.foodtech-portal.eu).

RIs with core activities in the manufacturing sector

As for the research infrastructures in the food sector, the available research infrastructures in the man-ufacturing sector cover a wide range of topics and activities, such as innovations in polymer and com-posite processing (PIEP) and development of technologies for biomass and waste conversion (Piattaforma Integrata per l’uso di Biomasse e rifiuti di origine vegetalE, PIBE). Also in Italy, a pilot plant is being de-veloped where innovative intelligent, flexible and automatic manufacturing solutions can be developed (ITIA-CNR). This will operate as a learning factory to train students and skilled operators in areas such as robotics and human-robot cooperation, virtual/augmented reality and system engineering and inte-gration. Also, related business models in the field of de-manufacturing of mechatronics processes will be designed and tested.

Technology transfer in some form is included in most research infrastructures. However, some organ-isations have been created to act as interfaces between academia and industry and are dedicated to tech-nology transfer, training and innovations (INESC Porto and INEGI). A production technologies cluster (PRODUTECH) promotes cooperation, innovation and internationalisation in the manufacturing sector.

To utilise innovations in manufacturing, a consortium of research institutes and universities across Europe manages the European Manufacturing Survey (EMS). EMS covers a core of indicators on the innovation fields: i) technical modernisation of value-adding processes, ii) introduction of innovative or-ganisational concepts and processes, and iii) new business models for complementing the product port-folio with innovative services.

RIs in ICt solutions

Improved and reliable ICT solutions are key issues for the Food Factory of the Future. There is an avail-able research infrastructure for improved performance, reliability and production costs regarding in-ternet applications, which will build a core platform for the Future Internet (FI-WARE). The platform will increase the global competiveness of the European ICT economy. An Open Innovation Lab will be created to nurture future innovations. Also, a pan-European data network (GÉANT) seeks to shape the Internet of the future by developing an advanced portfolio of technologies. Services, tools and network capabilities will be created for the researchers of tomorrow.

Computer power is essential for many research sectors, and a research infrastructure on High Perfor-mance Computing (HPC-Europa2) is giving the research community in Europe access to first-class super-computers and advanced computational services. In this way, transnational access to HPC systems avail-

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able in Europe is realised. Also, the European Grid Infrastructure (EGI) is an e-Infrastructure providing access to computing and storage resources across Europe, linked by high-performance networks.

In order to centralise the available information resources in Europe, a platform (Collaborative European Digital/Archival Infrastructure, CENDARI) is created with the aim to provide access to previously frag-mented European information resources, such as archives and libraries. New e-science tools and ser-vices will be developed to support transnational and comparative cross-disciplinary work.

RIs in environmental issues and Sustainable manufacturing

In the area of sustainability there is a growing requirement of data and information. Currently, most of the data is stored locally, and cannot be shared with the rest of the scientific community. Therefore, there is a large demand for research infrastructures to connect the already existing infrastructures. Some of the available research infrastructures in this field aim for a joint approach. Distributed Infra-structure for EXPErimentation in Ecosystem Research (EXPEER) is a project which federates already existing infrastructures in the field of Ecosystem Research, in order to improve the research capacity. Also, EXPEER facilitates access to experimental and observational platforms as well as analytical and modelling facilities. A similar approach is used for several research infrastructures, such as:

• theInGOSproject(Integratednon-CO2GreenhousegasObservationSystem),whichintegratesexist-ing infrastructures in the area of non-CO2 substances

• IS-ENES (Infrastructure for theEuropeanNetwork forEarthSystemModelling) thataims forde-velopment of a common distributed modelling research infrastructure for Earth System Modelling. This project will create a virtual modelling resource centre using state-of-the-art technologies, to al-low research groups across Europe to have access to their data and resources

• theInfrastructurefortheMeasurementoftheEuropeanCarbonCycle(IMECC)providesdatabasesand tools on the relationship between atmospheric carbon and the terrestrial biosphere. It strives towards an integrated and accessible European carbon data assimilation system

In sustainability assessment methodology data on attitudinal and behavioral changes in social, political and moral climate data are important. This can be used to analyse the impact of food manufacturing. The European Social Survey Infrastructure (ESSi) improves social measurement in Europe and provides an infrastructure for such data and methodologies. The impacts on human health and the environment of nanotechnologies are handled at the European Centre of the Sustainable Impact of Nanotechnology (ECSIN).

A European Platform on LCA improves the credibility, acceptance and practice of LCA in business and public authorities. This research infrastructure will ensure better coherence across LCA instruments and robust decision support.

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3.3.4. Gaps in food manufacturing research infrastructure

One of the main aims of the project was to identify the gaps in the necessary Research Infrastructure for the current and future food manufacturing industry in Europe. The research infrastructure should include facilities, resources and services necessary to serve the needs of the food industry in order to fulfil the future vision scenarios of the Food Factory of the Future.

The identified needs and solutions have been intended to be paired in order to find the gaps and identify the necessary enabling elements of research infrastructure. Several available research infrastructure elements have been collected across Europe both in the food and the manufacturing sectors having suit-able competences. Based on the available research infrastructure compared with the necessary ones, missing elements of research infrastructure were identified. They were then discussed in terms of im-portance, necessity and feasibility and a priority list was proposed based on their ratings.

The inventory of needs and solutions and the list of gaps were discussed and verified during the national consultations held in nine countries and at the 1st stakeholder event. The following elements of research infrastructure were considered to be necessary to eliminate the major gaps in the available research infrastructure for food manufacturing in Europe:

Gaps in existing RI that have been identified with priority I

1.1 Pilot size factories to develop manufacturing solutions for the food processing industry

1.2 Pilot plants for implementation of robotics and automation in food production

1.3 Collection of business models on innovation practices in the food production sector

1.4 Virtual/augmented reality for simulation and training

1.5 Research facilities for radical innovations in food technology

1.6 Nanotechnology to produce tailor made surfaces

1.7 Improved packaging solutions for food applications

1.8 Assessment of environmental impact of food processing

Heyko Stöber

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Gaps in existing RI that have been identified with priority II

2.1 Material sensing and characterisation

2.2 Databases on shelf-life extension

2.3 Novel piping materials

2.4 Database for informing the public about the effects of over-processing of food products

2.5 Value added products from waste

2.6 Time series data on macroeconomic performance and forecast tables

2.7 Reliable and disaggregated economic data coupled with environmental data

2.8 Robust economic models for running simulations

Characterisation of gaps in Research Infrastructure based on the typical parts and constituents of the “Food Factory of the Future” were preformed (D3.14 Verified inventory of gaps in research infrastruc-ture and its Annex: Characterisation of gaps in research infrastructure). General infrastructure ele-ments which are necessary for all Research Infrastructures and distributed sites:

• Allpilotplants shouldhaveadequate supportinge-infrastructure, suchasauthenticationandau-thorisation technologies and policies, middleware, data infrastructures and persistent data storage grid, cloud and virtualisation services which enable advanced computation, data handling and net-working capacities, 3D visualisation techniques, access to services supporting the use of Internet of Things, appropriate virtual remote access to the pilot scale equipment and remote instruments to enable cost effective sharing of them, which can substantially reduce the human and financial costs of research.

• Thereisaneedforapackagewithstandardisedsoftwareandaudio-visualtechnicalfacilitiesforwe-binars and videoconferences at each site for broadcasting good, reliable quality pictures and sound. Compatible, simpler packages should be made available at the receiver side, e.g. at the users of knowl-edge transfer and training services. These enable reduction of the cost of practical demonstrations, training and professional consultations by reducing the need for personal attendance and improve access to training.

• Foodhandlingand foodhygiene facilitiesateachnon-foodprocessingbaseelementsofResearchInfrastructures.

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Table 1: Gaps identified in the existing Research Infrastructure in Europe of highest priority

RI gaps of high priority

technical specification of the gap identified

1.1 Pilot size factories for developing, testing and training of new manufacturing solutions for the food processing industry

•Laboratorypilotandindustry-scalefoodprocessingequipment–fornewtechnologies/state of the art – ultrasonic cutting – high pressure homogenisation – advanced membrane technologies – pulsed electric field – infrared surface pasteurization – hyper/multispectral imaging system – microwave– powerultrasound– extraction– further non-thermal processing methods like HPP, cold plasma, etc.

•Adaptivecontrolsystemsatmachineandsystemlevel- Intelligent wireless sensor network along the production lines including extended sensor infrastructure in the manufacturing equipment of pilot size factories

•Pilotplantswithadequatesupportinge-infrastructure,supportforrunning cloud services

•Managementsystemsuchaslean manufacturing and Six Sigma

•EnhancedtraceabilityRadio-frequencyidentification(RFID)

1.2. Pilot plants for implementation of robotics and automation in food production

•AutomationandRobotics– manufacturing assistants, Mobile Manipulators– robotic co-workers– easy-to-use man-machine interface– vision systems, advanced cameras

1.3. Business models and food manufacturing strategies

•Laboratory-scalefoodprocessingequipmentembeddinginnovativemanufacturing solutions in order to provide reliable performance parameters to estimate impacts at business model level

•Stockofmachines,replacementpart,components,modulesandreplacement machines for at least 3 pilot testing cases for each business model and for collecting data for calculation of fees and costs

• Facilities (test-beds) for collecting reliable data on machinery operation in pilot-plant and real factory environment

• Intelligent equipment and control systems able to collect data and statistics on process performances in order to make them available for business model performance simulations and for evaluation of impact of equipment maintenance alternatives or productivity

• Databases on machinery operation and performance, databases of customers on the operation of their machinery

1.4. Virtual/augmented reality for simulation and training

•Virtualreality•Advancedvisualisationtechniques,3Dcomputergraphics•Computer aided manufacturing interactive analysis, simulation,

optimisation and decision making, modelling tools•Component-andModel-DrivenDevelopmentCMDD

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3.4. model building

A set of strategic options for a research infrastructure addressing the needs and gaps identified in previ-ous work were developed and a set of models were sketched based on this work. In this context ‘model’ is a description of different elements which reflects managerial, financial, scientific and technical assess-ments.

In the following chapter the methodologies of the Success Model Building and the Roadmapping for the European Research Infrastructure Food Factory of the Future is described in detail. The following Figure 7 gives an overview of the whole process, starting with the model drafting, followed by the pro-cess of the model building in the Success Model Building workshop. It shows the refinement of the model with the results from in-depth interviews with European industry experts. Based on the organisational model of the Food Factory of the Future the road mapping process was conducted in a high level work-shop, from which the roadmap as well as further information concerning the value proposition was derived.

© Heyko Stöber

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Figure 7: Process of model building for a Food Factory of the Future

3.4.1. Infrastructure model Drafting

methodology of the Infrastructure model Drafting process

This section recounts the methodological approach that was taken toward the development of the mod-els described below. In the first step of WP4, the existing landscape of research infrastructures was reviewed. This included research infrastructures in the food and manufacturing sector as well as all other sectors, on the national, European and international level. The review drew upon the work by the

TurkeySloveniaPortugalGermanyItalyBelgiumHungaryDenmarkThe NetherlandsCzech RepublikGreeceAustriaSpain

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European Strategy Forum on Research Infrastructures (ESFRI), policy documents from the European Commission and the OECD, previous conceptual design studies developed in a broad range of research infrastructure contexts, as well as the relevant management and governance literature, which deals with such organisational choices.

Based on this review, it was possible to identify the most important organisational dimensions of re-search infrastructures. For each dimension a range of potential options were identified.

In the next step, the advantages and disadvantages of the various options were defined. This is the first set of tools for the construction of the draft research infrastructure models for the food manufacturing sector. In order to allow flexible use, the various options across different organisational models during the model building process, the ‘pros’ and ‘cons’ were initially considered at a generic level. During the model building process, however, they were reassessed and adapted to the requirements of the specific model. The purpose of this model is to foster food research.

In the following step the most critical organisational dimensions were identified. A dimension was deemed as critical, when it had far reaching implications for further organizational choices and when the dimension has a decisive impact on the overall nature of the research infrastructure model. The se-lected critical dimensions are: location, membership, management structure, access policy, type of fund-ing and source of funding. Figure 8 provides an overview of the critical and the ones deemed to be less critical which can be developed once the critical dimensions have been defined.

model dimensions

Figure 8: Dimensions and options for RI identified by literature review and gap analysis

In the next step, options were selected for each of the critical dimensions. This step was strongly influ-enced by the results of the previous work packages (WP2 and 3). Furthermore, the project beneficiaries were asked to interpret the identified priority (see chapter ‘Gap analysis’) via an online survey. In this

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survey, the requirements for each prioritised gap were assessed (e.g. possible usage of existing research infrastructures, needed stakeholder, cost information, etc.). Altogether the results of the gap analysis were used to define concrete requirements and principles which all of the organisational models should fulfil. Moreover, this step led to the exclusion of some potential options (e.g. a single sited solutions), as they seemed inappropriate in light of the results.

After this step, the critical dimensions and the critical options were brought together in the consistency matrix. This is an analytical tool that highlights the interactions between the various options and di-mensions. The choice of one funding model, for instance, may have significant implications for another option of another dimension, such as decision-making rules, management structure and even location. This approach ensures the internal consistency of the selected models.

On the basis of the consistency matrix and an intensive analysis of the research field, three draft models were selected eventually. The priorities for the development of the models were to develop models that are plausible, realistic and internally consistent and thus represent real options for a future Research Infrastructure. At the same time they were meant to be different from each other in order to offer a real choice and to promote discussions among the project beneficiaries about the most adequate organisa-tional structure of the Research Infrastructure. The main characteristics of the draft model have been verified by the core members of the work package.

Figure 9: Workflow: drafting of three ideal-typical models

Figure 9 summarizes this approach.

The Organisational Infrastructure Model Drafts

Three draft models, which are the results of the model building process, were sketched as the starting point for the further model building process. Subsequently, the models were described for different criti-cal dimensions. It is worth mentioning that these draft models represent ideal types and are meant to cover the entire spectrum of potential research infrastructure models.

model 1

Out of the three draft models, this research infrastructure model is the most industry focused. Its main driver is the objective to promote applied research and technology transfer between companies from the two sectors. Rather than the promotion of basic research, this model is expected to focus on harvest-ing the low hanging fruits by promoting the technology transfer from one sector to another and from one company to another. This objective had a decisive influence on the options chosen for most of its dimensions.

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model 2

Like model 1, this model tries to promote knowledge transfer. However, in contrast to model 1, its main driver is to place knowledge and technology transfer on a much broader platform that does not only include industry, but also a large range and number of other stakeholders from research, education and business as well as the food and manufacturing sectors. Based on the assumption that innovation occurs wherever and whenever new combinations of stakeholders and ideas are brought together, this model seeks to create a place and time for such innovations.

model 3

The main driver of this model is the promotion of basic research. This research infrastructure is meant to work on the long-term challenges of the food manufacturing sector that may already be on the indus-try’s radars but which may not be concrete enough to win industrial funding. Unlike the previous two models it mainly focuses on universities and research institutions. Industry would play a lesser role in this model.

3.4.2. methodology of the Success model Building processDuring the Success Model Building Workshop (held in Brussels, May 15th and 16th 2013) the three models were validated and one of the three models was taken as a basis on which a final model for the European Research Infrastructure Food Factory of the Future (FFoF) was developed. Based on three ideal-typical sketches for research infrastructures the most important organisational dimensions for the FFoF were discussed and the best respective options for the FFoF were chosen. The result of the Success Model Building is a feasible model for the design of the FFoF that fully reflects the most ambitious aspirations formulated in the vision scenarios and addresses the gaps in research infrastructure for the food manu-facturing sector identified in the gap analysis. This model was compiled by potential stakeholder (exter-nal experts from industry, science and society as well as the project beneficiaries) experts.

In this Success Model Building Workshop the gaps in the food manufacturing research (outcome of WP 3) were presented in detail. Afterwards the three developed ideal-typical model drafts for Research In-frastructure were presented, explained and discussed in detail in order to get a deep understanding of possible designs of Research Infrastructures. This basic information was followed by a description of the future main beneficiary of the European Research Infrastructure Food Factory of the Future, the main objective and the key requirements for the FFoF. These key requirements are an abridged list of the identified gaps in Research Infrastructure (WP3) and the result of a survey, conducted with the con-sortium members. They show the basic requirements for the FFoF which are referred to in the following Success Model Building process.

In order to facilitate the work with this complex issue, an illustrator supported the discussions by visualising the ideas and arguments and their relation.

Basis for the Success Model Building were:

• themainobjectiveandthekeyrequirementsfortheFFoFincludingtheidentifiedgapsandresultsfrom a survey with the FoodManufuture beneficiaries

• threeideal-typicalresearchinfrastructuremodeldraftsasstartingpointfortheSuccessModelBuild-ing process

The building of the Success Model for FFoF was conducted in a two-step process in order to handle the extremely high complexity and the interdependencies in the models:

• step1:threeideal-typicalmodeldraftsforresearchinfrastructurewaschosenasthebasisforfurtherrefinement. Therefore the three model drafts were discussed and an assessment of pros and cons of the respective model was conducted.

• step2:theselectedideal-typicalmodeldraftwasfurtherevaluatedandworkedoutindetail

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Step one: Choosing an ideal-typical Draft model

The aim of step one in the Success Model Building was to choose one of the three ideal-typical model drafts that have been developed in Work Package 4. These drafts were graphically worked out for the workshop to support the discussion. The workshop was conducted as ‘World-Café’ which is a method for a structured conversational process of a group (Figure 10).

Figure 10: Step one: world café and choice of one draft model (DM)

In this first step, complexity was reduced by choosing one of the three ideal-typical draft models. In or-der to enable this choice, the critical organisational dimensions of the three model drafts were discussed in a World-Café exercise and the advantages and disadvantages of the the three ideal-typical models (not of the single options) were assessed.

Figure 11: Position of the draft models in the model building process and on the whole spectrum of potential models

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Group Work in Step One: The three ideal-typical model drafts were discussed on three tables by defined expert groups which were heterogenous as regards the professional background of the members. In ad-dition to the pros and cons, general comments on the respective models were collected.

Decision Making in Step One: Following the discussion and assessment, the results of all three models were presented to the plenum. Step one was completed with the choice of one of the three ideal-typical model drafts. This was done by asking all participants to vote for one model according to the aim and main objective of the research infrastructure. Afterwards, the reasons for their choice were collected.

Step two: Working out Critical organisational Dimensions of the Success model

In this second step, the critical organisational dimensions of the chosen ideal-typical model draft were worked out and the success model for FFoF was built.

After reducing complexity in step one by choosing one model draft, now the complexity was increased again by adding all the possible options of the critical organisational dimensions of the chosen ideal-typical model draft. Due to the extreme high complexity of all dimensions, options and their interdepen-dencies, only the critical organisational dimensions have been discussed in this step.

Figure 12: Step two: Process of refining the chosen model draft

Group Work in Step Two: The single dimensions of the chosen ideal-typical draft model were discussed and assessed at four tables with rotating groups. The options from the ideal-typical draft model were used as starting point for the discussion and possible options were presented afterwards. Then the par-ticipants were asked to either name further possibilities for the options or to decide for one of the given options. The main question that had to be answered was which option had to be chosen, in order to sup-port the aim, objective and key requirements of the FFoF.

Discussion Session and Wrap up in plenum

After this discussion and collection of the arguments and possible conflicts, the results were presented to all participants during an information session. The results of all the research infrastructure dimensions were collected and further discussed during a wrap up session in plenum.

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The results of the Model Building Workshop formed the basis for the in-depth interviews and the road-mapping workshop described in the following section.

In-depth Interviews with Industry Experts

Due to the high importance of the industry in the European Research Infrastructure Food Factory of the Future, the model was further refined with the experts’ opinion from industry. These in-depth in-terviews were conducted with 38 industry experts from the food processing and manufacturing sector. The results from these interviews were included in the success model. This refined organisational model of the FFoF was used as the basis for the road mapping workshop.

Figure 13: In-depth interviews with industry experts from European countries

3.4.3. methodology of the Road mapping processThe road mapping workshop took place on 3 and 4 September in Brussels. The basis for the road map-ping process in the workshop was mainly the organisational model for the European Research Infra-structure Food Factory of the Future. This was derived from the Success Model Building workshop and refined with the results of interviews with 38 industry experts from the food processing and manufac-turing sector all over Europe. In parallel to the interviews, additional organisational aspects and tech-nical issues for the FFoF were collected and integrated into the organisational model. With additional prerequisite for the road mapping workshop was derived from the vision scenarios (WP 2) and the gap analysis (WP 3) and especially from the strategic approach for the FFoF.

In the road mapping workshop, the main steps were identified, which are required to establish the FFoF. In addition, many ideas concerning the value proposition and further details on the design of the FFoF have been worked out by the experts.

The participants in the road mapping workshop included experts from existing research infrastruc-tures, representatives of the European Commission and project members. These experts have deep ex-perience of European research infrastructures in very different sectors as well as expertise in funding, building up and running research infrastructures.

The most important prerequisite for building up the roadmap is an understanding of the planned or-ganisational design of the FFoF. Therefore, the experts in the road mapping workshop were informed in detail about the strategic approach and the organisational design of the FFoF, i.e. the description of the aim and the main beneficiaries of the FFoF, of its planned performance and how it should be designed.

TurkeySloveniaPortugalGermanyItalyBelgiumHungaryDenmarkThe NetherlandsCzech RepublikGreeceAustriaSpain

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Road Mapping Methodology

For the road mapping process two things are crucial: on the one, hand a time frame in which the single steps have to be arranged. On the other hand, different, so called layers are necessary, i.e. dimensions, fields or topics, where the identified steps refer to. For this road mapping process, the layers have been defined as different organisational and technical dimensions of the research infrastructure. These lay-ers are: governance, membership, access to research infrastructure and information, funding and fi-nancing and technical issues. As mentioned above, the organisational model for the FFoF had already been developed; therefore a so-called scenario-based road mapping process was conducted. This means that the design of the FFoF was basically given. Therefore, the discussion was mainly focused on how to reach the given design of the FFoF, not on how it should be designed.

Chronological Sequence in the Roadmap

In order to get a chronologic sequence of the required steps to build up the FFoF, the timeframe was divided into two phases: the initialisation phase and the implementation phase. The initialisation phase referred to the first steps which are necessary until the foundation of the FFoF. The steps which were found in this phase are the preparatory ones and they are necessary to prepare the foundation of the FFoF.

During the working sessions where the implementation phase was discussed, the steps that are neces-sary after the FFoF is implemented and starts to work were identified (see figure 3.1). The discussions were conducted in three working groups, each discussing some of the organisational or technical layers.

Figure 14: Frame of the roadmap for the FFoF with timescale and layers

Timeframe for the Roadmap

As mentioned above, one crucial prerequisite for the identification of steps towards the FFoF in a road mapping process is the timeframe. Therefore, the road mapping process in the workshop started with a discussion on the timing of the initialisation and the implementation phase.

FFoFfounded

FFoFimplemented

FFoF Governance

FFoF Membership

FFoF Funding

FFoF Access policy

FFoF Technical

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Dimensions of the Roadmap

Basically, a research infrastructure has many organisational and technical dimensions which could be discussed in a road mapping process. The identification of steps in all possible dimensions would be very time consuming and complex. Therefore for the workshops the most relevant dimensions have been chosen and agreed on:

• Governance

• Membership

• AccesstoRIandInformation

• FundingandFinancing

• TechnicalIssues

This choice is based on the former work in WP 4, where the most critical dimensions had been identi-fied. These dimensions have considerable influence on other dimensions, therefore they are crucial and have to be discussed.

As mentioned above and illustrated in fig. 3.1, the steps have been discussed referring to different di-mensions, so called layers of the roadmap. Due to the number of experts in the workshop, the work had been done in three working groups. These groups worked according to the world café method. The rotation conducted using this method was very important, because most experts had considerable ex-pertise in complete research infrastructures, not only in single dimensions by rotating the groups, it was ensured that each expert contributed to each layer.

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Iv. StRateGIC appRoaCH FoR tHe RI FooD FaCtoRy oF tHe FutuRe

4.1. Strategic developmentThe main objective of the new research infrastructure is to improve the competitiveness of the Europe-an food sector and manufacturing sector. Therefore, a new research infrastructure will be designed that offers appropriate capabilities, services and activities that can be utilised by researchers and industry industry (both large and small) and researchers. Such research infrastructure will finally boost innova-tion in both sectors, food processing and manufacturing.

Therefore, the main beneficiary of the new research infrastructure will be the food processing indus-try and manufacturing industries dealing with food related issues, which explicitly includes SMEs. The infrastructure will offer the conditions to progress in research and innovation by making unique re-sources available to carry out research and technology transfer, and by constituting a place where in-dustrialists can cooperate with researchers.

To achieve this aim and to meet the challenges, the new research infrastructure will:

• bean inspiring network of innovative existing pilot-size factories and pilot plants to develop and demonstrate manufacturing solutions for the food processing industry. The research infrastructure will be a distributed location

• buildonexistingresearchinfrastructureelements(researchfacilities,services,etc.)asabasistode-velop a new generation of combined facilities, resources and related services to provide new manu-facturing solutions

• facilitatetheutilisation and maximisation of capacities, knowledge and know-how by industry

• meettheexpectationsandneedsofthemainresearchinfrastructurebeneficiariesinalong term perspective

• focusonapplied research and transfer cutting edge technologies and information from the manu-facturing sector to the food sector and supporting basic research to applied research by considering education and training

• be industry driven and reflect this in the overall management structure, in the decision making rules and the operational structure

• haveplatforms dealing with key topics identified as gaps of the existing research infrastructure in Europe such as:

– pilot size factories for developing, testing and training of new manufacturing solutions for the food processing industry:

– pilot plants for implementation of robotics and automation in food production – collection of business models on innovation practices in the food production sector – virtual/augmented reality for simulation and training – research facilities for radical innovations in food technology – nanotechnology to produce tailor made surfaces – improved packaging solutions for food applications – assessment of environmental impact of food processing

• haveaninclusive membership which is open to a broad range of members

• giveopen access to industry especially Smes to utilise the research infrastructure

• haveaflexible structure to adapt its focus on future demands and challenges

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• create trust among involved stakeholders in the food manufacturing sector

• provideabalance between confidentiality and exploitation

• befunded by a mixed funding scheme from public and private sector

4.2. value proposition and expected impact

4.2.1. value propositionThe Food Factory of the Future will present itself as a privileged field for testing, piloting, and demon-strating new and emerging innovations in food production. In order to meet the need to extract more value from R&D and innovation investments, a critical aspect in the current economic crisis, Horizon 2020 will offer stronger support to the market ‘take-up’ of innovation. This will therefore imply a higher focus on the realisation of proof-of-concepts, pilot lines and demonstration plants. It will also imply a better use of the potential of research infrastructures, as well as setting technical standards and pre-commercial procurement.

Building and maintaining research facilities and generating new knowledge is extremely expensive. The pilots and demonstrators network provided by the Food Factory of the Future aims at fostering synergies between food researchers and production technology developers, where technologies are in-tegrated and demonstrated in real or virtual settings. It is expected that complementarity and blending of resources along with know how in such demonstrators and pilot installations within this research infrastructure will dramatically improve the effectiveness of the exploitation of research results and will consequently boost innovation and improve competitiveness.

The new Food Factory of the Future will give added value namely for:

• thefoodmanufacturingindustry

• thesolutionandserviceprovidingindustry–suchasequipmentsuppliersandconsultingcompanies

• providersofappliedresearchinfrastructure–suchasresearchinstitutes

• fundamentalresearchproviders–suchasuniversities

• allstakeholders–synergiessuchasininnovation,transfertomarket,educationandtraining

Finally, the new Food Factory of the Future will finally and consequently give added value to the European consumer who will have access to better food and the European food industry.

An overview of general value propositions for specific stakeholders of this RI is shown in Table 2 below.

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Table 2: General value propositions of the RI Food Factory of the Future

General value propositions of RI

Food

m

anu

fact

uri

ng

indu

stry

Solu

tion

an

d

serv

ice

pro

vid

ing

indu

stry

pro

vid

er o

f ap

pli

ed r

esea

rch

pro

vid

er o

f fu

nd

amen

tal

rese

arch

Easy and affordable access to complementary, joint cutting edge technologies and facilities

x x

Provision of existing equipment/solutions developed for other sectors to the RI and create a bigger field of potential new users and therefore customers

x

Fast product development by food industry (possibility to test new processes and produce products for sensorial evaluation)

x

Fast development of equipment, solutions and business mod-els and their applications suitable for food industry

x x x

One-stop access to networked experimental facilities and related competence

x x x

Unique combination of capacities, capabilities and competen-cies integrated into a wider network to:• Complementary and optimized use of currently existing

dispersed RIs • Exploiting existing capacities, capabilities and competencies• Strengthening the research capacities, capabilities and com-

petencies in food processing and developing manufacturing solutions for the food sector in Europe

x x x x

Provision a standard operational model (standard provision-al service, all processors are certified, frame contracts)

x x x

Easy access to EU-wide training and education facilities with cutting edge technologies

x x x

Creating an arena for food industry and equipment produc-ers to meet and generate new ideas and to foster dialogue about faster adaption of existing technologies

x x x

Better access to contacts, mutual information, stronger personal connections compared to solely single project based activities

x x x x

Facilitating the dialogue between the industry and research x x x x

Create an atmosphere of transparency, trust and reliable IP policy

x x x x

RI can use, test, adapt existing non-food technologies and of-fer new markets to food and manufacturing industries

x

Reduced running costs due to integrated approach and cost sharing of existing RIs

x

Boost interdisciplinary applied research activities x x

Systematic advanced solution development, problem solving using complementary knowledge

x x x

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Demonstration of advanced technologies for a broad audience

x x x

Collection, structuring, codification of new knowledge, best practice guides

x x x

Faster proof of plausibility and practicability of research concepts

x x

Faster adaption of research concepts from fundamental re-search

x

Support to innovation policies x for policy makers

Boost innovation and improving competitiveness x for policy makers

4.2.2. the expected impactThe new Food Factory of the Future will give added value to the food processing and manufacturing industry by:

• providingameetingplaceforthefoodproducingsectorandthemachineriesandequipmentmanu-facturing sector to foster dialogue between both sectors in order to identify new ways to interact and to boost interdisciplinary research activities

• givingaccesstotechnologiesanddemonstrationactivitiestomaximisetheutilisationofknowledgegenerated in academia

• offeringtheindustrycustomisedstafftrainingactivities

• havingatransparentandfairintellectualpropertyrights(IPR)regime

The new research infrastructure will meet and take into account the social, economic and ecologi-cal challenges the food and manufacturing sectors are facing today (e.g. sustainable food production, healthy and safe food). According to its policy and activities the impact of the new research infrastruc-ture is expected to foster the following areas:

• employment:sustainandcreatejobs

• consumerexpectations:healthyandsafefoods

• economy:Createnewbusinessthroughproducts,processesandserviceswithhigheraddedvalueconsidering the pricing pressure in the sector

• environment: Promoting sustainability in production and respecting environment and ensure amore effective use of resources

• business:Buildingtrustamongstakeholdersinthefoodprocessingchain

• Fosterentrepreneurship

• knowledgeandeducation:createandmaintainnewadvancedknowledgeandskills inEuropeonhigh added-value processes and technologies, as well as entrepreneurial skills.

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4.3. Synergistic effects in the european environment The FoodManufuture project sets up a conceptual design study for a new European research infrastruc-ture which will have a clear added value on European level. This exercise is a preparatory work for the next steps: initialisation, foundation, implementation and operation of the RI and thus requires a European effort. The European Commission and the EU Member States are in general willing to in-crease efforts to invest in higher-level initiatives to inspire excellence and to serve as high-performance platforms for innovation. At the same time, both parties have started or are planning initiatives which are already dealing with competitiveness of the food processing industry in Europe. Therefore, it is nec-essary to consider current and planned initiatives in the food sector to create synergies to avoid doubling and, if possible to consider follow-up activities for the FFoF.

The following chapter describes these initiatives in more detail and examines benefits and/or disad-vantages regarding the FFoF. Furthermore, Table 3 gives a brief overview of the strategic use of these initiatives for the FFoF.

In FP7, different research infrastructure calls have been announced in the CAPACITY programme fos-tering the design, support and development of new or existing infrastructures. In Horizon 2020 similar activities are planned dealing with:

• supporttoexistingresearchinfrastructures(IntegratingActivities,ICT-basede-Infrastructures).In-tegrated Activities (IA) have been the core activity of FP7 and will be continued in Horizon 2020

• supporttonewresearchinfrastructures:Constructionofnewinfrastructures(ormajorupgrades)–Step 1 topic identification (e.g. by design study), step 2 funding of the preparatory phase to apply for ESFRI roadmap, step 3 topic identified for ESFRI roadmap, step 4 funding of implementation phase

• supporttopolicydevelopmentandprogrammeimplementation

Further information: http://ec.europa.eu/research/infrastructure

• ESFRI,theeuropean Strategy Forum on Research Infrastructures, which is a strategic instrument of the EU to develop the scientific integration of Europe and to strengthen its international outreach. ESFRI identifies topics for Research Infrastructures (RI) that can tremendously increase the quality of activities of European scientists by the implementation of competitive and open, accessible and high quality RI. Only large undertakings are supported by ESFRI and the process is lengthy. ESFRI helps the projects on the roadmap to move towards implementation, but main financing of this new or advanced RI is expected by the EU member states and associated countries

Further information: http://ec.europa.eu/research/infrastructure

• etp Food for life is an industry-led, public-private partnership platform encouraged by the Europe-an Commission to drive innovation and unite stakeholder communities in determining the strategic research objectives of key European industry sectors. The main goals of the ETP are to strengthen the European innovation process, improve knowledge transfer and stimulate European competitive-ness across the food chain.

To date the platform has established 36 National Food Platforms to draft national research agendas, ex-change experience and best practice, link individual stakeholder groups across national boundaries and provide support and assistance to new and emerging platforms.

The ETP Food for Life is the mouthpiece of the european food and drink sector regarding R&D needs. It’s a highly strategic group, with stakeholders from industry and science which are in close contact with the European Commission and other decision makers in Europe. It sets up research agendas and imple-mentation plans.

Further information: http://etp.fooddrinkeurope.eu

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• etp manuFutuRe is, like the ETP Food for Life, an industry-led, public-private partnership platform encouraged by the European Commission to drive innovation. It proposes, develops and implements a strategy based on research and innovation, capable of speeding up the rate of industrial transformation to high-added-value products, processes and services, securing high-skills employment and winning a major share of world Manufacturing output in the future knowledge-driven economy.

For the same reason as the ETP Food for Life the ETP MANUFUTURE established a network of national platforms, currently existing of 29 ManuFuture National/Regional Technological Platforms. The plat-form established EFFRA (European Factories of the Future Research Association) which is a non-profit, industry-driven association promoting the development of new and innovative production technolo-gies. Interests of EFFRA are considered in the FoF.

Further information: http://www.manufuture.org/manufacturing and http://www.effra.eu/

• Factories of the Future (FoF) is one of three public-private partnerships (ppp) included in the Com-mission’s recovery package. It consists of a research programme of 1.2 billion Euros under FP7 to support the manufacturing industry in the development of new and sustainable technologies. The programme was financed jointly by industry and the European Commission under FP7. The initia-tive will be continued in Horizon 2020 with FoF calls in the Programme ‘Industrial Leadership’. The FoF gives opportunities to FFoF to connecting state-of-the-art in R&D of both sectors in joint projects.

Further information: http://ec.europa.eu/programmes/horizon2020/en/h2020-section/industrial-leadership

• Theeuropean Institute for Innovation and technology (eIt) is currently preparing together with the European Commission a call for Knowledge and Innovation Communities (KIC). One of five calls under Horizon 2020 that will address a Food4Future KIC. The call is planned to be announced in 2016. KICs are independent but operational parts of the EIT. They are built up by integrated, creative and excel-lence-driven partnerships, which bring together the fields of education, technology, research, business and entrepreneurship in a sector. KICs need an excellent and promising business model to drive ef-fective “translation” between partners in ideas, technology, culture, and new business models for the sector. They will create new business for existing industry and for new entrepreneurs. The European Commission supports the establishment of new KICs through 25% co-funding. KICs themselves need to guarantee a strong level of support by industry and other main stakeholders.

Further information: http://eit.europa.eu/

• TheJoint programming Initiative on agriculture, Food Security and Climate Change (FACCE-JPI) brings together 21 countries which are committed to building an integrated European Research Area addressing the interconnected challenges of sustainable agriculture, food security and impacts of climate change. JPI members are funding joint calls. The aim of FACCE-JPI is to support sustainable agricultural production and economic growth, to contribute to a European bio-based economy, while maintaining and restoring ecosystem services under current and future climate change. JPIs are one of the EU’s instruments aimed to realising the European Research Area.

Further information: http://www.faccejpi.com/

• the Joint programming Initiative a Healthy Diet for a Healthy life (JPI HDHL) aims to provide a holistic approach to the development and implementation of a research programme to understand the interplay of factors known to directly affect diet-related diseases. Its goal is to also discover new relevant factors, mechanisms and strategies, as well as to contribute to the development of actions, policies, innovative products and diets, with the aim of drastically reducing the burden of diet-related diseases. 22 Member States and associated countries are engaged in establishing a fully operational European Research Area on the prevention and awarenessof diet-related diseases and deliver inno-vative, novel and improved nutrition and health.

Further information: http://www.healthydietforhealthylife.eu/

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• JtI (Joint technology Initiatives) address strategic areas where research and innovation are essen-tial to European competitiveness. JTIs have been a novel element of the Seventh Framework Pro-gramme for Research, Technological Development and Demonstration Activities; JTIs support large-scale multinational research activities. Currently no JTI dealing with food exists.

Further information: http://ec.europa.eu

• TheRegional Research and Innovation Strategies for Smart Specialisation (RIS3) searches for more efficient and effective regional development policies and avoids overlap and imitation, while setting innovation as priority for all regions (Europe 2020) and improving the innovation (strategy) process. By doing so, they aim at making a better use of scarce public resources, looking for synergies be-tween EU, national/regional and private funds (PPP) and at driving economic transformation, focus-ing on regional profiles in global value.

Further information: http://s3platform.jrc.ec.europa.eu

Table 3: Overview about the strategic use of EU initiatives for the FFoF.

added value for RI Food Factory of the Future

Research Infrastructure Calls in Horizon 2020

Possible funding scheme

ESFRI Strategic approach for possible funding scheme

ETP Food for Life Strategic network

ETP MANUFUTURE Strategic network

Factories of the Future (PPP) Possible funding scheme

KIC Food4Future (EIT) Possible funding scheme

FACCE-JPI Project funding

JPI HDHL Project funding

JPI Project funding

RIS3 Possible funding of individual platforms

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v. ConCeptual DeSIGn oF tHe FooD FaCtoRy oF tHe FutuRe

5.1. IntroductionThe main beneficiaries of the research infrastructure are the food processing industry and the manufac-turing industry dealing with food related research and innovation issues, which explicitly includes SMEs.

The technical focus of the new research infrastructure will be an inspiring network of innovative pi-lot size factories and pilot plants to develop manufacturing solutions for the food processing industry. Existing relevant food pilot plants in Europe are taken into account, as well as pilot plants within the manufacturing sector which provide promising solutions not taken up by the food sector so far. applied research will be the focus of the research infrastructure but it has strong connections to excellent re-search sites to push radical innovation in the food sector.

The research infrastructure will have platforms dealing with key topics identified as gaps in current re-search infrastructures (see above). These platforms will consist of a sub-network with stakeholders from science and industry, if needed also from society and public authorities. Each platform will describe how it can give added value to the industry a main beneficiary of the research infrastructure by respective key activities and services. A main part of this exercise is the description of new research infrastructure elements which are currently missing at research institutions to complete the overall picture.

Additionally, education, training along with knowledge and technology transfer units will be set up to facilitate innovation processes. These units can be platform specific or serving all platforms.

The concept of the new research infrastructure Food Factory of the Future is illustrated in figure 15.

Figure 15: Concept of RI Food Factory of the Future

strategies

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5.2. Setting up the Food Factory of the FutureThe setting up of a new research FFoF infrastructure will be characterised by three different steps : the description of the organisational and managerial design, the initialisation and implementation and the technical design of the RI platforms ( Figure 16 ).

Figure 16 : Setting up the RI Food Factory of the Future

5.2.1. operational and managerial design

Heyko Stöber

Organizational and managerial design

Initialisation and implementation

Technical design

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Food Factory of the FutureNetwork - applied research - open access

Members

User

Novelprocessing

technologies

Robotics & automation

BusinessModels Etc.

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5.2.1.1. type of research and training

Industry is meant to be the main beneficiary of the European Research Infrastructure Food Factory of the Future. Therefore, the research focus should mainly be on applied research. However, fundamen-tal research is necessary to develop future visions and impulses for further applied research. It is also necessary to strengthen interdisciplinary research in order to bring promising results of fundamental research accomplished in other sectors into food sector. The research infrastructure will research the development of enabling technologies, i.e. key technologies developed at a conceptual level whose ap-plication will be possible in multiple sectors (such as the manufacturing and the food sector) through customisation. It will participate in public and private funded research projects and deliver different types of services and research: “one solution fits all” services (i.e. tailor-made services starting from a standardised basis), customised research and contracted research.

The advantage of offering these different types of services is that a wide range of stakeholders could benefit from the research infrastructure activities (e.g. “one solution fits all” services will be adapted to SMEs in order to provide sufficient, affordable and relevant services), while at the same time less risky undertakings such as customised research would stabilise the income flows. In addition to research ser-vices, the RI will provide information services, for example an appropriate e-infrastructure, cloud ser-vices and possibilities to build up (meta-) databases.

The FFoF is also meant to engage in training. It will promote the transfer of practical skills to raise the knowledge and level of skill of technicians and operators working within research infrastructures. There-fore, in addition to providing a place for research, the RI is meant to be a teaching factory supporting the advanced training of industrial operators and innovation stakeholders in the field of food manufacturing.

5.2.1.2.membership

To build a large platform for knowledge transfer and mutual learning, the FFoF’s membership needs to be as broad as possible and include a wide range of stakeholders from industry research institutions, education and government. In order to conduct research for the entire innovation chain, the members should come from both food and related manufacturing sectors as well as from all other relevant sectors, such as, retail. The FFoF also needs to allow for the representation of SMEs, which play a critical role in the food manufacturing industry. This was confirmed in the first workshop as well as the interviews with industry representatives.

Heyko Stöber

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The inclusion of industry is crucial. Industry is seen as the main applicant of the research results that are produced by the research infrastructure. The industry representatives that have been interviewed sug-gested that many companies are likely to be open to becoming a member of the research infrastructure as it provides them with an opportunity for sharing information across industry and for sharing the costs of running and maintaining a research infrastructure. The interviewed industry representatives, however, also suggested the inclusion of academia, whom they saw as a valuable source of know-how, skills and talents.

In order to be able to accommodate the different degrees of commitment and ability to contribute to the research infrastructure of the various members, it was agreed in the workshops that the infrastructure needs to introduce a differentiated membership system. The research infrastructure’s membership may be divided into in separate groups with varied degrees of privileges and membership fees, as discussed in the Section on funding below. The membership could, for instance, be differentiated into core, inter-mediate, associate and observing members. Ultimately, however, this needs to be determined by the members themselves.

Based on inventories for different RI Platforms (Annex 1) some institutions with high expertise in their respective areas have been identified. These institutions might have an interest in building up the FFoF and becoming future core members of the RI.

5.2.1.3. legal structure

Given the industry-focused nature of the FFoF, it cannot be set up as an international organisation like many other research infrastructures, but it shall be incorporated under national law as a private organ-isation. This will allow it to participate as a single legal entity in research projects. However, this may potentially lead to the problem that there is competition between the RI and its members that may seek to participate independently in the same research projects. This problem needs to be addressed in the statutes of the RI. In the workshop it was also agreed that it would be best if this issue never became a legal conflict because individual members have an incentive to apply to research projects through the RI rather than individually. This could be achieved, for instance, by establishing the RI as a brand that is recognised for its high level food manufacturing research.

Given its incorporation as a private company under private law in one EU Member State, there might po-tentially be a problem that the local regulations in some of the other member states in which the FFoF is co-located may differ. Within the EU, however, the legal status that is given to private companies in one member state is recognised in all other member states as well. Moreover, national regulations on food and manufacturing and research in these fields are largely harmonised across the entire EU although in some cases regulations might still differ. This is a problem, however, that all organisations in this field have to deal with, regardless of their legal form. During the initialisation phase, legal advice will be sought on this issue.

5.2.1.4. management structure

The research infrastructure’s management structure comprises of a general manager, a general assem-bly, an executive board and a technical advisory board. The general assembly includes all members and takes all the main decisions of the research infrastructure based on voting rules described below.

The general manager is in charge of the day-to-day management of the infrastructure and will repre-sent it externally. The manager will be held accountable to the executive board.

The executive board is in charge of the day-to-day decision-making and management of the research infrastructure. It will comprise a small group of representatives from the RI’s members. The general as-sembly will select the members of the executive board by qualified majority-voting. As this grants more weight in the voting process to those members that contribute more to the RI, these members are also granted a greater degree of control of the RI through the executive board. The members of the executive board will rotate periodically.

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The technical advisory board provides technical input and advice to the executive board which over-sees it. It consists of scientific experts selected by the members of the RI in the same way as the members of the executive board. It will be in charge of proposing, selecting and assessing individual projects based on scientific criteria.

In the model building workshop, as well as the interviews with industry representatives, it was empha-sised that there should be the possibility for members to form temporary sub-groups on a project basis. These sub-groups should be given with a degree of decision-making autonomy to manage their projects effectively. Members should be required to propose such sub-group projects, which should be assessed by the technical board. The RI should draw up clear rules regarding such sub-groups, which shall be adopted by the general assembly.

The research infrastructure will not include a separate government board but may include governmen-tal stakeholders as full members or observers in the general assembly. Due to the inclusiveness of the RI’s membership, there will also be no need for a separate stakeholder committee, as the RI will seek to include all relevant stakeholders as members in its general assembly.

5.2.1.5. Decision-making rules

The RI needs to adopt decision-making rules that allow it to remain manageable, flexible and responsive despite its large and heterogeneous membership. This is achieved by applying different decision-making rules to different types of decisions.

All decisions regarding the RI’s vision, mission, funding allocation and the inclusion of new members should be taken by unanimity. This may come at the cost of some flexibility and responsiveness but can serve as an important instrument to ensure continuity and investment certainty. If members are expected to make non-negligible financial contributions to the RI, they need to be given longer-term certainty and a sufficient degree of control over the main strategy of the RI.

All other decisions, however, should be made by a system of qualified majority voting, where the members’ voting rights are weighted according to different parameters that need to be agreed by all members once the RI has been set up. Qualified majority voting has the great advantage of speeding up decision-making and preventing costly stalemates and hold-outs. Potential parameters for the qualified majority voting sys-tem include: the size of the individual members’ contributions to the RI, their size, whether they come from industry, academia or government or whether they are SMEs. The voting parameters, however, need to be defined in a way so as to prevent single members from assuming a dominant position within the RI.

All decisions regarding the day-to-day management of the RI should be delegated to the executive board that is small enough to ensure speedy decision-making.

5.2.1.6. operational structure

As the FFoF will be distributed across several sites there is a need for a centralised coordination struc-ture. This should consist of a small group of permanent staff that are directly employed by the RI itself. The RI’s own staff may be supported flexibly by project-based staff located at the RI’s different sites.

5.2.1.7. location

The location of the RI shall be distributed across several sites and will be built mostly on existing RIs. Establishing a complete new RI in a single location would be prohibitively expensive. The various sites of the RI will be coordinated in an unbalanced network of research centers of varying size. This network will be supported by a virtual portal for the management of local research capabilities and the exchange of information and research results. Moreover, some RI elements which have to be build up addition-ally, like the use or an upgrade of existing RIs is not sufficient or possible. Those new elements should be ideally owned by the RI itself as this would enable the provision of additional services and access to members as well as non-members.

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5.2.1.8. Sources and distribution of funding

The funding of the RI should come from a wide range of different sources and combine different types of funding to build and operate the infrastructure. The different types of costs of the RI require a dif-ferentiated funding structure.

The initial investment costs could be carried by structural funds from EU institutions (e.g. the European Investment Bank, the European institute of Technology) or member states’ institutions. Also, private contribution of companies should be added (for example, industrial end-user companies could invest directly in the infrastructure or provide equipment they produce). Moreover, initial contributions of (potential) members are needed to cover some early costs to build up the RI organisation (e.g. for lawyers to negotiate IPR policy and legal entity).

In order to attract industry as funding source for the RI, the objective should be, from the beginning, to persuade companies to become a member of the RI. One possibility would be to start with few RI elements to show some immediate results and to expand the RI later on. This approach may lower the initial costs and speed up the implementation of the RI.

To run the research infrastructure, membership fees should cover at least part of its fixed costs (such as administration costs). Pursuant to the objective to achieve a wide membership, the membership fees should be differentiated based on various characteristics of the members:

• turnoverorsizeofemployment

• typeofstakeholder(industry,academia,governmentorelse)

• optionally,potentialuseofinfrastructure

Such a system may also attract smaller members and financially weaker members such as from aca-demia. Overall, the fee should be as low as possible to prevent the creation of participation barriers for small and medium-sized companies. Moreover, the differentiation between membership fees should be modest, as otherwise a few main payers would emerge who would probably dominate the strategy and development path of the RI.

Other operative costs should be funded from research activities and the provision of services to RI mem-bers and non-members, where members shall pay lower fees than non-members. This is meant as an additional incentive for becoming a full member of the RI. The funding of research activities could come both from industry-driven projects and from public research programmes at regional, national and Eu-ropean (mainly under the frame of Horizon 2020). The RI should act both as an organisation that applies to public funding as a single body grouping members that have common interest, and as a network that can facilitate the creation of sub-networks that apply to projects tackling issues of their interest. It will depend on the type of projects and on the available opportunities for funding. In some cases, it will be advantageous for the RI to apply for funding as a whole. In other cases it can be better if individual sub-groups write the funding applications when there are special benefits to certain kinds of members such as academics or SMEs. In addition, RI may create additional revenues from non-members who pay for access to the infrastructure or access to information or licenses (see below “access to infrastructure and information”).

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5.2.1.9. Forms of funding

The form of funding (e.g. cash, in-kind-contributions) that the RI’s members provide to the RI should be a combination of different types. Research partners could offer in-kind contributions, paying through the effort of their own personnel engaged in research or offering state-of-the art knowledge to propose and setup new research initiatives. Also, equipment suppliers could contribute in-kind, for example pro-viding equipment to setup the research infrastructure in its initial configuration phase. Other members should pay cash in order to have the necessary funds to keep the business running.

5.2.1.10. access to infrastructure and services

Given FFoF’s objective of promoting knowledge transfer across a diverse set of stakeholders, an open ac-cess policy might be an opportunity to promote mutual learning within the RI and with parties outside the RI. A complete open access policy, however, might not be accepted by all members and may prevent some from becoming members in the first place.

Heyko Stöber

Heyko Stöber

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In order to provide incentives to join the FFoF, wide access to the infrastructure and related services would only be granted to its core members. These members should have priority access to the use the fa-cilities. For those physical elements of the RI, which already exist and are owned by individual members, a direct access by other members will be difficult to realise. However, the owner may provide services to the other members for a reduced price and the response time to the members’ requests should be shorter.

Given the objective to spur collaboration among a large and heterogeneous set of stakeholders, access should be opened to non-members, however, at different terms. The RI may collaborate with non-mem-bers in a project-based model. Moreover, non-members should be allowed to use RI with a fee and get access to the services of the RI for market prices. In order to prevent free-riding and to strengthen incen-tives for membership, third-party access should have to pay market prices.

Hence, there are different type of access to infrastructure which are depending on the kind of services expected and the status of the user who wants to get access. The following levels could be distinguished:

type of access user group with access

Direct physical access to the existing elements of the RI

Owner of RI element, potentially training staff

Direct physical access to new elements of the RI RI staff, members

Access to services for reduced prices and prioritised in timing

members

Access to services for market conditions Non-members

5.2.1.11. access to information

The RI will generate valuable information for which access rights have to be defined:

• TheRIshouldprovideinformation-orientedserviceslikebulletinsornewsletterswithtechnologicaldevelopments, business intelligence contents and regulation-related issues.

• Regardingtheresultsoftheconductedprojects(iftherearenospecificreasonsforconfidentiality),the members should have privileged access and non-members should gain only restricted and/or time delayed access.

• The intellectualpropertyrights (IPR)policyshouldbekeptflexibleoverall.The IPRrights shouldbe outlined at the beginning of each project or initiative. It has to be decided case by case and the decision criteria should depend on the stakeholders that effectively generate new knowledge and re-search results. Several cases can be distinguished: First, if the RI reaches the goal using exclusively its own resources, it can retain IPR ownership. Second, the party which funds the project gains the IPR rights. Third, regarding public funded activities, project partners anyway have to follow guidelines of the respective funding scheme. Moreover, other solutions are possible, e.g. that RI customers have rights on products in field of use and the RI has rights to technology.

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5.2.2. Initialisation and Implementation In the following chapters of this document, the initialisation phase and implementation phase of the or-ganisational dimensions membership, governance, access to RI and information, funding and financing are presented one by one.

Figure 17: Visualisation of the steps required to build the RI Food Factory of the Future

5.2.2.1. membership

Initialisation phase

The very first step in the initialisation phase is to create a nucleus group of core members that are able to work well together and that can push forward the initialisation of the RI. One of the advantages of start-ing with a small group is that such a group is able to operate and take decisions more quickly than a large group. Within a small group it will also be easier to build up sufficient levels of trust that are necessary to solve the initial problems that are likely to happen in this early phase. These initial members need to be committed and driven by a strong intrinsic motivation to build up the RI. They also need to be prepared for the risks involved in such an ambitious project.

As soon as the core group is established, its members need to start discussing the vision and goals of the RI and come up with a clearly defined value proposition that can be communicated to other potential members.

In order to achieve an initial commitment from the first members, a memorandum of understanding may be drawn up.

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Implementation phase

The recruitment of as many members as possible should be the main objective of the implementation phase. Within a year, the membership should be increased to a minimum of 30 members. To achieve this objective, the first necessary step is the development of a communication and PR strategy. This strategy needs to be differentiated for the three main target groups, which include large companies, SMEs and academia. The communication strategy also needs to be differentiated across different national and lan-guage environments. As national food and manufacturing markets are very diverse, a single commu-nication strategy is unlikely to translate into every single context. To relay the communication strategy into the various countries and groups of stakeholders, a sales team needs to be recruited and trained. This sales team will need to speak the local languages and know the local business practices in order to be able to persuade the potential members to join.

For each national market the relevant contact points need to be identified to be used as a bridgehead in the local communities and a resource for contacts and reputation building. A good starting point can be the national technology platforms in food and manufacturing.

The final step during the implementation phase is to gain commitment from the newly recruited mem-bers. One way to achieve this, is to ask all members to make a first financial commitment in the form of a lump sum payment or a first membership fee payment. To reduce the entry barrier for other potential members that are still less committed, a trial membership may be offered that comes with less privileges but essentially also a lower membership fee. The users of this trial membership may eventually decide to become full members, once they have convinced themselves of the capabilities of the RI and once they have built up greater trust toward the existing members of the RI.

5.2.2.2. Governance


The core group should recruit a general manager responsible of the day-to-day management of the RI and allows the core members to focus on the main strategy and visions of the RI. This manager must be trusted by the core members. She should also bring along the experienced that is required to carry such an ambitious project. Moreover, she should already be respected or be able to win the respect of the target group of potential members. As this target group is so diverse, this may be a challenge. The academic community is likely to have higher respect for someone with scientific credentials. Such a per-son, however, may lack the management experience necessary. The business community may prefer an experienced business manager but such a profile may not appeal to the academic community. Therefore, the members need to decide and communicate very clearly whether the general manager should mainly be an administrator or whether she or he should also represent the RI within the community.

At the same time, what should happen at the very beginning of the initialisation phase is to seek legal advice on the legal structure of the RI. The establishment of the legal structure of the RI is a necessary condition for many of the following steps.

Once legal advice has been obtained, the infrastructure should be registered as a legal entity as soon as possible. This is a precondition for many of the steps that follow. Without the registration, the in-frastructure would not be able to handle financial issues, apply for public or private funding or to sign formal contracts with its members and third parties.

Throughout the initialisation phase it may also be a good idea to operate under different voting rules than during later stages. In the core group, decisions may initially be taken by consensus. This ensures that all of the initial members stay committed and support all the decisions that are made during this phase. During the later phases, majority voting rules should be introduced in order to ensure that deci-sions can be taken quickly despite a larger membership.

Already during the initialisation phase, the RI should also try to start operating research projects by using the already existing parts of the RI as soon as possible. However, it is likely that this can only be

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realised toward the end of this phase. Even if it only operates at a small scale, this has the benefit that the RI will already be able to signal its abilities and ambitions to potential members.


In the implementation phase, the RI needs to revise some of the governance decisions that have been made up to this point. Voting rules need to be adapted to the new membership structure. Majority or qualified majority voting may need to be introduced in order to keep the RI manageable. If a qualified majority voting system is chosen, the RI’s members need to decide how their votes shall be weighted. This is a necessary step to ensure the manageability of the RI as the membership grows.

The general manager needs to be confirmed or changed.

Moreover, a more selective and smaller board needs to be put in place, which does not include all mem-bers anymore but that is small enough to allow for fast and flexible decision-making.

Furthermore, an independent advisory board may be set up to consult the RI’s management and mem-bers. The advisory board should bring together experienced experts on strategy and management as well as on the technological context within which the RI is going to operate.

At the same time, the members need to decide how to introduce a differentiated membership system. As stated in the organisational description, the RI’s membership fees should be differentiated in sev-eral groups, which also differ in privileges. For instance, the membership could be differentiated into core, intermediate, associate and observing members. This may be necessary to accommodate different groups of members with different degrees of commitment and abilities to contribute to the RI.

Additional, permanent staff must be hired to support the general manager in running the RI. One cen-tral or distributed offices need to be set up and provided with the necessary resources.

At this stage, more detailed business practices and rules of procedure also need to be defined and ad-opted by the RI’s members.

Heyko Stöber

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5.2.2.3. Funding and Financing


In the beginning, some initial costs (e.g. for lawyers) will occur, which will probably have to be financed by the members. Next, funding sources for investment have to be found. Hence the RI should start actively applying for funds from different sources as soon as possible. For that purpose, first a clear value proposi-tion for the different potential funding sources has to be developed. This step will build on the value propo-sition in the CDR, but has to be to extended regarding all potential funding sources. In particular, the focus or key assets to build a quality “brand“ for research/knowledge exchange have to be pointed out.

After that, the RI should start to apply for the different potential sources for investment costs:

• OnepossibilitywouldbetoapplyforEUstructuralfunds.Asuccessfulapplicationmayalsohelptoattract private money. However, the administrative burden for structural funds is considered high and the application process may take at least a year.

• TheremayalsobeopportunitieswithinHorizon2020toco-financerunningcostsandnewinvest-ments on new pilot plants. Moreover, concerning project funding, the RI may be participating in specific calls for RIs under Horizon 2020, Those possibilities should be actively promoted by the RI core members already in the initialisation phase.

• Anotherpossibilitywouldbetosearchforacountryorafoundationthatwouldfundinitialinvest-ments for RI elements.


During the implementation phase, a strategy has to be developed and actions need to be taken to obtain continuous funding. One critical type of cost which has to be covered are overhead costs (capital costs, administration) of the RI. As far as the RI participates directly in the project, a certain overhead could be reimbursed from project funding. Apart from that, membership fees have to cover those costs. There-fore, a regular membership fee system has to be introduced, which sets the rules for the differentiation of fees and their type (cash vs. in-kind contribution).

The main operational costs have to be financed via research projects and services. Therefore the RI should start to participate in project calls and start to initiate private funded projects at the end of the implementation phase.

Access to RI and access to information


The necessary decisions on the access and IPR policy of the RI could be taken at different points in time. On the one hand, IPR should be clarified as soon as possible as it has serious implications for the incen-tives to becoming member of the RI. Most potential members will join the RI only if there is clarity about IPR issues and access to knowledge, networks and resources. Hence these policies should be set up immediately after the core members and the vision and goals of RI are defined. On the other hand, a fi-nalised and rigid IPR policy may discourage additional members to join the RI as they have no influence on these issues and they may be not aligned with their usual practices and strategy. Hence, when setting up these policies, it should be explored to which degree they may be changed later on in the initialisation and implementation phase, without losing their reliability. Moreover, as described above, the IPR policy should include in general some flexibilities and for project-by-project negotiations.


In the implementation phase, the setup of services and training activities is of crucial importance. These activities will probably begin in the end of the implementation phase. Many other organisational issues

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have to be clarified before and a clear plan of potential services and training has to exist.

Another important aspect is that the potential access to RI will be really used to identify and inform industry (new customers) on existing RI. Possible channels to promote the RI would be via Technology platforms, Industrial Association or other (professional) networks. Moreover, the RI should set up a clear calendar of use of all the pilot plants facilities to avoid “overbooking” and confusion from members.

5.2.3. technical designIn the chapter ‘Gap analysis’ gaps in food manufacturing research infrastructure were identified. These gaps present at a glance the current need of high priority to be given to new research infrastructures or elements of new research infrastructures. This list is not exhaustive and might be adapted in future to upcoming needs if necessary. However, with regard to this design study these gaps form the basis for the RI platforms. Each RI platform has specific requirements regarding the technical design.

The technical design per RI platform is structured into:

• Short description of RI platform

• activities and value proposition, including activities and services offered by the platform, value proposition for the user of the platform and justification how the platform close the gap identified

• technical requirements, including physical and technical specifications and requirements of the platform. It includes the scope of application, technical parameters, if any, further information re-garding applicable standards, difficulties and limitations, safety issues, legal issues, suggested location and estimated costs.

• Integration of existing RI, including a list of existing RIs in Europe dealing with the topic of the plat-form, their main elements as well as missing elements.

• Human resources and training facilities

including information about skills and competences of staff needed to operate the platform, specific ser-vices especially regarding technology transfer and training.

Annex 1 describes the technical design of the following RI platforms:

– advanced technologies

– multi- and hyperspectral imaging system

– intelligent wireless sensor network

– enhanced traceability including RFID

– membrane technology

– lean manufacturing and Six Sigma management systems

– cloud services

– robotics and automation

– business model

– virtual augmented reality

– sustainability assessment of food technologies

Annex 1 present the design of some platforms. Depending on the expertise in the FoodManufuture con-sortium the strategic design of some RI platforms was completed.

5.3. Financial contributionsAs described in the organisational description, concerning the financing of the investment costs, the RI should apply to many different sources, as sufficient funding will be probably a key bottleneck for the buildup and operation of the RI. It is hardly possible to define a desirable proportion of the different sources.

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Of course, a high proportion of funding from companies would be desirable, as the commitment and participation of industry is a key objective of the RI. However, public funding from the EU is needed to achieve broader goals of the RI such as the deployment of knowledge to many stakeholders from dif-ferent countries. Moreover, public funding would help avoid a situation whereby the RI is dominated by a few members, which would be the case if a few private members finance the main parts of the RI. Funding from Member States would help as well to solve these issues, but similar problems may arise if a few member states dominate the RI.

Concerning operational costs, main income should be generated via projects funding, services and usage fees. Also here, the RI should apply for a mix of funding sources (EU, member states, industry).

The figures below aim to summarise the main costs and sources for the initial funding in the first year and the funding of recurrent costs.

Figure 18: Setting up the RI Food Factory of the Future

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The following Table 4 summarises the costs estimations of the different RI platforms and the required staff (see Annex). However, not for all platforms quantitative estimations can be provided as:

• itmainlydependswhethertheplatformcanbeintegratedinthemostadequateexistingRIsormoreadditional investments are needed;

• theinitialandsomerunningcostsstronglydependontheconcretenumberandscopeofactivities.The same applies for applied techniques, which differ in their equipment costs and staff needed;

• overallveryfewinformationaboutcostsofrelatedresearchinfrastructuresexistsandthetransferof existing examples to specific requirements listed in the RI platforms is not feasible

Table 4: Estimations costs and staff needed of the different RI platforms

platform Initial cost Running cost Staff needed

Advanced Processing Technologies

30-50 Mio € n.a. •manager •expertsatPhDlevelorseniorresearchers •technicalStaff

Multi- and Hyperspectral Imaging Technologies

ca. 0,5 Mio € n.a. •imagingprofessional •spectroscopyprofessionals •techniciansandexpertsfortheequipmentmaintenance •softwaredeveloper/softwareengineer

Intelligent Wireless Sensor Network (including extended sensor infrastructure in the manufacturing equipment of pilot size factories)

2-5 Mio € can be considered as an estimated cost range.

Strongly depend on the applied techniques, area, etc.

Team composition: engineers, technicians, specialists in food inspection; Experts for •communication(protocols,routing,coding,error correction etc., electronics (energy efficiency, miniaturisation) •control(networkedcontrolsystem,theoryand applications). •sensornetworks,IT,foodtechnology,foodrheology •technical-economicalcompetencescapableto assess (in advance) the business impact of advanced sensor networks

Business Models and Food Manufacturing Strategies

1,5 Mio € 2 Mio € (mainly staff)

Industrial management engineers, machinery building/maintenance specialists, sensor and data collection specialists, food engineers, statistics engineers and industrial economists.

Virtual/augmented reality for simulation and training

•Officebuildingrelated costs •Computer/Hardware related costs •Softwarerelated cost •HRrelatedcost

n.a. •ICTbasedexpertsfordevelopingorensuring quality of the models and system approach in combination with the relevant experts on the application •stafftoprovidemaintenanceofthephysical and technical parts of the platform.

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However, some tentative overall conclusions can be drawn: The most expensive RI platform relates un-surprisingly to pilot plants for implementation of robotics and automation in food production. This plat-form is not only capital intensive, but also significant and specialised staff is needed for running the pilot plants. The other RI platforms are less capital intensive; the running costs for them are mostly unknown.

Regarding the staff required, the high interdisciplinary of at least some of the RI platforms get visible. Often different technical backgrounds also outside the food disciplines are necessary.

Sustainability Assessment of Food technologies, products and value chains (SAF)

ca. 0,5-1 Mio € for computing infrastructure, organisation and implementation of databases.

20 – 30 person*year of total effort, requiring an annual budget of 1,6-2,4 Mio €

Experts in •agriculture •foodprocessesandtechnologies •engineering •socialscience •earthscience •economic(micro-macroeconomics,econometric),statistictechniques,••lifecycle-based methods •computationandmodellingtechniques •developmentofindicators •datamanagers

Management systems for Lean manufacturing

minimal investment 0,5 Mio €

n.a. Staff and outsourced experts with knowledge on •differentcostreductiontechniques •technicalbackground •foodtechnology

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vI. annex :

teCHnICal DeSCRIptIonS

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5.1. aDvanCeD pRoCeSSInG teCHnoloGIeS

Short descriptionInnovation can be defined as new products, services and processes introduced in competitive markets. An idea or invention is not innovation until it has been tested and verified commercially or come into widespread use otherwise. Meanwhile, the road to commercialization ( innovation chain ) is long and complex involving many actors with different interests and roles. Many promising food products or technologies are never fully tested because of the lack of testing facilties.

the advanced processing technology platform offers capabilities, services and activities to help food companies and equipment suppliers to develop new products ( food and equipment ) based on advancement of traditional technologies or on emerging technologies.

The platform will contribute to improving the innovation potential of our customers by creating an arena where food producers and equipment suppliers can meet to test and further develop food processing technologies. These technologies can be either the advancement of existing processing technologies or new processing technologies that will be integrated in full production lines and are expected to improve profitability, increased product quality ( safety, sensory properties, health, convenience ) and/or reduce environmental impact ( energy, better use of raw materials, reuse / recycling of waste and reduction of waste ). The plattform will also have available tools ( DSS ) to help the food industry to select the suitable technology for a given propose.

Example of technologies that will be available is this RI are :

• Non-thermalprocessingtechnologies, such as pulsed electric field, HPP, cold plasma

• Advancedheating, such as microwave, ohmic heating, infrared and radio frequency ( RF ) heating

• Advancedprocessing, such as supercritical-fluid extraction and advanced membrane technology, ultrasonic cutting, high pressure homogenization and power ultrasound

A short description of the technologies is described below :

non-thermal processing technologies : High pressure processingis being mostly applied as a pasteurization process to extend the shelf life and control safety risk in a wide range of food products, without the use of high temperatures potentially detrimental to their sensitive quality traits. HP treatments at ambient temperature are capable of inactivating undesirable microorganisms and enzymes. In many cases the combination of HP treatment with mildly elevated temperature is needed to achieve inactivation of the more pressure and/or temperature resistant microorganisms and enzymes. HP processing has been extensively studied for selected pre-packaged foods and its application assures safety, shelf life extension and nutrient preservation ( Hayman et al., 2004 ). HP pasteurization is environmentally friendly and can retain the fresh-like characteristics of foods better than commonly used thermal treatments.

Apart from HP application as a non-thermal pasteurization technique, there are other potential applications ; HP processing ranging from 200 to 350MPa may denature proteins from the adductor muscle of mollusks such as oysters and clams. The treated muscle, which is responsible for closing the shell, will not be able to contract and the oyster will open. This exposes the meat for easy extraction, resulting in a significant yield increase. Another potential application is the cheeses maturation enhancement by increasing the aminopeptidases activity responsible for the maturation process. The gelatinization of starches under pressure is significantly different from that induced by heat, and hence they offer unique functional properties, like e.g. a formation of weak gels which could be used as fat replacer in dietary foods. Pressure induced proteins gels opens up to possible generation of new textures as they additionally retain their original flavour and colour accompanied by a glossy

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appearance. Such gels can be applied for the manufacturing of milk products to e.g. improve yoghurt texture or increase cheese yield.

non-thermal processing technologies : pulsed electric Field peFPulsed electric field ( PEF ) processing is a non-thermal method based on a pulsing high voltage ( typically 10 - 40 kV/cm for preservation proposed and 0.5 – 10 kV/cm for cell desintegration ) delivered to the product placed between a set of electrodes. This treatment, conducted at ambient, sub-ambient, or above ambient temperature for less than 1 s causes minimal or no detrimental effect on food quality attributes. When food is subjected to the electrical high-intensity pulses several events, such as resistance heating, electrolysis and disruption of cell membranes, can occur contributing to the inactivation of microorganisms. A combination of mild heat and PEF might also be helpful to achieve sufficient enzyme inactivation. Although the study of PEF technology has been focused on its ability to inactivate microorganisms in liquid food products at low temperatures, some other applications in the food industry have been explored as well, such as the enhancement of drying efficiency and decontamination of liquid waste and the increase of the yield in fruit and vegetables juice extraction.

non-thermal processing technologies : Cold plasmaCold plasma is considered a relatively new decontamination technology in the field of food processing. Plasma is often referred to as the fourth state of matter after solid, liquid, and gas and is actually a mix of ionized gas molecules and free electrons. Cold plasma is electrically energized matter in a gaseous state, and it can be generated by electrical discharge. When a gas passes through plasma, the gas becomes excited, ionized, and full of dissociated electrons, leading to the formation of active species such as atomic oxygen, ozone, and free radicals ( e.g., hydroxyl, superoxide, and nitrogen oxides ). These reactive species have been shown to have antimicrobial activity by oxidation, although the exact mechanism of microbial cell inactivation is not fully understood. Formerly, stable glow discharge plasma could only be generated under vacuum or with gases such as helium and argon.

advanced heating : Infrared processingInfrared ( IR ) radiation of food implies the application of IR radiation ( part of the electromagnetic spectrum lying between ultraviolet ( UV ) and microwave ( MW ) energy ) to foods with the purpose of thermal microbial decontamination, baking, drying, roasting etc.

IR radiation, having both a spectral and directional dependence, can be divided into three different categories : near-IR ( NIR ), mid-IR radiation ( MIR ), and far-IR radiation ( FIR ), corresponding to the spectral ranges of 0.75 to 1.4, 1.4 to 3, and 3 to 1000 µm. Also, the terms shortwave ( SIR ), medium wave and long wave IR are used which correspond to similar wavelength ranges. In general, FIR radiation is seen as advantageous for food processing because most food components absorb radiative energy in the FIR region. However, for certain applications SIR and MIR are being used more and more frequently due to higher energy transfer and for SIR slight penentration into materials is achieved.

advanced heating : microwave processingMicrowaves are part of the electromagnetic spectrum and belong to the non-ionizing radiation spectrum because of their small energy quanta, which are much smaller than that of visual light and, as such, can neither ionize materials nor alter the atomic structures of a molecule.

The principle of microwave heating is based on the fact that electromagnetic fields cause dipoles, mainly water molecules, and dissolved ions ( due to salt content in foods ) to move rapidly. These movements correspond to heat, which results in dissipation of the electromagnetic energy as heat within the foods. Microwaves are generally in the frequency range 300 MHz to 300 GHz. For heating purposes, industrial as well as domestic heating applications, only the ISM ( Industrial, Scientific and Medical ) frequencies are however available. In practice, this often means microwave applications at

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2450 MHz in Europe, with some additional frequencies available in several countries ( e.g. 896 MHz available in the UK, and 433.92 MHz available in Europe, though subject to local acceptance ).

Current aplications of microwave processing of foods is focused on pasteurization/sterilization, drying, tempering, extraction and blanching.

advanced heating : ohmic heatingOhmic heating ( also called Joule heating or electrical resistance heating or electro-conductive heating ) is a process where electrical currents are passed through the food, with a resulting heating of the food. Electrical energy is dissipated as heat internally within the material. Ohmic heating involves the presence of electrodes contacting the food, unrestricted waveform and frequency . The principle of ohmic heating is based on the fact that electrical currents which pass though the food will cause heating, due to the electrical resistance of the food. The resistance of the food will in turn depend on the conductivity of the food in question.

Current aplications of ohmic heating of foods is focused on pasteurization/sterilization and cooking. It can be applied to liquid foods and liquid foods containing large particles in a continuous process. Application to solid or semisolid foods is more problematic due to the difficulty of achieving good contact between the food and the electrodes but it has been successfully applied to meats.

advanced processing : power ultrasoundThe use of ultrasound has attracted considerable interest in food science and technology due to its promising effects in food processing and preservation ( Knorr et al., 2004 ). The sound ranges employed can be broadly divided into high frequency, low energy, diagnostic ultrasound in the MHz range ; and low frequency, high energy, power ultrasound in the kHz range ( Mason and Chemat, 2003 ). Ultrasonication is the treatment of a liquid sample with ultrasonic ( >20 kHz ) waves resulting in agitation. Sound waves propagate into the liquid media result in alternating high-pressure ( compression ) and low-pressure ( rarefaction ) cycles. During rarefaction, high-intensity sonic waves create small vacuum bubbles or voids in the liquid, which then collapse violently ( cavitation ) during compression, creating very high local temperatures.

Depending on the type and properties of the material exposed to ultrasound, mechanical, thermal and chemical effects are induced. Amongst them, the effect of cavitation is of highest importance ( De Gennaro et al., 1999 ; Earnshaw, 1998 ; Leighton, 1998 ). When applying transient, alternating pressure to liquid systems, compression and decompression is induced, causing bubble formation and implosion and leading to hydrodynamic shear, compression heating and formation of mircrostreamers.

Power ultrasound in particular, has many application in food industry such as extraction, emulsification defoaming, crystallization.

advanced processing : ultrasonic cuttingis used as enhanced cutting for improving the cutting quality while reducing energy requirements. Its main attributes are improved fine cuts without adhesion of the cutting material in a fast and hygienic manner. The ultrasonic cutter vibrates its blade with an amplitude of 10 to 70 micrometres and a frequency of 20 to 40 kHz.

It is seen as having uses for very fast and accurate slicing of foods such as cheese and meats and can be automated to produce portions to within 1g.

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advanced processing : High pressure Homogenisation ( HpH )is a mechanical process, which is achieved by forcing a fluidic product through a narrow gap at high pressure of 350-400 MP. As a result, inactivation of microbes and/or enzymes or a change in food attributes in order to obtain consumer-desired qualities is achieved. Subjecting the liquid to very high shear stress forms very fine emulsion droplets from the micro- to the nano-scale range. Emulsions are more stabilized in comparison to ordinary emulsions. Introduction and application of Nano emulsions in food products does not only reduce the requirements for additives such as emulsifiers but can also facilitate less fat use without a compromise in creaminess with the possibility of producing healthier foods which contain less fat.

advanced processing : Supercritical-fluid extraction ( SFe ) is a separation process in which a substance is dissolved into a fluid which is brought above its critical temperature and pressure. The extraction process usually consists of two parts : the extraction of the components from the matrix material and the separation of the component( s ) from the supercritical fluid. The matrix material from which the components need to be extracted is placed in an extractor where it is brought into contact with the supercritical fluid. For an extraction with solids, a batch wise process is performed whereas liquids can be done with a continuous process. After this the supercritical fluid and the components pass through a separator where the pressure and/or temperature are reduced, thereby reducing the dissolving power of the supercritical fluid. As a consequence, the components are separated.

Supercritical fluid extraction ( SFE ) does not use chemical solvents and it is therefore conisered a “green” technology. The use of supercritical carbon dioxide for the extraction and isolation of valuable compounds has been demonstrated in several applications ( Hartono et al ( 2001 ) ).

advanced processing : advanced membrane technology Membrane technology is a generic term for a number of different, very characteristic separation processes. The membrane separation process is based on the presence of semi permeable membranes. The principle is quite simple : the membrane acts as a very specific filter that will let water flow through, while it catches suspended solids and other substances. There are various methods to enable substances to penetrate a membrane : examples of these methods ( driving force ) are the applications of high pressure, the maintenance of a concentration gradient on both sides of the membrane and the introduction of an electric potential across the membrane. Membranes occupy through a selective separation wall : certain substances can pass through the membrane, while other substances are caught.

Membrane filtration processes are classified according to the membrane pore sizes, which dictate the size of the particles they are able to retain : membrane filtration can be divided up between micro and ultrafiltration on the one hand and nanofiltration and Reverse Osmosis ( RO ) on the other hand. When membrane filtration is used for the removal of larger particles, micro filtration and ultrafiltration are applied.

Membranes are now competitive for conventional techniques : their intrinsic characteristics of efficiency, operational simplicity and flexibility, relatively high selectivity and permeability for the transport of specific components, low energy requirements, good stability under a wide spectrum of operating conditions, environment compatibility, easy control and scale-up have been confirmed in a large variety of applications and operations, as molecular separation, fractionation, concentrations, purifications, clarifications, emulsifications, crystallization, etc., in both liquid and gas phases and in a wide spectrum of operating parameters such as pH, temperature, pressure.

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activities / value propositionThe main activities that this platform will carry out will be :

• Tomakeavailableforfoodindustryandequipmentsuppliersanetworkofinnovativepilotsizefactories and test bed facilities to develop and optimise manufacturing solutions for entire processes not limited to single food process steps alone.

• Broadeningthescopeofeachtechnologytonewapplicationsallowingcrossfertilizationbetweensectors

• Acollectionofbestpracticesandfoodprocessingandmanufacturingsolutionstoproduceinnovative products and increase the sustainability of food production ( costs and environment )

• Toolstoselectsuitabletechnologiesandfocusonsolvingbottlenecksfortechnologyimplementation

• Focusonappliedresearchandtransferofcuttingedgetechnologiestothefoodprocessingindustryand supporting burgeoning basic research to applied research

• Offeringconditionstoensureprogressinresearchandinnovationbymakingavailableuniqueresources to carry out research and technology transfer, and by constituting a place where industrialists can cooperate with researchers.

• Offertheindustrycustomizedstafftrainingactivities.

• Offerflexible,scalableequipmentforadiversityofproducts

The benefits for users ( both technology/equipment/service providers and food manufacturers ) will be :

to speed up the innovation process • by creating an innovative forum for idea generation

• supporting the selection and validation of technologies by making available skilled personal resources and test bed facilties for a wide range of technologies able to produce real food products in real industrial equipment at pilot scale

to access best practices, success and failure stories of technological applications both from food industry and equipment suppliers. Cross –fertlisation between sectors is also expected.

to have constantly available business-related information useful to operate consciously in the market, to make reliable forecasts, to react fast to changes and to trigger internal innovation processes according to the change of the context

To understand what are the key-success factors and barriers of technology and business innovation and to design the innovation process and exploitation strategy accordingly

All of these benefits will translate into cost savings and more environmentally sustainable processes. In more detail, examples of the technolofical value proposition for specific technologies are :

non-thermal processing technologies : High pressure processing Hpp :• Foodretainsitsshape,evenatextremepressures.

• Asnoheatisneeded,thesensorycharacteristicsofthefoodareretainedwithoutcompromisingmicrobial safety.

• HPPinactivatesvegetativebacteria

• Forfoodswherethermalpasteurizationisnotanoption(duetoflavor,textureorcolorchanges)HPP can extend the shelf life by two to three fold over a non-pasteurized counterpart

• Improvedfoodsafety

• Shelf-lifeofHPtreatedfoodproductscaninsomecasesbeextendedcomparedtothermallytreatedones ( i.e. orange juice, meat products etc )

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non-thermal processing technologies : pulsed electric Field peF :• Asnon-thermalpreservationtechnologycanleadtoimprovedquality

• Asanassistingtechnique,itcanbeappliedinsomeprocessingoperationssuchasfreezing,peeling,cutting, etc.

• Decontaminatesthefoodmatrixwiththeleasteffectsonorganolepticproperties

• Expandstheshelflifeinfreshproducts

• Extendedshelflifewithoutchemicaladditives

• Inactivationofvegetativemicro-organismsincludingyeasts,spoilagemicro-organismsandpathogens

• Juiceyieldincreaseviaformationoflarge,permanentporesincellulartissues

• Itmayreducetheamountofrequiredenergyandthereforesupportssustainablefoodmanufacturing

• Lowelectricfieldstrengthandorpulsenumbercausesreversiblecellrupturestimulatingastressreaction in plants or cell cultures, allowing enzymes or proteins with potential health benefits to be released.

• Consumersperceivethetechniqueasenvironmentallyfriendlyandarepositiveaboutthenaturalness of the product

• Energyefficient,waste-freetechnique

non-thermal processing technologies : Cold plasma :• Novelnonthermalfoodprocessingtechnologythatusesenergetic,reactivegasestoinactivate

contaminating microbes on meats, poultry, fruits, and vegetables.

• useselectricityandacarriergas,suchasair,oxygen,nitrogen,orhelium

• antimicrobialchemicalagentsarenotrequired

• primarymodesofactionareduetoUVlightandreactivechemicalproductsofthecoldplasmaionization process

• awidearrayofcoldplasmasystemsthatoperateatatmosphericpressuresorinlowpressuretreatment chambers are under development

• Reductionsofgreaterthan5logscanbeobtainedforpathogenssuchasSalmonella,EscherichiacoliO157 :H7, Listeria monocytogenes, and Staphylococcus aureus.

• Forfoodswhichcaneithernotadequatelysanitizedorareotherwiseunsuitablefortreatmentwithchemicals, heat or other conventional food processing tools

advanced heating : Infrared processing :• Highenergytransfercapacity

• Heatpenetrationdirectlyatthesurfaceof/intotheproduct

• Fastregulationresponse

• Compactandflexibleinfraredovens

• Goodpossibilitiesforprocesscontrol;

• Noheatingofsurroundingair

• Rapidprocessing(processtimereductionupto50%)

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• Energyefficient(savingsof50%havebeenachieved)

• contact-freeanduniform

• greatsuccessindrying,roasting,bakingandsterilization

• combinationofconvectionorimpingmentgivingsuitableadavntagesforproductquality

• Enhancementoftaste

• Extensionofshelf-life

• Alternativefryingtechnology

• Suitableforpackedfoodsintransparentpackages

• Wastereduction

• Fatreduction

advanced heating : microwave processing :• Directtotheproduct

• Veryfastin-depthheating

• Significantreductionsinprocessingtime

• Improvedquality

• Fastregulationresponse

• Noheatingofsurroundingair

• Energysavings

• Cleanenergytransfer

• Extendshelf-life(e.g.microwavepasteurisation,microwavesterilisation,microwave-assisteddrying etc. ),

• Achievespecificfunctionalproperties(e.g.microwavepuffingofgrains)

advanced heating : ohmic heating• Rapidlyheatingliquidfoods,orsolid-liquidmixtures

• Theheatisvolumetricallygeneratedandnottransfered

• Conduction,convectionandin-depthpenetrationarenotanproblemanymore

• Possibletoprocesslargefoodparticulatesinheatsensitiveliquids

• Reducedfouling

advanced processing : power ultrasound• Enzymeinactivationadjunctatlowertemperaturesforimprovedqualityattributes

• Increasedextractionefficiency,yieldinsolvent,aqueousorsupercriticalsystems

• Costeffectiveemulsionformation

• Increasedfluxrates,reducedfouling

• Accelerationofheating,coolinganddryingofproductsatlowtemperature

• Accelerationofseparationandcrystallisationprocesses

• Increasingproductionofmetabolites,accelerationoffermentationprocesses

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advanced processing :ultrasonic cutting : • Improvedcuttingqualityandprecision(cuttingforceincreasebyincreasingcuttingvelocity).

• Reducedcuttingforcesinacontinuouscuttingoperation

• Avoidswettingofcutmaterial(comparedtowaterjetcutting)

• Avoidsnoises,smokeandaircontamination(comparedtolasercutting)

• Increasestheblade’slifebystayingsharperforalongertime

• Avoidsfoodproductsadheringtothebladewhichmakescleaningmoreconvenient

• Lessenergyrequiredincomparisonwiththeothermethods.

• Productwastereduction

• Consumersperceivethismethodasquitefast,cleanandconvenient.

• Moresanitary

• Lessdowntime

• Moreconsistencyatcuttingsurface

• Multi–layers&densitiesarecutwithoutsmearing

• Preventsmouldinessofthesurfaceofbakeryproducts

• Cleancut,lowerrunningandmaintenancecosts,withminimalorevenwaste-freecuttings

advanced processing : High pressure Homogenisation HpH :Fresh product

• Shelflifeextension

• Inactivationoffood-bornepathogens(suchasListeria,E.coliandSalmonella)byreducingthemmore than 5-log

• Anactivepost-harvestinterventionsteptoincreasethesafetyofrawshellfishfromotentialVibriobacteria decontamination

• PotentialVibriobacteriadecontamination

• Nochemicaladditives

• Verylittleeffectonlowmolecularweightcompoundssuchasflavourcompounds,vitamins,andpigments as compared to thermal processes

• Enhancedtasteandflavour

• Lesspreservativeneeded(suchassalt,lactates,etc.)

• Minimallyprocessed

• Appearseffectiveinimprovingmeat,eggorsoyaproteingelationproperties

• Highconsumeracceptance

• Beneficialforheat-sensitiveproducts

• Cleanlabelling:reductionoffoodadditivesinfoodproduction(stabilisers,emulgents,water-retention )

• Energyefficient

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advanced processing : SFe extraction• Greentechnology(nosolvents)

• Specificcompoundextraction

• Possibletorecoverbioactivesofhighaddedvaluefrombyproducts

• Suitableforheatlyproducts

• Nothermaleffects

• Innovativeproducts

• Lowenviromentalimpact

• Cleanandfunctionalextracts

How does the platform close the gap identified ?This platform aims to fulfill gaps identified in D3.11 and summarized as Pilot size factories for developing, testing and training of new manufacturing solutions for the food processing industry.

More specifically, it will cover aspects like :

• Pilotsizefactories(full-scaleindustrialtestbedsoremulatedindustriallaboratoryfacilities)fordeveloping and testing pilot equipment, e.g. modular manufacturing systems, autonomous, flexible and/or mobile manufacturing solutions, etc. The “factories” must resemble real-world industrial environments ( including human-machine and machine-machine interactions ), but are able to withstand disturbances.

• Databasesthatcanbeusedtoinformthepublicabouttheeffectsofover-processingoffoodproducts, collected through portals ;

• Databasescontaininginformationaboutmethodsforshelflifeelongationinfoodproducts;

• BiotechnologyR&Dcentresdevelopingvalueaddedproductsfromwastesuchasalcohol,colorants,essences, etc.

• Dedicatedmultidisciplinaryresearchteamsconsistingoffoodscientists,materialscientists,processengineers, equipment developers, software developers and technology specialists

• Researchfacilitiesforradicalinnovationsthatrequireextensivemultidisciplinaryresearch,equipment development and construction and installation of pilot scale production facilities

• Legislativetoolstofacilitatetheimplementation

• SpecificfacilityandknowledgeelementsforfoodprocessingenablingthecurrentRItoassessenvironmental impact and develop new technologies to reduce CO2 emission )

• Trainingfacilitiesforpracticalonthejobtrainingofindustrystaffandresearchersonuseofmanufacturing solutions.

Current situation :The innovation process in the food industry is slow and implemention of new technologies is often delayed due to the lack of testing and verification facilities. Although, there is a significant number of small scale pilot plants at universities, research institutes or regional develoment centers for testing and manufacturing of new food products or development of processing technologies, these pilot plants :

• areunevenlyspreadaroundEurope

• havetoolowproductioncapacity

• donothavepossibilityofpackaging–Nopossibilityofshelf-lifetesting

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• donothavecapacitytomanufacturefoodproductstobetestedbyconsumers

• donothavepossibilitytointegratenoveltechnologiesintraditionalproductionlines

• areoftenspecializedfortheproductionofafewproducts(eg.beer,milk,meat,bakery,chocolate)based on traditional technologies

• Ontheonehand,thelargeequipmentandrunningcostsandontheotherhand,thesmallnumberof clients lead to poor financial viability of these pilot plants

• theequipmentmaybecomeobsoleteinashorttime

• criticalmassislackingfortrainingactivities

• havefewpossibilitiesformarketingoutsideregionorcountry

• havefewpossibilitiesforinvestimentinICTtoolsorsensors

• havefewpossibilitiesforbuildingdatabasesorDSStools

• donotalowcomparisonwithtraditional/alternativetechnologies

Desired position / situation :To make available in Europe a well-developed and easily accessible test infrastructure, with access to technical and business support to speed up the the innovation process ( in the early stages by helping on screening and during the final stage helping with commercialization ) of new products and technologies by food producing companies and equipment suppliers, mainly SMEs. Product and process development will be done science based ( not trial and error ) using modern toolboxes. This will be achieved by :

• providingtestingandverificationpossibilitiesforusers/customers

• makingpossibilethescaleupoftheideastestedatlabscaleandproducingnewproducsonanindustrial scale based either on traditional technologies or new technologies

• havingthepossibilityoftestingnewequipmentandnewtechnologiesinreal(smallscale)production lines

• havingthepossibilitytotestandgetacessandinformationaboutthestrengthofnewtechnologies

• gettingexpertsupport,DSStoolsandtestfacilitiestoselectthemostsustainbletechnologyforthenew products

• acessingfoodtechnologiststounderstandtheeffectsofprocessonfoodpropertiesandconsequently further improve the performance of the equipment

• facilitatingnetworkingwithinthefoodindustrytopromotethetechnologies

• facilitatingnetworkingwithequipmentsuppliers

• benchmarketingoftechnologies

• supplyingsolutionswithhigherenvironmentalperformance

• leadingtofasterdevelopmentofnewenvironmentaltechnologyinnovations

• givingaccesstolegislation

• testingchangesofrawmaterials

• testingadjustmentsofproductionprocesses

accessing technical expertise and training courses

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Challenge :Select a group of core nodes providing test bed facilities willing to set up entire food processes with all intermediate steps from raw materials or early semi-finished goods to end products and operating on a European-wide market.

Integration of existing research infrastructuresConsidering the mission of this RI, it will be constituted by a group of core nodes providing test bed facilities and providing research on application of these technologies as the core mission. To accomplish this task, which implies setting up entire food processe lines with all intermediate steps from raw materials or early semi-finished goods to end products, the platform will necessarily be linked to all the platforms of the food processing RIs, in order to exchange technology-related information that is needed to carry out complete food processes. Thus, the platform will be constituted by a set of core-nodes closely interconnected, linked with the rest of other platforms.

The following research infrastructures were identified as European potential candidates to become core nodes of the Advanced Technologies platform, since they embrace ( also ) an applied approach to food processing. They are below classified per area of specialization :

Infrastructures dealing with Hpp• Germaninstituteoffoodtechnologies(DIL),Germany(Researchinstallation,equipment,pilot

plant, instrument, observation, monitoring unit )

• InstitutdeRecercaiTecnologiaAgroalimentàries(IRTA),Spain:Research&technologyfood&agriculture ( equipment, pilot plant )

• KULeuven,Belgium(equipment,pilotplant,instrument)

• TheSwedishInstituteforFoodandBiotechnology(SIK),Sweden(Researchinstallation,Pilotplant,Knowledge transfer )

• TechnicalUniversityofBerlin,Germany(equipment,pilotplant,,instrument,observation,knowledge transfer )

• NationalTechnicalUniversityofAthens(NTUA),Greece)(equipment,pilotplant)

• CampdenBRI,UK

• ProdAL,UniversitySalerno(pilotplant)

Infrastructures dealing with peF• Germaninstituteoffoodtechnologies(DIL),Germany(Researchinstallation,equipment,pilot


• InstitutdeRecercaiTecnologiaAgroalimentàries(IRTA),Spain(equipment,pilotplant)


• Food&BiobasedResearch–WageningenUR(equipment,pilotplant)

• UniversityLeida,Spain

• UniversityCampiege,France

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Infrastructures dealing with cold plasma• Food&BiobasedResearch–WageningenUR(equipment,pilotplant)

• Leibniz-InstitutfürAgrartechnikPotsdam-Bornime.V.(equipment)


• DublinInstituteofTechnology(equipment)

Infrastructures dealing with Infrared processing :• TheSwedishInstituteforFoodandBiotechnology(SIK),Sweden(Researchinstallation,Pilotplant,

Knowledge transfer )

• ONIRIS,Nantes,France(Researchinstallation,Pilotplant)

Infrastructures dealing with microwave processing :• TheSwedishInstituteforFoodandBiotechnology(SIK),Sweden(Researchinstallation,Pilotplant,


• Research&technologyfood&agriculture(IRTA),Spain(equipment,pilotplant)

• KarlsruheInstituteofTechnology,Germany

Infrastructures dealing with ohmic heating• TechnicaluniversityofBerlin,Germany(equipment,pilotplant,,instrument,observation,

knowledge transfer )

• UniversidadedoMinho,Portugal(labscale)

• CTCPA,France(pilotplant)

• CampdenBRI,UK

• ProdAL,UniversitySalerno(pilotplant)

• UniversityCollege,Dublin(Research,pilotplant,meatprocessing)

Infrastructures dealing with power ultrasound/ultrasonic cutting : • TheSwedishInstituteforFoodandBiotechnology(SIK),Sweden(Researchinstallation)

• TechnicaluniversityofBerlin,Germany(equipment,pilotplant,,instrument,observation,knowledge transfer )

• Germaninstituteoffoodtechnologies(DIL),Germany(Researchinstallation,equipment,pilotplant, instrument, observation, monitoring unit )

• CampdenBRI,UK

Infrastructures dealing with High pressure Homogenisation HpH :• Germaninstituteoffoodtechnologies(DIL),Germany(Researchinstallation,equipment,pilot


• KULeuven,Belgium(equipment,pilotplant,instrument)

• UniversityMontpellierII,France

• UniversityAntonomBarcelona,Spain

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Infrastructures dealing with SFe extraction• TheSwedishInstituteforFoodandBiotechnology(SIK),Sweden(Researchinstallation,Pilotplant,


meat processing• TheSwedishInstituteforFoodandBiotechnology(SIK),Sweden(Researchinstallation)

• Nofima,Norway

• IRTA,Spain

Heat processing• CampdenBRI

• LeatherheadFoodResearch

Dairy and Brewery• UniversityCollegeCork

Bakery products• CampdenBRI

Confectionary products• Leatherheadfoodresearch

• UniversityGhent

Database on Database of Laboratory and Pilot Plant Equipment at ( https ://www.iseki-food.net/equipment )

technical requirementsThe RI Processing technology will create a network of pilot size facilities having equipment for manufacture of food products based on traditional technology and new technogies. At selected locations will be available pilot factories able to manufacture and pack innovative products in amounts required to perform shelf-life studies and sensorial evaluation. To guarantee the flexibility these factories will be modular manufacturing systems, with autonomous, flexible and/or mobile manufacturing solutions. The plants will be equiped with process control systems, decision support tools, simulation tools.

Processing lines will consist of modules of processing equipment that can be arranged depending on the products to be produced. The equipment in modules may also be moved to food manufacturers own plants to be tested in place. The factories must resemble real-world industrial environments ( including human-machine and machine-machine interactions ), but must be able to withstand disturbances.

The processing equipment will be modelled so that prediction software can be used to minimize the number of experimental trials and substantially reduce the human and financial costs.

The RI are needed for researchers and companies to test new ideas. It is essential to assure that these pilot size factories are international, have enough critical mass, and that they are open to both industry and academia.

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Product specific facilities ( e.g bakeries, meat processing ) or Technolgy specific facilties ( e.g.thermal processing, non-thermal, freezing ) will be created at one site or by collaboration between different sites. This will create enough critical massa for development and sharing of databases or portals where information about new technologies / novel management systems is made available for food manufacturers.

Product specific networks and Technology Specific networks would increase the dissemination of new solutions in Europe.

Training with practical demonstrations will create an arena for inspirations and cross fertlisation between the food producers and equipment suppliers.

the specific scope of application of the selected technologies is :High pressure processing Hpp is applied as a time and energy saving processing step for the meat industry, e.g. for ready-to-eat whole muscle and sliced meats. Functional and rheological properties of meat can be improved. Safe packaged fruit and vegetable products without additives or preservatives can be delivered by HPP, retaining the sensory qualities, texture, colour and nutritional content of the fresh-picked product. HPP can provide juices and smoothies safe and with a fresh appearance without any need for additives or preservatives. The extended refrigerated shelf life achieved by HPP allows more efficient production scheduling and reduces food waste. This technology provides also ready-to-use wet salads and dips safer and more natural with extended quality and shelf-life. High pressure processing can achieve clean and high yield meat separation from lobsters, oysters, clams, and other fresh products while doubling or more increase its shelf life

pulsed electric Filed peF is applied for microbial inactivation in all vegetables and fruits and in fruit juices as in liquid products as a pasteurization method, to extend shelf life significantly. It is also well applied via cell disintegration in processes where mass transfer is important to improve drying, extraction, distillation and structure modification. Examples here are potatoes ( softening for easier cutting ), mango ( softening to facilitate peeling and removing stone ), pumpkin ( softer structure, easier to cut ), and tomato ( easier peeling ). Furthermore, it is used as an extracting method for juice, colorants, pigments, enzymes. Examples are grape/wine ( improved colour extraction ) or red beetroot and tomato ( extraction of colorants ).

Cold plasma is under investigation and applied on a non-industrial scale to inactivate contaminating microbes on meat, poultry, fruits, vegetables and other foods with fragile surfaces. These foods are either not adequately sanitized or are otherwise unsuitable for treatment with chemicals, heat or other conventional food processing tools. Food waste is reduced in this manner as well as shelf life is extended.

Infrared processing has a large number of applications in the food industry.

IR drying is very suitable for thin products ( slices of fruit, vegetables and mushrooms ) or thin layers of product ( for example powders or small pieces of solid products ). Reduction in drying time, alternative energy source, increased energy efficiency, uniform temperature in the product while drying, better-quality finished products, a reduced necessity for air flow across the product, high degree of process control parameters, and space saving along with clean working environment are the numerous advantages of the use of IR radiation technology for dehydrating foods. Successful examples can be mentioned for : fruit and vegetable products such as potatoes, sweet potatoes, onions, kiwifruit and apples as well as drying of seaweed, and fish flakes. Drying of pasta and crackers can also be achieved in tunnel infrared dryers.

Enzyme inactivation is also possible with IR heating. Lipoxygenase, lipases α-amylases and lipases are some examples of enzymes which were effectively inactivated by IR heating ( Sawai et al., 2003 ). However, inactivation of enzymes with IR heating follows the same thermal destruction kinetics as for

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heat in general. Blanching of fruit and vegetables with IR heating also has the advantage of reduced water usage.

pasteurisation and decontamination

IR heating can be used to inactivate vegetative bacteria and spores, yeast, and mould in both liquid and solid foods. As for enzyme inactivation the thermal heat load necessary to inactivate microorganisms is the same as for other heating methods. Therefore, the result is dependent on product temperature and how even the temperature is distributed. Thus infrared power level, starting temperature of food sample, peak wavelength, and bandwidth of infrared heating source, sample depth, types of microorganisms, moisture content, physiological phase of microorganisms ( exponential or stationary phase ), and types of food materials are among the parameters which influence the efficacy of microbial inactivation by infrared heating. IR heating for spore decontamination in spices for example is achieved at lower temperatures in spices with higher moisture content ( Staak, 2008 )

Due to the low penetration depth infrared heating is especially advantageous for surface pasteurization of foods just before packaging or of packaged foods. A high temperature of the surface can be achieved for short times, while the volume of the product remains at a low temperature with little or no effect on other quality parameters. Some examples are surface pasteurization of sponge cake, other bakery products, ham, sausages, nuts and recently also cheese ( Bergström, 2010 ). For this kind of application it is advantageous to use SIR, which can achieve a fast temperature rise and which has a penetration depth of a few millimetres into the product.

Roasting involves colour and/ or flavour development mainly at the surface of a food. For these applications MIR or FIR may be advantageous. With the low penetration depth at these wavelengths IR heating can result in similar colour and flavour characteristics as can be achieved with contact heating or convective heating at high temperatures. Among applications for infrared roasting are : roasting of almonds ( Yang et al., 2010, Bingol et al., 2011 ) and hazelnuts ( Uysal et al., 2009 ), roasting of ham or meat and crust formation in bread ( Olsson, 2005 ).

Baking is an interesting application for IR heating. Users of the technology apply different wavelength areas for baking. In general, SIR baking will have the greatest effect on reducing baking time ( setting the crumb ). However, to achieve various thickness and colour of the crust MIR and FIR are also of interest. Baking of crackers, rolls and other products with small dimensions is very efficient with IR heating. IR heating can be combined with hot air, impingement or microwaves to achieve process efficiency combined with desired product quality.

The high heat transfer rates of IR heating can also open the possibility of using IR as an alternative to conventional frying enabling the production of products containing less fat or the substitution to more healthy fat.

Infrared heat provides a particularly efficient and reliable alternative for many heating processes in the food industry. Apart from the applications described here, there are many more : cooking sausages, heating wafers prior to embossing, setting coatings on chicken strips, browning waffles, “relaxing” shellfish, blackening vegetables, browning of hams, popping corn for cereals, branding of meat and cheese.

Other miscellaneous food processing operations include ground beef patties by IR broiling in a conveyor broiler, infrared-based systems with conventional ovens for baking rice crackers and for roasting fish pastes, increase in the rate of colour development of the crust and shortened the heating time of par-baked baguettes during post-baking by infrared radiation and jet impingement, IR dry peeling of tomatoes, tuna thawing using FIR heating without drip losses and discoloration.

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microwave processing

Microwave drying increases the product temperature from inside the product and forces moisture migration to the surface while maintaining a relatively low drying temperature. This generally results in products that appear fresher with better rehydration properties compared to hot air drying. For several applications, the best system appears to be microwave-assisted drying incorporating 70% conventional drying with 30% microwave assistance. This system has the lowest operating cost while maintaining a final product of good quality. Microwave drying has been evaluated for e.g. pasta, fruits and vegetables.

The application of microwave technology in pasteurization, sterilization, and microbial reduction treatments came about because of its unique heating mechanism. Ironically, this unique mechanism is also the reason for the previous poor adoption of microwave technology in these areas, mainly because of tendencies to non-uniform heating for some applications where modelling-based design was previously not used. It is, therefore, important to understand that exploitation of the advantages associated with microwave heating should be done with full understanding of its interaction with the product or process in question. Current simulation tools can accurately highlight any heating non-uniformity and help design applicators using noncoherent multiple microwave sources to address this problem. Several installations for microwave pasteurization of e.g. ready-made meals, for in-pack systems ( e.g. www.micvac.se ), and tubular systems and a limited number of systems for microwave sterilisation of foods ( http ://www.topsfoods.com/ ) exist.

Microwave blanching has the advantage of producing good product quality ( sensorially and microbiologically ), with less production time and use of resources ( energy, water, and steam ). The blanching unit can also be compact and easily maintained.

Another application for microwaves related to blanching is the area of disinfection and pest control. Batista ( 2002 ) reported that microwave disinfection of Keitt mangoes gave better quality and longer shelf life than an equivalent hot water treatment. Ikediala et al. ( 1999 ) used 915 MHz microwave to heat Bing cherry pits and temperatures 45-55°C for quarantine treatment. The treatment was said to compare favorably with the usual methyl bromine fumigation used in fruit control. However, Nelson ( 1996 ) concluded in a review that RF and microwave treatments are not a practical alternative to other stored-grain insect control methods. While there are varying opinions about the practicality of microwave use in pest and disease control, there has never been any doubt about the effectiveness of this technology.

Microwave or microwave-assisted extraction processes can also be beneficial in sectors of the food industry and for food analysis. The use of microwaves in extraction takes advantage of the selectivity of microwave heating and may be applied either as a pre-treatment or as an integral part of the solvent extraction process. As a pre-treatment, microwaves are used to heat the product to be extracted causing the cell walls to rupture, facilitating a higher release, as well as improving the quality of the product during mechanical extraction.

Meat tempering ( a process that brings deep frozen products from e.g. -20°C to a few degrees below the freezing point for further processing ) is the largest use of microwave processing in the food industry ( 1.8 million tons frozen food per year ), followed by bacon processing for food services ( 2-3 million slices per hour ) and sausage cooking. Metaxas ( 1996 ) mentions a figure of 250 installations all over the world. Tempering of fish blocks by microwave processing also exists.

ohmic heating

Possible applications are continuous heating of pumpable foods, such as for example jam, soups, sauces, and stews, fruit slices in syrups and sauces as well as heat sensitive liquids. Ohmic heating is useful for the treatment of foods containing proteins, which tend to denature and coagulate when thermally processed. For example, liquid egg can be rapidly heated by ohmic heating, without coagulating it. Fruit juices can be ohmically treated to inactivate enzymes while maintaining the flavour. Other

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potential applications of ohmic heating include thawing, blanching, fermentation, on-line detection of starch gelatinization, dehydration, peeling, and extraction.

power ultrasound applications in the food industry

In the table below is shown a number of applications for power ultrasound in food industry

application Benefit

Extraction Increased extraction efficiency, yield in solvent, aqueous or supercritical systems

Emulsification/homogenization Cost effective emulsion formation

Filtration/screening Increased flux rates, reduced fouling

Defoaming Increased production throughput, reduction or elimination of antifoam chemicals and reduced wastage in bottling lines

Separation Adjunct for use in non-mechanical separation procedures

Viscosity alteration Non-chemical modification for improved processing traits, reduced additives, etc.

Extrusion Increased throughput

Crystallization Formation of smaller crystals

Enzyme and microbiala inactivation Enzyme inactivation adjunct at lower temperatures for improved quality attributes

Fermentationa Increasing production of metabolites, acceleration of fermentation processes

Heat transfera Acceleration of heating, cooling and drying of products at low temperature

a The authors were not aware of any commercial scale installation of this application( Adapted from Patist and Bates, 2008 )

ultrasonic cutting is applied to a wide range of confectionary, nutrition bar, cakes, pastries, cereal bars, cookies, pet foods etc. as well as in cheese, servings, deli, meat, bacon or frozen food. These food stuffs can be simple, multi-layered or of various densities. It can be integrated into food processes in a fully of semi-automatic way, continuously or for batch operations, for cutting on trays, in pans and on conveyor belt lines. This technology allows the blade to enter effortlessly and almost without any friction with the product.

High pressure Homogenisation HpH is applied to food manufacturing factories processing food products that need to be stabilized by emulsifiers, as well as food factories producing diet food products. HPH is applied for inactivation of microbes and/or enzymes or a change in food attributes in order to obtain consumer-desired qualities. Products which can be processed using this technology include ham slices, snacks, fish ; ready to-eat meats ( cold cuts ), fresh juice, prepared fruits and vegetables, picked crabmeat and oysters, fruit smoothies, guacamole, chicken strips, and salsa.

SFe extraction

Counter-current liquid-liquid SFE can be used to extract milk from fat, refining of olive oil, extracting bioactives ( eg polyphenolic compounds from grape, natural colorants as well as antioxidants, carotenoids )

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expected functionsThe platform is expected to have the following functions :

• Expertstohelpwiththeproductionofideasandconceptsintheintroductionphase

• Avarietyofequipmentfromlabscaletoindustrialscalefortestingandverificationofprocessesand technologies

• Productionlinestomanufacturenewproducsatindustrialscalebasedeitherontraditionaltechnologies or new technologies

• Flexibleunitsthatwillofferthepossibilitytotestnewequipmentandnewtechnologiesinreal( small scale ) production lines

• OfferDSStoolstocomparetechnologiesandmakeselections

• Mostupdatedinformationaboutnewtechnologies

• Modellingtoolsforoptimizationofequipmentandplanningoftrials

• Accesstolegislation

Description of technical parameters ( as applicable )There is a significant amount of processing equipment distributed among the existing research infrastructures. There are overlaps and there are also equipment deficiencies. Few infrastructures have the capacity to produce food products in sufficient amount or under required safety conditions to be tested by consumers.

The description of the technical details for this RI requires a decision about which nodes/instititions are willing to be commited so that a proper inventory of existing equipment and needs is done.

Although the lines will be flexible, it will be necessary to decide the specific focus of the RI. Either on products ( bakery line, meat processing, etc ) or on technologies ( heat treatment, non thermal treatment, freezing, etc.. ). may be a combination of both could be relevant.

applicable standardsThere is a large number of standards that regulate food manufacturing in terms food safety, packaging materials, health claims, etc.

Difficulties / limitationsEach of the above described technologies has advantages but also limitations. Some examples are :

Hpp is a semi continues process and equipment costs are still quite high. Possible colour/texture changes in raw meat products may occur. Specific packaging materials are needed resistant to high pressure. Products with high in air content such as bread cannot be processed. Food to be processed must contain water and not have internal air pockets like strawberry, marshmallow, nuts, etc. HPP is unable to destroy spores. Food needs packaging prior to processing and requires refrigerated storage unless the product is acidic. As a new processing technology with a limited market, pressure-processed products may cost 3 to 10 cents per pound more than thermally processed products.

peF is merely applicable to products with conductivity limitations below 0.1 or above 30 mS/cm, maximum particle size 20 mm, and carbonated products require back-pressure application. It is not applicable for sterilisation and does only limited inactivation of enzymes. Electrochemical reactions and electrode erosion does occur.

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Key limitations for cold plasma are the relatively early state of technology development, the variety and complexity of the necessary equipment, and the largely unexplored impacts of cold plasma treatment on the sensory and nutritional qualities of treated foods. Also, the antimicrobial modes of action for various cold plasma systems vary depending on the type of cold plasma generated. Optimization and scale up to commercial treatment levels require a more complete understanding of these chemical processes. Active research is ongoing to enhance efficacy.

microwave heating A major impediment to the success of microwave technology uptake in the food industry may be attributed to non-uniform heating ( safety problems and quality problems ), relative high equipment costs ; dificulties with process control and validation ( variability in heating time, end temperature ). However, some problems could be solved by a better communication between food technologists and microwave technology experts. It is important to note that microwaves do not work in the same manner as conventional heating. In order to ensure microwaves are utilized properly, one needs to understand that the food material as a whole plays a major role in how microwaves work.

Infrared heating has many advantantages but it has a limited use in food industry. The main reason has been the risk of breakage of the lamps and possible contanination with food. Nowaday lamps are make or protected with unbreakable glass. Another risk of use of infrared energy for the operator is related to occular hazards ( risk for effects on the eyelid, the cornea, the aqueous humour in the eye, the iris, the lens, the retina ) and skin hazards. The eye has some protective mechanisms, since IR is usually accompanied by an intense visible phenomenon.

ohmic heating is relatively sensitive to the fact that the conductivity of the food to be ohmically processed should be within an appropriate interval of values. For this reason, ohmic heating works well for some food products, while for other ones the recipes would need to be altered in order to adjust the conductivity towards an appropriate interval of values. This is not always possible. As a result, ohmic heating is not generally appropriate for all foods, but works well for selected foods with conductivity within a certain interval. Liquids are particularly suited to this process, even if they contain significant particulate material. However, the problem of adequate contact between the electrodes and a solid product remains a difficulty.

power ultrasound The usefulness of high-intensity ultrasound for modifying certain physical and chemical properties of foods has been realized for many years. Nevertheless, it was only very recently that manufacturers have begun to adapt laboratory-scale equipment for large-scale processing operations. The increasing use of high-intensity ultrasound depends largely on the availability of low cost instrumentation that is proven to have significant advantages over alternative technologies.

ultrasonic cutting, absorption heating can affect negatively soft food stuff ( slight softening on the edges being cut ). Sono-chemical effects which enable easier cutting may cause high efficient chemical reactions. Further limitations are possible temperature increase and off-flavour generation. This technology is not applicable for products with a high fat content ( initiation of chemical reactions such as hydroperoxide formation ), semi-liquid product, irregular shaped products and products with many particles of a solid nature. Temperature may increase on the cut surface leading to overheating or burn at the cut surface in case of poor temperature control at the blade.

SFe extraction uses high pressure conditions and the ( sometimes ) high temperatures, however, these risks are not worse compared to the risks associated with the traditional extraction technologies. With respect to economical/financial risks, SFE is often mistakenly associated with a technology that cannot compete with traditional extraction techniques. Although SFE has high investment costs, the costs of extraction are generally lower, due to the fact that no toxic waste has to be extracted out of the product.

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menbrane technology With the exception of widespread use in the dairy industry, there is little industrial experience in other food sectors. Consequently, industry is reluctant to become the launching processor for the new technology. Limitation with investment costs.

Capacity The capacity of the equipment will depend on the application. Possibilities from lab scale to industrial scale ( 200 kg/h to 1-5 ton/h ) will be available within the RI. The small scale pilot plant will have capacity to produce real food products with a capacity comparable with an industrial process.

Work /equipment / environment safety issuesTechnical trained personal will be necessary to run the equipment. For each piece of equipment a risk analysis has to be done since most of the equipment may involve high temperatures, pressures, or risk of cutting.

legal and technical approvalThe RI will have updated information about the legal and technical aspects of implementation of new technologies. Special attention to the Regulation ( EC ) 258/97 on novel foods and novel food ingredients. This legislation applies to foods and food ingredients to which a production process not currently used has been applied, and evaluates possible changes in nutritional value, metabolism and level of undesirable substances.

Hpp Regarding HPP, in January 14th 2008, EU published a proposal for the amendment of Regulation ( EC ) 258/97. The competent authorities of the member states agreed in 2001 that the national authorities should decide on the legal status of high pressure treated foods, as it was no longer considered to be a novel process.

peF There is no special European legislation on products treated with pulsed electric field processing technologies in the food industry but the general EC Regulation 258/97 [Regulation ( EC ) No 258/97 of the European Parliament and of the Council of 27 January 1997 concerning novel foods and novel food ingredients] concerns, which entered into force on 15 May 1997. It brought, before any sale, a permission marketing in the European Union for new foods and new ingredients. Products treated with pulsed electric field processing are considered as such. This approach is mandatory for all Member States.

Cold plasma From a European perspective, foods that are produced by novel means are subject to the Novel Food Regulation 258/97. However, if a treatment is considered rather than e.g. a new ingredient or functional compound it is sufficient to demonstrate substantial equivalence of the product under consideration.

The minimum requirements for packaging materials which are in contact with foods and act as a barrier to protect foods are regulated by guideline EC 1935/2004. Treatment by plasma gas may not alter the barrier or functional properties of the material to pose a risk.

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microwave heatingThe national legislations for countries in the EU follow the EMC directive ( CENELEC Publication ( 2009/2010 ) ). An interpretation of this directive is found in the EMC guide. All applications, except for applications related to commercial telecommunication services and information technology, are denoted as ISM ( Industrial, Scientific, and Medical ) which means the use of radio and microwave frequencies for industrial and domestic processing, in scientific research and in medicine [CENELEC Publication ( 2009/2010 )]. Only a limited number of ISM frequency bands are available for microwave processing, since the spectrum is almost exclusively pre-destinated for radio-communication. The ISM frequency which is commonly used for microwave processing in Europe is 2450 MHz. Another ISM frequency in the microwave area is 433.92 MHz ( in region 1 of the ITU, the International Telecommunication Union, including Europe ), though subject to local acceptance, and 5.8 GHz. The latter is though not yet commonly used. In region 2 of the ITU, which covers America, Greenland and some of the Eastern Pacific Islands, 915 MHz is also an ISM frequency. In the United Kingdom, and some other countries, the frequency 896 MHz is an ISM frequency. In Europe, the HF frequencies 13.56, 27.12 MHz and 40.68 MHz are also available ISM frequencies, which allow for HF food applications, e.g. tempering of meat blocks.

The IEC ( International Electrotechnical Committee ) provides a worldwide regulatory basis for access to the radio frequencies and to prevent interferences in more detail, in particular with the issue of EMC ( electromagnetic compatibility ). In Europe, the EMC directive ( 89/336 EC ( European Commission ) ) explains electromagnetic compatibility. This directive requires that all new manufactured goods placed on the market within the EU must comply with it. There are also international safety guidelines ( ANSII, IEEE, WHO, ICNIRP, etc. ). This framework provides a basis for national guidelines and standards for human exposure.

ohmic heating For in-container processing, the requirements are similar to that of traditional thermal processing in the US. For continuous flow processing with aseptic packaging, the approaches are currently in development in research projects ( e.g. in a project funded by the USDA National Integrated Food Safety Initiative ). Evaluation and monitoring of the safety of ohmically processed foods, falls under the Food and Drug Administration’s ( FDA’s ) purview, unless the product contains a specified minimum amount of meat and poultry. In such cases, USDA is responsible.

power ultrasound The process could be considered as novel, but, due to its dominant physical effect no significant changes in composition, nutrient value and content of undesired substances is expected for the products. Nevertheless, with regard to the Novel Food Regulation this would have to be evaluated product-specific. The formation of radicals by cavitation might result in undesired changes of product quality.

SFe extraction Supercritical CO

2 is biologically compatible, and has been given a generally regarded as safe ( GRAS )

designation by the FDA. The specifications for this rating can be found in 21CFR184.1983 -- Sec. 184.1983.

location This RI should be a series of nodes, virtually linked each other and widely distributed around Europe. It is suggested that only one node per country is established, but it is not excluded that one node may have connections/links to other locations in the same country. The distribution of technologies and specific production facilities in the different nodes should be decided taking in account the existing activities at each node and possibilities of future financing.

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estimated costs The RI element described will be an upgrading of existing RI elements/infrastructures. The calculation of costs can not be done at this stage as the need for investiment at each site is unclear as is the possible engagement of equipment suppliers.

Initial investments, in the order of magnitude of 30-50 M€, may be required to start up activities at some nodes. Running costs are not clear at this stage.

Human resources and training facilities

StaffSkilled human resources, knowledge and competences are required for all technologies described in this platform. It is expected to be required :

• Manager:

– To define a strategy

– To organise the activities between the different co-locations

– To marketing the activities

• ExpertsatPhDlevelorseniorresearchers:

– To create the project and plan the industrial trials

– To contribute with specific knowledge in the technologies above mentioned as well as in food science and technology such as food processing, food quality, food microbiology, rheology, food chemistry, etc

– To marketing the activities

• TechnicalStafftoruntheequipmentandperformtheindustrialtrials

Competence and servicesThe RI will offer a wide range of competences and services for food and manufacturing industry. More specifically :

– Process and production technology : streamlining, optimisation, manufacture such as :

– Production of ideas and concepts in the introduction phase

– Developing and manufacturing new production processes for a given product

– Developing manufacturing processes for new products

– Developing and testing new technologies

– Verifying new technologies

– Improvement throughout the whole product development process

– Optimising specific technologies

– Evaluation of equipment and technologies

– Sustainability evaluation for different technologies

– Testing new raw materials at the production site

• Acesstosimulationandoptimizationtools

– Product development : design and production – such as :

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– Developing and manufacturing low fat or sugar products

– Manufacturing processes for healthy foods

– Developing and manufacturing diet products targeting different diseases or issues such as cardiovascular, diabetic problems, lactose intolerance, favism, different allergens, etc.

– Developing and manufacturing food products for different cultural, ethnic or religion groups such as kosher, halal, vegetarian, etc.

• Developingandmanufacturingnutraceuticalsandfunctionalfoods

– Assignment – Problemsolving – Consultation

– Acute problems with production or testing and verifiering

– Advice on most suitable technologies for a process

• Environmetalimpactoftechnologies

training and technology transfer facilitiesThe RI will offer a wide range of possibilities for training and technology for food and manufacturing industry. More specifically :

The Infrastructure will offer excellent possibilities for Training and technology transfer both for food industry, Training : company-specific or open courses

• Coursesonnewtechnologies

• Coursesonprocessdevelopmentandoptimisation

• Coursesonsustainabilityevaluation

• Coursesonsimulationandoptimizationtools

Networks

• Accesstoworldwidenetworksforthepurchaseandsaleoftechnologyandtechnicalsolutions.

• Assistancewithcontactwithpublicauthoritiesandlesscomplexlegislationmatters.

• Anetworkofcontactswithnationalandinternationaluniversities,collegesandinstitutes

Information

• Technologymonitoring

• Updatingaboutnewtechnologies

• Updatingaboutthenutrientsandtheirroleinfood

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5.2. Hyperspectral and multispectral imaging

This document describes the technical specification “Hyperspectral and multispectral imaging” to be implemented in RI Platform of “Pilot size factories for developing, testing and training of new manufacturing solutions for the food processing industry”

Short description Spectroscopy has recently become increasingly important as a fast, non-destructive analytical technique in food science and technology.

Different methods can describe different material interactions.

• Absorption can be measured based on transmission or reflection. It is specific to chemical bonds. In NIR range the moisture content, the presence of O-H ( water, alcohol ) and C-H ( hydrocarbon, aromatic ) can be well detected.

• The emission is characteristic to the chemical compounds. If the emission spectrum is known, based on the energy of emitted photons the elements in the matter of unknown composition can be identified.

• The scattering of incident radiation mostly depends on the physical properties of the material ; x-rays or electrons enable inspection of scattering at high energy radiation ; and according to another phenomenon, the measurements are based on how the matter shifts the wavelength of the scattered radiation, like Raman or Compton scattering.

Ranges and properties concerning absorption spectroscopy :

• visible domain can provide information about several visible compounds like flavonoids ( anthocyanin ), carotenoids, quinones ( mushrooms ), pyrroles ( chlorophyll ), betanin ( beetroot ), melanin ( skin ), etc. with the advantage of quantitative description.

• infrared range ( NIR, MIR, FIR ) can serve the estimation of primary ingredients like moisture, sucrose, carbohydrates, fats ( oils ), proteins, etc. This range might be the most useful for applications in food processing.

• ultraviolet bands are used e.g. to detect defects, bacterias, fungicide infections, etc. One of the first rounds of applications of UV spectral imaging was to recognize fecal contaminations on poultries.

Hyperspectral and multispectral imaging combines conventional absorption spectroscopy with the capability of image processing of spatial information. As optical methods, these technologies offer also fast, non-destructive, remote sensing method and application opportunities for quality inspection, classification, and evaluation of a wide range of food and agricultural products. Due to rapid development of hardware and software, hyperspectral imaging is evolving from a research platform into a useful tool for many practical applications.

Based on the number of bands stored in wavelength domain, spectral imaging can be divided into two main techniques ; hyperspectral imaging ( HSI ) using hundreds of bands and multispectral imaging ( MSI ) processing only a few bands like traditional RGB imaging.

Hyperspectral imaging provides three-dimensional information of the analysed objective by different data collection techniques : point-scan, line-scan ( push-broom ) or area-scan ( tunable filter ) methods. It can serve us the spectral information by each pixel of the analysed surface. The spectral cross-section of acquired 3D data cube might serve also spatial information about the distribution of a given attribute.

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HSI has big potential in discrimination, classification and detecting defects applied in quality control for the food industry and it offers a new way for studying different tissues and inhomogeneous surface properties.

Direct on-line industrial implementation of a hyperspectral imaging system is not practical because of the hardware speed needed for rapid acquisition and analysis of a huge amount of data collected, but it can play a prominent role as a research tool to define significant wavelengths for each specific application.

In general, significant wavelengths are determined first for a particular application, then they are used by a multispectral system for fast and much cheaper image acquisition than used in hyperspectral imaging. Successful lab-scale applications have already been achieved ; however, more research and facilities would be necessary to extend their applicability and implementation in the industry as well.

multispectral imaging system can be used as online and real-time application performing measurements at the key wavelengths allocated by HSI. MSI works with a few discrete wavebands producing a set of isolated data points for each pixel. It enables rapid measurements on the key wavelengths. MSI can be used for decision making ( processing plants, packaging houses ), it can be implemented as online / real time industrial application.

The proper combination of both technologies can pave the way to development and application of modern “zero-defect and zero-waste manufacturing” principles in the food industry. As a fast and in-line defect detection and data gathering technology it can support the development of advanced resource management and quality control procedure, enabling continuous control of e.g. the defect rate as well as the efficiency of the resource usage in the production line.

Integration : For research purposes, a hyperspecral system is useful to work in several spectral ranges ( NIR, VIS, UV, … ), therefore the HSI system needs the support of

• absorptionspectrophotometer ( transmission/reflectance, multichannel ) for results comparison,

• multispectralsystem with several filters to be set according of the test efficiency of results,

• softwaredevelopmenttools and developers to improve algorithms,

• strong computer network to connect each elements, transfer huge amount of data,

• researchers need quick access to relevant scientific abstract and full-text resources, therefore building a local, method-specific full-text database is also recommended.

• existence of an available spectral database would also facilitate starting and following up new projects, assuming that there would be researchers who will be ready to upload sample spectra and even statistical results.

• finally, a proper accessibility of sources should be designed among method-centres.

Image acquisition combinations ( reflectance/fluorescence, reflectance/transmittance ) along with data fusion techniques are likely to expand building multitask food inspection systems in the future.

activities and value proposition

General value propositionMulti- and hyperspectral imaging offer quick, non-destructive and remote sensing method for quality inspection, classification, evaluation of a wide range of food and agricultural products using reflectance and UV spectral information which is invisible to the human eye. Based on the values of absorbance/reflectance or transmittance, concentration of the ingredients can be estimated.

Moreover, HSI-MSI is probably the only available technology that is able to provide accurate spatial ( location, shape, pattern - answering the question “where ?” ) and spectral ( compounds, ingredients -

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answering the question “what ?” ) information on the analysed product at the same time. Since most of the food and agricultural material has non-homogeneous structure, implementation of this technology can be particularly very important in the food sector.

If the MSI imaging system is properly set-up, it can support in-line data gathering, while significantly decreasing the identification time and supporting the application of short quality control loops.

Food manufacturing industryThe main beneficiary of this RI platform would be the food manufacturing industry, e.g. meat industry, bakery industries, fruits and vegetables, packaging industry, beverages, etc.

Multi-and hyperspectral imaging offer several way for analysis of physical, chemical and biological properties of a broad range of food and agricultural products. Just a few examples :

• Vegetable and Fruits ( apple, grape, walnut, cucumber, mushroom )

– Bruise detection

– Firmness evaluation

– Defect detection

– Ripeness /maturity detection

– Chilling injury detection

– Mushroom’s fungal infection, fungicide treatments

• Meat ( beef, chicken, pork, lamb )

– Tenderness evaluation of beef sirloin

– Marbling evaluation, PSE/DFD detection

– Bone fragments / foreign body detection

– Microbiological spoilage detection

• Grains ( corn and wheat kernel )

– Oil and oleic acid evaluation

– Aflatoxin detection

– Fusarium damage detection

– Monitoring the state of germination

• Beverages(milk,tea…)

– Milk : fat content evaluation, melamine detection

– Tea : quality classification, checking moisture content

• Dairyproducts,confectionaries

– Cheese : diffusion during maturation – distribution of fat content, study on the effect of enzimes

– Marzipan : diffusion of water, degradation of saccharose

The list of potential applications is continuously being improved.

The unique advantages of HSI-MSI method in food manufacturing industry that it enables :

• inspectionofchemicalcomponentsbyaspectrophotometer

• studyingnon-homogeneoussurface

• theeffectof3Dshapeduetounevenilluminationcanbeeliminatedbynormalizingthespectrapixel by pixel

• remotesensing:evenanobjectfromadistancecanberecognizedandinspectedattheproductionline

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• establishedMSIsystemismuchcheaperthanaspectrophotometer

• MSIisableoperateunderextremeindustrialconditions,e.g.incaseofhighmoisturecontent.

The main barriers towards a full scale industrial application of this technology currently include the development of advanced algorithm and software tools to ensure specific industrial application. This requires a non-negligible experimental and programming effort that many companies ( especially SMEs ) simply cannot afford.

HSI and MSI technologies at the state of the art are not “plug-and-produce” technologies ; if the user intent to acquire the technology, a relevant effort has to be invested before the technology can be reliably implemented in the plant as intended.

value proposition for the solution and service providing industry, fundamental research providerAlthough producers have recently invested in this direction and have provided preliminary solutions ( e.g. Specim’s SpecSensor SDK ), integrating different data coming from different HSI sensors in the plant and interacting with the HSI sensors is still a challenge in practice. The activities of this RI platform will have the goal of overcoming these limitations in order to promote the dissemination of this technology at plant level in the food industry.

Integration of existing research infrastructures Although, NIR hyperspectral sensors ( InGaAs, 900nm-1700nm ) have become available in Europe since 1999, only a few labs could build the necessary equipment listed at the end of section 2.

Some of them work slightly hidden selling devices and/or solutions to research institutes and industrial users. Few examples :

producers :

• Photonics(Xenics,Xeneth),Leuven,Belgium

• SPECIM,SpectralImagingLtd.,Oulu,Finland

Distributors, like :

• SphereOpticsGmbH,Hohenheim, Germany : application for mango sorting

The followings research institutes are publishing regularly in scientific journals or on international conferences :

Ireland :

• UniversityCollegeDublin,AgricultureandFoodScienceCentre,Belfield,Dublin, Ireland

• BiosystemsEngineering,SchoolofAgriculture,FoodScienceandVeterinaryMedicine,UniversityCollege Dublin, Belfield, Dublin, Ireland : application for meat, mushroom

Hungary :

• CorvinusUniversityofBudapest,FoodScienceFaculty,DepartmentofPhysicsandControl,Budapest, Hungary

• HungarianInstituteofAgriculturalEngineering,Gödöllő, Hungary : application for sirloin ageing, meat marbling, mushroom fungal, cheese, marzipan

Germany :

• LeibnizInstituteforAgriculturalEngineeringPotsdam-Bornim(ATB),Max-Eyth-Allee100,D-14469 potsdam, Germany

• JohannHeinrichvonThünen-Institut,FederalResearchInstituteforRuralAreas,ForestryandFisheries, Braunschweig, Germany : fruit, tomato, wheat, fusarium in maize

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Spain :

• InstitutoValencianodeInvestigacionesAgrarias(IVIA),CentrodeAgroingenierıa,Moncada( valencia ), Spain

• InstitutdeCiènciesdelMar(ICM-CSIC),Barcelona, Spain : citrus fruit, grape

Italy :

• CRA-INGAgriculturalEngineeringResearchUnitoftheAgricultureResearchCouncil,Monterotondo ( Rome ), Italy : fish

the netherlands :

• WageningenUR,Biometris,Wageningen, The Netherlands : tomato, tulip, polymers

Belgium :

• WalloonAgriculturalResearchCenter:proteininfeed(SWIR),ergotinwheat(NIR)

norway :

• NofimaMarinfoodresearchinstitute:fishfillet

Related organizations, events :

• CIGR : International Commission of Agricultural and Biosystems Engineering conferences every even years : 2012 :Spain, 2014 :China, 2016 :Denmark, 2018 :Turkey

• WG on Image analysis : 2009 : Potsdam, 2010 : Budapest, 2012 : Valencia, 2014 : Montreal

• EurAgEng : European Society of Agricultural Engineers, European member of CIGR

• conferences on every even years : 1984 :Cambridge,…2002 :Budapest, Leuven, Bonn, Crete 2010 :Clarmont,11 :Hannover,12 : Valencia,13 :Hannover,14 :Zurich,15 :Hannover,16 :Aarhus

– ICnIRS : International Council for Near Infrared Spectroscopy conferences every odd years : 1987 :England,…2009 :Thailand, 2011 :South Africa, 2013 :France, 2015 :Brazil, 2017 :Denmark

Device and solution distributors, research institutions and industrial partners

• The distributors might be glad with an extended market accessible on a homepage ( center, staff ) of domain. The test environments as parts of RI would be also useful for testing their devices and algorithms.

• The industrial partners will definitely benefit from collected information about subject and increased research capabilities.

• In case of research institutes, it is important that the RI should not be a concurrency but a supporter. They mostly cannot be aware of every field of the method because of limited staff, PhD students, etc. They cannot test the application due to the lack of industrial environment. They would need a center, a homepage for this discipline to connect them to the other partners, and provide access to full-text ( links ) and spectral databases. And so on.

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The following diagram contains some suggestions for future cooperation.

technical requirementsImaging-spectrograph-based line-scan systems are used as major hyperspectral imaging tools, but area-scan systems using electronically tunable filters are also useful for many applications. Transmittance measurement is usually performed using high-intensity light sources, when inspection of internal attributes is aimed. Light scattering technique provides another approach for internal quality evaluation.

In addition, combining different image acquisition methods ( e.g., reflectance/transmittance and reflectance/ fluorescence ) can provide more capabilities for the systems than those ones that use only single imaging mode.

Construction of the hyperspectral imaging systems, necessary constituents :

• Typesofcamera(FPA:2Dfocalplanearraysàranges): extended silicon CCD : UV : 250-500 nm ( fungal infection, bacteria, fecal contamination ) Silicon CCD : VIS 380-780 nm ( flavonoids, anthocyanin, chlorophyll,… ) InGaAs CCD : NIR : 900-1700 nm ( conventional HIS instruments ) cooled MCT : SWIR : 1000-2500 nm ( range of NIR spectrophotometers ) InSb : MWIR : 3-5 μm ( stronger absorption peaks )

• Wavelengthdispersivedevicecanbe imaging spectrographs tunable filter : AOTF : acousto-optic tuneable filter LCTF : liquid crystal tunable filter / LVTF : linear-variable tunable filter

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• Opticallensesfordifferentranges zoomable lenses in wide range for large objects ( to inspect watermelon ) a width macro or even microscopic capability for small objects ( to inspect structure of chocolate ? )

• Setupforinspectionofintermediatesizeobjects(inabiggerresolution)

• Y-tablepositionedbysteppingmotorfastorveryslow(forsmallobjects)

• Stabilizedilluminationsource light source : for line, disperse and transmission modes

Spectrophotometers for testing measurements on homogeneous materials

• foreachselectedspectralranges:UV,VISNIR,SWIR,MWIR(reflection/transmission)

Specific software needs :• Softwareforcalibrationofdevice,dataacquisition(C?)• Algorithmtopreparehypercube,algorithmfornormalization,segmentation(Matlab?)• Softwarefordataprocessingandanalysis,finalapplication(LabView,Ansys,Matlab?)

Multispectral systems for testing final application• PCcontrolledfilter-holder• filtersetsfordifferentwavelengthsandwidthsandopticsforeachselectedspectralranges:UV,

VIS, NIR, SWIR, MWIR• conveyorbelt,sensor,interveningdevices,etc.formodellingindustrialenvironment

Scope of applicationLine-scan spectral imaging systems that can acquire hundreds of lines per second have been developed for online food inspection, and they have great potential to become the standard for a variety of routine uses in food processing plants. Imaging spectrographs that can scan more than 1000 lines per second are already available on the market. New hardware design concepts will be continuously introduced to produce improved and novel components for building high-performance systems. The fast-growing computing capacity of the computers will facilitate handling of large data files and processing spectral images in real time. Advances in both spectral imaging instruments and spectral image analysis techniques will propel the development of hyperspectral/multispectral imaging technologies in the future.

According to the opportunities considering today’s reality, hyperspectral results can be used primarily in laboratories for quality control :

• Estimationofanyingredient(moisture,fat,oil,carbohydrates,proteins),

• detectionofinfections,diseases,contaminations,

• qualitydescription(meatmarbling,aging,PSE/DFD,etc.)

It is really valuable in every branch of the food sector as it is a fast, remote, non-destructive optical method.

On the base of hyperspectral measurements, the significant wavelengths applied by specific application can be identify and assigned. A multispectral imaging solution, operating on these wavelengths, will solve a specific task much cheaper and faster. This control solution is applicable for smaller size objects as well.

The biggest problems and limitations of the dissemination and the technology-transfer are the followings :

• thehyperspectralinstrumentsarestillnotcheapenoughtobeavailableforeveryone

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. the multispectral solutions should be studied and need specific development before industrial application and most of the listed research institutes ( universities ) are working in small groups ( teachers/researchers, PhD students ). Their capacity is quite limited and further financial motivation is lacking.

( Qin, J. et al, 2013 )

expected functionsThe basic benefit of the RI would be the opportunities of cooperation, networking and creating consortia for existing participants and stakeholders : device and solution distributors, research institutions and industrial partners.

• Thedistributorscouldextendtheirmarketviaacenter,ahomepageofthismethod.

• Theindustrialpartnerscouldfindcollectedinformationaboutthetopic,solutionsanddistributorsand other stakeholders.

• Theresearchinstituteswouldalsoneedcollecteddataofotherparticipants, access a scientific full-text link-collection and a spectral database. They could use the instrumental power of this and other RI platforms. They could find a stuff to develop their hyperspectral results ( scientific part ) into a multispectral method ( practical part ). They could test their fundamental results using the infrastructure of RI.

The main potential would be cooperation opportunities for all stakeholders.

The expected functions :

• inform industrial partners about the technology and application opportunities

• providewideinstrumental support and test environments for testing technology and methods

• research mainly on applicability but also on fundamental areas not covered

• software development for measurement control, data reduction ( segmentation ), data analysis ( so called calibration ), till final application

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• undertake each part of development process : – hyperspectral measurement ( by the help of wide instrumental background ) – segmentation ( part of data reduction ), data analysis ( so called calibration ) – software development for any purpose listed above – build final hyperspectral/multispectral setup

• customersurveysondemands

• cooperating with national centers and other partners in/over Europe

• monitoring EU tenders, collect and connect relevant partners to create consortia

• connecting stakeholders on a homepage, forum, periodical newspaper, work groups,

• full-textandspectraldatabase to help research

• find supporters of teaching, researching mobility, conferences, publications ( for research institutions, universities )

Description of technical parameters In section “Technical requirements” five hyperspectral imaging systems are described for five spectral ranges : UV, VIS, NIR, SWIR and MWIR. All of these might have

• own setup with Y-table

• own optics, spectrograph and sensor

• optics and adapter to measure 1. ) very small surfaces and 2. ) transmission as well

• own stabilized illumination units for 1. ) line- and 2. ) diffuse illumination and for 3. ) measuring transmission

• own accessories, like spectral calibration illumination units, bright standards

The hyperspectral measurements should be supported by spectrophotometers working on four ranges : UV, VIS, NIR and MIR

• all of them should have transmission adapters and fiber optic adapter as well

The multispectral system is for testing purposes. One setup could use different sensors and filters for different applications :

• PC controlled filter-adapter or stereo-viewing

• conveyor belt, PLC ( programmable logic controller ), intervening devices

Suggested software tools :

• C-Sharp for the development of controlling devices ( sensors, stepping motor,… )

• Matlabwithlibraries for data reduction ( normalize, ROI-s, segmentation ), …

• ENVI for manipulating hypercube

• LabView for accessing special devices

• Statistical software for building calibration ( PLS, PCA, ANN, … )

personal computers in powerful local network having internet access :

• controlling machines : having proper periphery

• data processing machines : having strong processor and big memory

• laptops for mobile data acquisition and for presentation

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• data server : having very big hard drive which is mirrored and saved periodically

• internet server : as a part of internet surface of whole RI surface

• secretary’s machine with printer, scanner, fax, …

environment :

• air-conditioned rooms ( 4+3+2 )

• climate chamber, drying chamber ( 2+2 )

• freezing and refrigerator boxes ( 3+5 )

• thermometer, humidity sensors and controllers, scales, support of analytical labs

Handy and flexible tools / instruments :

• portablespectrometers ( 4 ), photo camera to take RGB images ( 3 ), camera ( 1 )

• workshop/work-roomwith hundreds of handy tools

• mechanical support

location Hyperspectral imaging system can be used as a research tool in a research laboratory in unnoisy environment ( few diffuse lights, no oscillating fluorescent lamp ) under controlled air-conditions in order to perform stabile and reproducible measurements and provide necessary instructions for acquisition of specific spectral and spatial information.

Multispectral imaging system can be used and implemented as a real time / online application in production line in a pilot factory.

Research group would need :

• air-conditioned room for 4-5 HSI instrument, 4 spectrophotometer, portable spectrometers, table for preparing samples

• one room for climate- and drying chambers ( 2+2 ) and additional instruments ( scales, ... )

• one room for storage in freezers and refrigeration boxes or simply on shelves

• one bigger room for multispectral system and workshop of tools

• one for data processing with computers and printed sources

• room for secretary, organizing meetings, ...

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estimated costs Initial costs for investments for instruments, hardware, computing facilities, software development, etc. are estimated in the table below :

Description Cost per item [m€] total cost [m€]

HSI UV + VIS + NIR/SWIR + MIR 100 + 100 + 140 + 200 540

prof SPhM UV + VIS + NIR + MIR 30 + 30 + 60 + 100 220

MSI setup, PLC, peripheries ; 6 filters 6 + 20 26

hardware 10 ctrl., 10 proc., 5 note., 2 svr + 1 25*0.5 + 2*1 15

software VC, Matlab, ENVI, LabView, statistics, etc.

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environment 4 air conditions, 4 chambers, 3 + 5 fridges

1,5 + 3,3 + 1,2 6

others portable( 4 ), camera ( 3+1 ), sensors, tools

60 + 4 + 1 + 3 68

total 0.89 m €

(hsi:Photonics/Specim,spec:Büchi/Bruker/Foss,filter:Andover,portable:OceanOptics)

The annual running costs are estimated by :

Description Cost per item [m€] total cost [m€]

personal 6 engineer, 1 developer, 0.6 secretary , 1.4 superior, 2 students

11*( 12*2 )*2 530

admin 3

overhead electricity, maintenance, etc. 6

publication 6*IP ( á 500€ ) + 6*conferences ( á 3000€ )

3 + 18 21

innovation 220

total 0.78

These costs would cover all ranges like UV, VIS, NIR and MIR ( without LWIR ).

Human resources and training and technology transfer facilities

StaffThe following estimation would be a minimal configuration of the system. Multiplying the number of engineers and students could foster the work of the RI Platform covering the development of this research field :

• Headofengineers:HSI-MSIprofessional,contactperson

• Engineer1-3:UV-VIS-NIR-MIR:HSI/MSIprofessionals,performing,teachingandconsultancy

• Engineer4:chemistry,spectroscopy,chemometrics,multispectraldeveloper

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• Engineer5:imageprocessing,professionalmathematician,multispectraldeveloper

• Engineer6:electricalengineer,alsoastechnicianandformultispectralequipmentmaintenance

• Developer:softwaredevelopmentonanylanguages,localandinternetserveradministrator

• Secretary:withperfectEnglish,foreignlanguages,administrativeandorganizationalcapabilities

• Students:1scholarship(0.5)/yearfor4yearstohelptheirPhD=2salary:4student

Competence and services• DevelopmentandtestingfacilitiesforlargeindustrycompanyandforSMEsaswell

• Researchonpotentialindustrialapplicationsandmeasurementfacilitiesfortestingtheapplicabilityof the imaging systems

training and technology transfer facilities• Involvementofmediators-industrialimplementationandapplicationopportunitiestobe

disseminated by mediators

• TrainingsontheHSIandMSItechnologyandtheirapplicationopportunitiesinpilot-sizefactories

References :Gowen, A.A., O’Donnell, C.P., Cullen, P.J., Downey, G., Frias. J.M. ( 2007 ) Hyperspectral imaging – an emerging process analytical tool for food quality and safety control. Trends in Food Science & Technology ( 2007 ), doi : 10.1016/j.tifs.2007.06.001

Qin, J., Chao, K., Kim, M.S., Lu, R., Burks, T.F. ( 2013 ) : Hyperspectral and multispectral imaging for evaluating food safety and quality ( review ), Journal of Food Engineering 118 ( 2013 ) 157–171

Hyperspectral Imaging Spectroscopy, A look at Real-Life Applications, John R. Gilchrist, Gilden Photonics Ltd. ; Timo Hyvärinen, Spectral Imaging Ltd. http://www.photonics.com/Article.aspx?AID=25139

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5.3. Intelligent wireless sensor network

This document describes the technical specification ‘intelligent wireless sensor network’ of RI platform ‘Pilot size factories for developing, testing and training of new manufacturing solutions for the food processing industry’.

Short description A wireless sensor network ( WSN ) is a wireless network consisting of spatially distributed autonomous devices which use sensors to cooperatively monitor physical and/or environmental conditions ( e.g. temperature, sound, vibration, pressure, motion or pollutants ) at different locations. Each sensor node ( called also mote ) is composed of a microcontroller, transceiver, memory, power source and one or more sensors, either internal or external to the sensor board.

A sensor network is composed of a large number of sensor nodes, which are densely deployed either inside the phenomenon or very close to it. The position of sensor nodes need not be engineered or pre-determined : on the other hand, this also means that sensor network protocols and algorithms must possess self-organizing capabilities to react and arrange themselves according to needs and specific situation. This enables innovative covering of the measurements of an area.

Another unique feature of sensor networks is the cooperative effort of sensor nodes. Sensor nodes are fitted with an on-board processor. Instead of sending the raw data to the nodes responsible for the fusion, sensor nodes can use their processing abilities to locally carry out simple computations and transmit only the required and partially processed data, where and when needed.

Main differences between sensor networks and ad hoc networks are outlined below :

• thenumberofsensornodesinasensornetworkcanbeseveralordersofmagnitudehigherthanthe nodes in an ad hoc network ;

• sensornodesaredenselydeployed,andcapabletoreacttofailures;

• thetopologyofasensornetworkchangesveryfrequently;

• sensornodesmainlyusebroadcastcommunicationparadigmwhereasmostadhocnetworksarebased on point-to-point communications ;

• sensornodesarelimitedinpower,computationalcapacities,andmemory;

• sensornodesmaynothaveglobalidentification(ID)becauseofthelargeamountofoverheadandlarge number of sensors.

Since large number of sensor nodes is densely deployed, neighbour nodes may be very close to each other. Hence, multihop communication in sensor networks is expected to consume less power than the traditional single hop communication.

Wireless sensor networks are widely used in various areas. Applications based on this technology are being used in natural sciences like biology, meteorology, geology, medicine and monitoring purposes in everyday life ( energy consumption, industrial control, operation supervision, quality assurance, etc. ), especially when time is an essential factor. Generally the main WSN factor is the fast and effective reaction focusing on the unexpected events.

Application of WSN technology in food opens a number of possibilities, e.g. in terms of :

• pervasive process control – thanks to the ubiquitous nature of the WSN, there is the consequent possibility to cover and continuously monitor variables and properties ( e.g. temperature, humidity ) which should be kept consistent both in the production phases and in the transportation steps ;

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• product quality monitoring – by offering a distributed and multivariable view, WSN can help in detecting and therefore isolating areas of the production process where production conditions locally changes, reflecting in modified product features ;

• energy optimization along the whole production chain – the intensive use of energy in several production steps ( e.g. heating, cooking, cooling, refrigerating ), and the complexity of ( large scale ) plants devoted to such task, normally impose an overall control strategy where some parts and process phenomena are simplified ( as not known or measured ) and treated as constants. Therefore, by exploiting dense WSN architectures, some major aspects related to process control and energy optimization are could be properly considered.

• Production and the supply chain connection – the control can be extended for a whole way of the product from production to the final utilization involving the logistic and sales or market as well.

The technical specification ‘intelligent wireless sensor network’ of RI platform ‘Pilot size factories for developing, testing and training of new manufacturing solutions for the food processing industry’ will consider :

• applicationofintelligentwirelesssensornetworkalongfoodproductionlines,tacklingaspectsrelated to production monitoring and process control both at machine and at system level ;

• designofrobustcostsustainableWSN,calibratedonfoodoperatingenvironment,providingfaulttolerance, scalability, based on advanced sensor network topology, power-aware protocols and algorithms ;

• providedataacquisitionsensors,facilities,abletomeasureanumberofvariables/parameterscrucial for the food sector ( e.g. temperature, pressure, speed, shape, color ), etc., as well as related post process and computing facilities ;

• aspectsofinteroperabilityofdata,soasbuildaconsistentdatasetfordeployingadaptivecontrolsystems at machine and system level, capable to automatically re-configure the system and materials flows in order to adapt to changing requirements.

• facilitiesforcollectionoftheaccurateproductiondata,ofknowledgeondifferentfactorsandtheireffects, for the energy efficient management of the production lines through advanced model predictive control techniques, based on ubiquitous WSN.


Food manufacturing industry :The main beneficiary of this RI platform would be industry. Therefore

• Adaptivecontrolsystemsatmachineandsystemlevelthatisabletoautomaticallyre-configurethesystem and materials flows in order to adapt to changing requirements.

• dataacquisitionfacilities,sensors,abletomeasuretime,temperature,pressure,speed,shape,colour,etc., computing facilities

• facilitiesforcollectionoftheaccurateproductiondataandknowingallfactorsandtheireffects.

• alarmingrules

Solution and service providing industry :Other main actors are sensor producers and ICT sector.

• Developmentandproductionofintelligentwirelesssensornetworkalongtheproductionlines

• Designfunctionsfordataacquisitionfacilities,sensors,abletomeasuretime,temperature,pressure,speed, shape, colour, etc., computing facilities

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• Designsensorswithnewfunctionsforspecifictasks

• Designtheevaluationandreportingplatform

• Designthealarmingplatforms

R+D sector :

• knowledgetransfer

• designthedifferentfunctionscontrollingtheprocessparameters(interactionbetweenthedifferent process steps )

• designthecontrollingfunctionsformeasurementpoints(intelligentsensorboard)

The overall objective addresses the building of a complex multilayer architecture for the food processing industry, addressing wireless sensor network, intelligent mechanisms for data pre-processing / aggregation, and advanced facilities for data handling and energy efficient management of the production.

This will enable to control several environments or special places executing activities ranging from advance monitoring and alarming till efficient production management.

Enucleating the single strategic objectives ( SOs )will help in better identifying related gaps and challenges, both in terms of industry targeted RTD&I activities, and in terms of related benefits. Specifically :

So1 – WSn motes for food sector application• CONTEXT-Thesensorsarebatterypoweredembeddeddevicesdesignedwithregardto

miniaturization, low cost, durability, energy preservation and environment resistance ( outdoor installations ). They shall acquire evently or periodically the selected environmental parameters, pre-process them and create messages for the base station via sensor network without any cabling.

• GAP-Sensornodesarelimitedinenergy,computationalcapacitiesandmemory.Sensornodesare small-scale devices with volumes reducing in the near future ( foreseen approaching a cubic millimetre ). Furthermore, a specific attention shall been paid to their use in food production environment, characterized by extremely varying conditions ( as for combination of temperature, humidity, residual parts, etc. ).

• CHALLENGE–Identificationanddevelopmentofspecificallyfoodsectortargetedmotes,considering best compromise among dimensions, hardware constraints - in terms of microprocessors, bus and memory- production costs, communication capabilities and support to a wide range of sensors ( e.g. CO2, pressure, temperature, ( relative ) humidity, lightning condition, colour, counting/ presence or absence of certain kinds of objects, ( vehicular ) movement, mechanical stress levels on attached objects, frequency, speed, acceleration, etc. ).

• OUTPUTANDBENEFITS–Specificfamiliesofmotes,engineeredandcalibratedfortheuseinclassified conditions/ characterized environments, will represent the output of the activity, with related explicit indications for industry in terms of advised set-up conditions, best binding with sensors and possible communication channels.

So2 – WSn motes power supply mechanisms• CONTEXT-Theremustbesomekindofcompromisebetweenthemotecommunicationand

processing tasks in order to balance the duration of the WSN lifetime and the energy density of the storage element. In summary, limitation in the device size and energy supply typically means restricted amount of resources i.e. CPU performance, memory, wireless communication bandwidth used for data forwarding and range allowed.

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• GAP-Sensornodesareverylimitedintheamountofenergythatthestorageelementsuchas batteries can store. Hence the batteries with finite energy supply must be optimally used for both processing and communication tasks. Unlike traditional networks, where the focus is on maximizing channel throughput or minimizing node deployment, the major consideration in a sensor network is to extend the system lifetime as well as the system robustness. Sensor network protocols must focus primarily on power conservation. They must have inbuilt trade-off mechanisms that give the end user the option of prolonging network lifetime at the cost of lower throughput or higher transmission delay.

• CHALLENGE–Addressingpowersupplysavingmechanismsisacomplextask,sinceitssolutionlays in the intersection of three major aspects :

– aspect I : batteries. The wireless sensor node, being a micro-electronic device, can only be equipped with a limited power source ( <0.5 Ah, 1.2 V ). In some application scenarios, replenishment of power resources might be impossible. Sensor node lifetime, therefore, shows a strong dependence on battery lifetime. Research approaches toward rechargeable ( Li-Ion ) battery packs, single use batteries – e.g. lithium thionyl chloride

– aspect II : energy harvesting. Due to the inner nature of the food production processes, technologies for energy harvesting could provide numerous benefits to the end user, addressing basically three different available energy sources ( thermal energy, radiant energy - RF fields and RF waves- and mechanical energy - motion, strikes, vibrations, air flow ), so allowing to reduce the dependency on battery power and to provide sensing capabilities in hard-to-access environments on a continuous basis.

– aspect III : on the software stack governing the mote, the so called power, mobility, and task management planes are those layers devoted help the sensor nodes in coordinating the sensing task and lower the overall power consumption. The power management plane is designed to control the power usage of each node. For example, when the power level is low, the sensor node will broadcast to the neighbours telling that its remaining power is low and can only be reserved for sensing rather than participating in routing. The mobility management plane will detect and record the movement of sensor nodes to keep track of the route as well as the neighbours. By having the knowledge of neighbours, each sensor node in the network can balance power usage and task processing. The task management plane will schedule the sensing tasks and balance the work loads.

– aspect IV : solar energy if possibly, mainly in case of outdoor implementations

– aspect V : complex implementation with more type of sensors ( observers with low power needs only check continuously the environment and wake the intelligent measuring and processing devices slept generally if necessary )

• OUTPUTANDBENEFITS–Newsolutionsforpowersupplysavinginfoodmanufacturing,mixing-dependently on the accessibility of the environment- the use of long lasting batteries with energy harvesting mechanisms, and balancing overall use with internal ( tuneable ) software mechanisms. Cost targeting will be a major driver and supporting element in advising industry for the best technical but viable solution. Beside this, use of energy harvesting could provide numerous benefits to the industry, by reducing the dependency on battery power, related installation and maintenance costs, as well as the environmental impact.

So3 – WSn topologies, protocols and algorithms for food sector application• CONTEXT–WithinWSN,alargenumberofsensornodesaredenselydeployedeitherinsidethe

phenomenon or very close to it : their position need not be engineered or pre-determined. This means that sensor network protocols and algorithms must possess self-organizing capabilities. Another unique feature of sensor networks is the cooperative effort of sensor nodes. Sensor nodes

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are fitted with an on-board processor. Instead of sending the raw data to the nodes responsible for the fusion, sensor nodes could use their processing abilities to locally carry out simple computations and transmit only the required and partially processed data.

• GAP-Asensornetworkdesignisinfluencedbymanyfactors,whichshouldincludefaulttolerance,scalability, sensor network topology, beside hardware constraints vs transmission media vs and power consumption.

– Fault tolerance is the ability to sustain sensor network functionalities without any interruption due to sensor node failures. Fault tolerance addresses the fact that some sensor nodes may fail or be blocked due to lack of power, have physical damage or environmental interference. The failure of sensor nodes should not affect the overall task of the sensor network. Fault tolerance shall be carefully considered and addressed in food processing, especially for the environmental and physical interferences ( wall, tanks, etc. )

– Scalability - The number of sensor nodes deployed in studying a phenomenon may be in the order of hundreds or thousands. Depending on the application, the number may reach an extreme value of millions. The new schemes must be able to work with this number of nodes. They must also utilize the high density nature of the sensor networks. The density can range from few sensor nodes to few hundred sensor nodes in a region, which can be less than 10 m in diameter. Such concept need to be worked out for food production, when considering that the monitoring of certain ( property of a ) product should take place across several differently characterized steps and rooms : resulting scalability of WSN is crucial for that.

• Sensornetworktopology–behindthetopologyconcept,therearethespatialstrategyandorganizational policy according to which different areas of the WSN ( with detecting motes ) communicate with information collectors ( called sinks ). This aspect is closely coupled with previous ones – as it represents the physical solution satisfying such criteria - but also implies aspects related to multihop transmission media, and power-aware protocols and algorithms for sensor networks. The topology is in context with the scalability because implementing separated models the different parts can communicate vie other channels as well. So adding additional parts the system will be more extensive and effective.

• CHALLENGE–ChallengesforfoodmanufacturingWSNdesignresideinsolvingissueswhichcanbe conjugated in terms of :

Topology : several approaches to WSN topology design are available, ranging e.g. from :

– differentiated static topologies : motes are separated into two sub-networks, one dense, to monitor the selected territory, and another sparse, to serve primarily as a link between the first group and an internet gateway ;

– mobile and static mote topology : The inability of motes to transmit over large distances and their energy constraints implies that it is unfeasible to keep all the motes on-line at all times. To overcome this problem, the network contains two types of nodes : mobile and static. The concept is that a moving mote collects and stores information until the event “runs across” a static mote. The static mote triggers the communication capabilities of the moving mote and the latter establishes a connection and uploads the stored data.

– mobile robots that act as gateways into wireless sensor networks. Localization of nodes in a sensor network could take place by means of mobile robots. This approach tries to solve the problem of unifying a network that is separated because of disconnected groups of sensors ( clusters ). Of course, in all these cases robots are integrated parts of the sensor network, and could perform such job as second task with respect to a primary one assigned along the line.

Protocols and algorithms in dense WSNs - as in the case of food manufacturing - problems are related to flooding of the network as a consequence of broadcasting of ( same ) messages across

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nodes. This implies, beside traffic, continuous solicitation of RX and TX of motes, i.e. huge power consumption. To reduce such traffic, data-centric routing sensor protocols for information via negotiation ( e.g. SPIN ) could be exploited and specialised in food manufacturing WSN, as they address the deficiencies of classic flooding by negotiation and resource adaptation. This family of protocols are designed based on two basic ideas : sensor nodes operate more efficiently and conserve energy by sending data that describe the sensor data instead of sending the whole data, e.g., image, and sensor nodes must monitor the changes in their energy resources. Typically, before sending a data message, the sensor broadcasts an advice message containing a descriptor, i.e., meta-data, of the data. Neighbour interested in the data sends a request message, and data is sent to this neighbour sensor node. As a result, the sensor nodes in the entire sensor network, which are interested in the data, will get a copy.

• OUTPUTANDBENEFITS–efficientandeffectiveWSNdesigns,conjugatingandadhocdeployingsolutions derived from SO1 ( hw motes ) and SO2 ( power saving mechanisms ). Therefore food industry could rely on a range of customised sensors, which will be connected into intelligent wireless sensor networks to enable effective process control and data collection. These extended sensors infrastructure in the food processing and manufacturing equipment of pilot factories and of real companies could act as ubiquitous data collectors, making such data available to industrial world and the scientific community for performance improvement.

So4 – WSn based data gathering and energy efficient management of the production lines through advanced model predictive control techniques• CONTEXT–WSNmotesaredesignedtoacquireevently/asynchronouslyorperiodicallythe

selected environmental parameters, pre-process them and create messages for the base station via sensor network without any cabling. Density and traffic over the network determine an overall situation where a distributed and sparse set of measures – some of which are deriving from dedicated sensors having dedicated motes, some others not – need to be mapped in order to form a consistent and complete “image” of the food production process being considered.

• GAP–Today,consideringfoodsector,therearesomemajorgapstobefaced:

– WSN based data gathering mechanisms – the problem exists of collecting heterogeneous information from lower layers ( through the Internet ) and organizing them into consistent ( filtered and post processed ), time- and space-homogenous images of the process situation. This implies :

- Post processing mechanisms, to mediate among different detections of the same variable in a dense network, so as to reach to an affordable measure of a value ;

- Link between the WSN topology and the model of the food production process being under evaluation, no to lose the physical meaning of the detection.

– Information abstraction and collection mechanisms - independently from the specific situation under monitoring, WSN informational schemas should be considered, in terms of readability, handling and interoperability ( capability of aggregate and scalability ) of the retrieved data, in order to automatically built consistent pictures of the sensed phenomena.

– Pervasive information detection is a mandatory condition, but does not enable, if not supported by proper modelling techniques, appropriate control and supervision of the process.

• CHALLENGE–Atwotierapproachrepresentsachallengingsolutiontoaforementionedproblems:

– a first level facility for collection of the accurate production data, of knowledge on different factors and their effects – at this level, accurate WSN data are collected from production, and mixed with those coming from traditional sensors. Overall aim is to offer a central information system ( at supervisory level, i.e. at SCADA or MES level ) as hierarchical software module where aggregated storage in ( central ) database and full picture evaluation takes place.

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The realization of this approach implies the consistent connection with ( micro )controllers /PLCs along the production line, which could act both as sources of traditional information, or as gateways for WSN based info. Use of mechanisms such as OPC-UA as emerging standards, or encapsulation of nodes as IEC 61499 blocks, could pave the way to central software evaluation mechanisms where to assess the common behaviour of coherent data and the significant elements ( e.g. visualization of the known activities and events an interactive map ( where/what/happens, evaluation of known rules and creating alarm signals, intelligent analysis of behaviour based events to detect the abnormal situation on complex way ( when where what how happens )

– second level facility for the energy efficient management of the production lines through advanced model predictive control techniques – food processing industry represent a sector typically characterized by complex hybrid processes, where the term complex refers to MIMO ( Multi Input Multi Output ) systems, and hybrid addresses the coexistence of time continuous ( e.g. heating, cooling, cooking, etc. ) and discrete phenomena ( ON/OFF operations, transportation, etc. ). This is by far one of the most complex situations, which is today solved by strongly decoupling the MIMO systems, i.e. creating simpler parts by fixing some elements of the system and controlling the single parts of the systems in such constrained solutions. This typically occurs due to the fact that the complexity of the phenomena to be considered is too high, but also because of lack of properly fed control techniques. As a result, non-optimized use of the production lines, especially with reference energy, takes place.

The use of ubiquitous WSN and the pervasively derived information could provide the base for a better understanding of the dynamics of MIMO systems, which could in fact be properly identified by advanced model predictive control techniques. Such techniques could on one side benefit from direct data feeding as well as from historical information, so building and tuning fitting model for the whole hybrid processes, on the other generate, based on such models, the set points and the decisions - to be followed by line control systems- for an optimized and balanced use of energy along production lines, both at machine, cell and line level.

• OUTPUTANDBENEFITS–WSNrelatedinformationcouldbeextensivelyaddressedandprocessedso as to create the base for a better knowledge of the process. Overall image of the food production process would therefore be the base for a deeper knowledge and amelioration of the process and for its control, which would conjugate production performance with energy saving policies.

So5 – Data processing, visualization• CONTEXT–Oneofthegoalsistocapacitatethesourceinformationforflexibleprocessingtoget

more type of outputs if necessary to get more types of conclusions in behalf of the most useful utilization. Supporting the decision makers on different levels the data gathered via WSN devices must be processed on more user friendly way.

• GAP–Theeffectiveapplicablesensornetworkscontainmoretypeofsensorsinthefoodsectorthatprovide very large scale of parameters to be processed on a same way. The real big results can be based on the complex analysis using knowledge bases that contains timelined measure values and empirical rules.

• CHALLENGE–Itisnottrivialtodefinetheusefulkindsofvisualizationsolutionse.g.simultaneousviews ( showing the different aspects of same data collections ) ; dynamic, but simple coloring ; scalable graphs ; filters ; aggregates ; clustered data ; linked views ; dynamic and interactive screens ; entity extraction. In order to understandable regulation and feedback must be find out different kind of graphical user interfaces ( GUI ), which enable the experts, to get the relevant information in a huge pile of data easily and quickly. The proposed GUI types will be probably the following : dashboard, time scale graph, table view, reports, multidimensional cubes ( OLAP ), special visualization based on data mining.

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The possible steps of solutions :

Accumulating inputs – In the measurement network are placed the metering elements ( smart meters, intelligent production lines/buildings, parts of factories, etc. ) on the relevant points which are determined in production and the environment ( heating, cooling, gas, hot water, electricity, lighting, CO/CO2, ventilation, etc. ). The procedure receives, stores temporarily and transmits the messages coming from the neighborhoods periodically.

Integrating data – There are more type of inputs depending on the applicable technology, on the selected environments, on the number of data/information sources ( sensors ), on habits of manufacturers/energy consumers. The procedure makes the standards for the advanced processing.

Monitoring production values – For the monitoring it is necessary to store the collected data in database and to complete them with additional information about measurement network and locations. The procedure allows the maintenance of the data to describe the measurement network configuration and displays the measured data together with additional information.

Alerting, alarming – Warnings based on predefined rules/ limits and standard values or on behavior of data.

Intervening – Feedbacks directly for the production procedures based simple or complex measurement mechanism

Recording of the financial and technical data – Historical cost, production, energy and technology characteristics, and journals of use ( locations, equipment ).

Production rationalizing – Complex calculations and plans from the measurements which support the suggestions for the production changes including specific indicators and components calculated at smaller units.

Analysing, forecasting, data mining – Complex processes with specific algorithm and methods using with external non-formal information and knowledge base or internal artificial intelligence issuing statistical analysis and audit report with suggestions if necessary.

• OUTPUTANDBENEFITS – complex and objective report based on measured information made by experts – simple and complex set of suggestions for decision making and regulation – information for human and automatic decisions – additional sector specific results for other industry, systems, devices, institutes

Integration of existing research infrastructuresPossible existing research infrastructures participating are :

• http://www.senslab.info/ Very large scale open wireless sensor network testbed, aggregating :

Institut National de Recherche en Informatique et en Automatique ( INRIA ) ( Project Leader )

– Dynamic Network( INRIA D-NET Team ) – Coordinator

– As Scalable As Possible ( INRIA ASAP Project )

– Portable Objects Proved to be Safe ( INRIA POPS Project )

University of Strasbourg – Computer Science Laboratory ( LSIIT )

Thales Communications France ( TCF )

University Pierre & Marie Curie / Laboratoire d’informatique de Paris 6 ( LIP6 )

The SensLAB project deploys a very large scale open wireless sensor network platform. SensLAB’s main and most important goal is to offer an accurate and efficient scientific tool to help in the design, development, tuning, and experimentation of real large-scale sensor network applications.

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SenslaB is a group of 1K sensor nodes available as a testbed for distributed embedding sensor network application and distributed systems research. Distributed systems based on networked sensors and actuators with embedded computation capabilities allow for an instrumentation of the physical world at an unprecedented scale and density, thus enabling a new generation of monitoring and control applications. The SensLAB project was started in 2008. As of June 2009, SensLAB was composed of 1024 nodes at 4 sites.

RWtH-aachen university, Germany ( http ://www.comsys.rwth-aachen.de )The vision of the research is the Design, Analysis, and Engineering of Flexible, Scalable, Reliable and Secure Communication Systems and the adequate Architectures, Paradigms, Algorithms, Models, Methods and Tools to realize this vision.

The scope of considered systems spans from complex and massively distributed Peer-to-Peer-systems, via traditional Internet-based communication systems to ubiquitous devices, embedded systems and highly integrated microsystems and sensor nodes.

With Flexibility, Scalability and Resilience as key challenges in mind, three important research areas and mainly focus on them are :

• Protocol-andCommunicationSystems-Engineering:

– Engineering of Resilient and Flexible Communication Systems

– Structured Engineering of Protocols

– Models, Methods and Tools for Protocol and Systems Development

Verification and Validation of Protocols and Communication Systems

• Self-OrganizationandCoordinationin(Massively)DistributedSystems:

– Scalability and Resilience in Massively Distributed Systems

– Structured Peer-to-Peer-Systems, Distributed Hash-Tables ( DHTs )

– Self-Organization in Massively Distributed Systems

– Load-Balancing and Resilience in Structured P2P-Systems

– Security, Trust and Anonymity in Massively Distributed Systems

– Infrastructure Services in/for Massively Distributed Systems

– Mobility Issues in Massively Distributed Systems

• CommunicationSupportfor(Massively)DistributedSystems:

– Flexible and Adaptable Network Architectures

– Energy Aware and Efficient Communication ( Mechanisms )

– Value Added Services for Tomorrow’s Networks

– Mobility Support

– Cooperatively Driven and Operated Networks

university of Surrey, uK ( http ://www.surrey.ac.uk/ccsr/index.htm )The Wireless Sensor Network Research Lab hosts a state-of-the art experimental research facility for WS&AN. The test-bed facility is used for the prototyping and evaluation of developed protocol solutions and serves as a basis for the development of novel mobile context aware services and applications.

The test bed consists of wireless sensor and actuator nodes that can be organized in different network topologies and individually configured for various experiments and uses the backbone infrastructure of the WNT ( Wireless Network Testbed ). The test bed facility also includes servers hosting an IMS service platform and laptops, servers and mobile devices some of them serving as mobile gateways

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devices, some of them used for protocol and application development and execution of context-aware application and services. In the following an overview of test bed components is provided.

WS&AN related equipment

• 70WS&Anodes-SensiNodeMicro.2420(MSP430+CC2420platform)withan802.15.4radio

• 5WS&Anodeswithverysmallformfactor-SensiNodeNano.2430(CC2430platform)withan802.15.4 radio

• 15prototypingboardsfordigitalsensors-SensiNodeMicro.Proto

• 5dataacquistionboardsforanaloguesensorswithPSoC-SensiNodeMicro.4

• 10USBboards-SensiNodeMicro.USB

• 3USB802.15.4dongles–SensiNodeNano.USB

• 2bodysensornetworks:basedontheMicro.4withcustomsignalconditioningboardsprovidingphysiological sensors for ECG and heart rate, galvanic skin response, skin temperature, breathing rate and acceleration information

• 1bodysensornetworkdev-kit–basedontheImperialCollegeBSNnodes

• VariousdigitalandanaloguesensorprobesandsensorIOboards

netherlands organisation for applied Scientific Research tno, the netherlands ( http ://www.tno.nl )TNO has been working for some years on a system design for sensor networks. The design of such a system is based on high-fidelity simulations of communication-channel protocols and communication protocols, and on hands-on experience with communication technologies and signal-processing technologies. Within this research context, TNO has designed systems that are based on various low-capacity communication protocols such as IEEE 802.15.4 ( low-cost WPAN ), ZigBee and 802.11. These systems are highly usable for the ISA100 project.

advanced knowledge of sensor networks

The systematic design process for sensor networks brings together knowledge of architectures, protocols, robustness and routing. In recent years, TNO has acquired extensive knowledge and experience in system design for sensor networks, particularly in the field of :

• simulationofcommunicationprotocols:802.15.4,802.11

• networkandMACprotocolsforuseinsensornetworks

• advisingclientsandpartnersonsystemarchitectureandtheimplicationsofchosencommunicationtechnologies, particularly with regard to robustness

vtt Finland ( http ://www.vtt.fi )VTT develops wireless sensor networks and embedded devices for industrial condition monitoring systems, for infrastructure and environmental monitoring, hybrid positioning, sports measurements, wellness and healthcare applications and for emergency operations. VTT has a long experience on sensor network research and development. Development process combines our knowledge in short and long range communication methods and embedded systems including data analysis methods.

Challenges

Development of wireless sensor network technologies enables new distributed high performance applications. In research our challenges are set by the following :

– QoS-requirements, real-time response, restricted wireless bandwidth

– multi-hop synchronization

– reliable data encryption

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– multipath-fading, interference of other radio systems

– radio based localization

– energy efficiency and energy autonomy of wireless devices.

CnR – ItIa Istituto di tecnologie Industriali e automazione ( http ://www.itia.cnr.it/ )ITIA continuously invests in innovative factories as a mean to implement and test the research results, to cooperate with industries in order to solve real production challenges and, finally, for training by utilizing the factory itself as a real laboratory. These aims are pursued through the development of physical factories whose characteristics embrace major innovative aspects, particularly on automation and control systems, and robotics.

A pilot plant for the de-manufacturing has been recently developed where innovative intelligent, flexible, automatic manufacturing solutions have been addressed. Research activity focused on the development of modular low cost mechatronics solutions – both at hardware and at control software level - exploiting optimal human-machine cooperation, capable of processing heterogeneous products with a limited hardware and software reconfiguration effort, while self-adapting to the context variations.

Data gathering was addressed, considering modular scalable approaches both in terms of information organization and protocols for connection with field and with control systems.

High expertise on overall system modeling, as well as on identification and advanced adaptive control technique has matured, considering applications in various industrial fields such as automotive, packaging, woodworking, foundry etc.

As an upgrading of existing infrastructures, a testbed - capable of emulating a set of specific typical environment/ conditions of food production - should be addressed, so as to test and validate food targeted WSN solutions by operating them into. This infrastructure can be foreseen to complete the spectrum of existing activities orienting WSN toward food sector. Building of this testbed will be dependent on size of addressed phenomena ( heating/cooling chambers, dust environment, mixing chambers ), which also affects possible topologies and density of WSNs.

Campden BRI – Seacon europe - Innoskart It Cluster ( http ://www.campden.hu/eng/index.php, http ://www.seacon.hu/index.php ?lang=en, http ://www.innoskart.eu/en/news )The main pillars of Seacon’s operation are the solid professional competence, partner network established over years, state-of-the-art IT background and market-leading software technologies. The high capital stock – which is rare in the SME sector – and implementation of several innovative projects reflect the owners’ commitment. Since 2006 the company is an active member of the Innoskart IT Cluster ( 25+ members ), and leaning on the cluster Seacon constantly improves its competences, economic and technological background. Expanding professional resources it cooperates with more universities and technical colleges.

In the last years Seacon performed more projects ( with partners or in consortium ) on different industrial area ( agriculture, energy sector, industrial production, technical environment, etc. ) based on WSN technologies and data processing :

• SeaForest is a unique safeguarding solution for forestry. It combines innovative IT solutions with specialized forestry knowledge. The system identifies motor vehicles, electrical machinery and engines by detecting and analyzing their physical characteristics. In case of unauthorized access the system alerts the predefined persons immediately preventing the timber theft.

• Seavine is an intelligent observation, safeguarding and support system in winery that provides support in each phases of wine making. It manages collection, storage and analysis of relevant data

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regarding air humidity, soil and leaf wetness, wind and air temperature. Furthermore, in case of necessity it signals the identified threats.

• Increasingefficiencyofenergyuse–andthedecisionmakingininvestmentanddevelopmentprojects regarding energy consumption - can be supported efficiently only by objective information based on measurements. SeaERA application - via wireless sensor network - provides valuable support in the matter of energy consumption rationalization and cost reduction for building operations professionals and energy auditors.

Cooperating with Campden BRI Seacon is going to realize projects exploiting the Campden’s food industry experience and own knowledge in WSN technology and data processing/visualization, data based regulation. Some members of the Innoskart cluster can be involved on WSN planning and manufacturing area if necessary.

technical requirements

Scope of applicationThe intelligent wireless sensor network will be applicable to :

• process and product quality monitoring systems : intelligent wireless sensor network along the production lines will be capable to measure several variables – both at process and/or at machine level – such as e.g. temperature, pressure, speed, shape, color, etc., in order to feed dedicated monitoring systems ;

• adaptive control and automation systems at machine and system level, so as to complete intelligent control schemas able to automatically re-configure the system in order to adapt to changing requirements, as well as to perform energy optimized control policies ;

• both specific food processing “continuous” operations ( e.g. mixing, conditioning, cooling, heating ) and discretized/batch/transport operations. No particular limitations shall be foreseen in terms of application on process, excluding immersive conditions, e.g. full contact with organic material, preventing WSN mote to run, due to absence of transmission media. Chemically aggressive operating conditions should be checked and considered case by case.

• extended sensors infrastructure in the manufacturing equipment of pilot factories and of real companies ( so in combination with traditional sensing technologies ) to collect data for industry and to make them available to the scientific community for performance improvement.

expected functions

Clustered functions

With reference to following specific targets, WSN should provide :

Food processing equipment and their assemblies, research installations, pilot plants, sensors, instruments for food processing research :

• Designanddeploymentofextendedsensorsinfrastructure,incorporatingWSN,inthefoodprocessing equipment of pilot factories and of real companies accepting to collect data and make them available to the scientific community for performance improvement.

• IndustrializedspecificsetofprocesstargetingsolutionsintermsofWSmotes,dedicateddesigns,protocols and algorithms for sensor operation and mote power supply saving strategy.

Equipment and their assemblies, research installations, pilot plants, sensors, instruments for manufacturing research including robotics and automation + compliance to hygienic food handling requirements :

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• Designanddeploymentofextendedsensorsinfrastructure,incorporatingWSN,inthemanufacturing equipment of pilot factories and of real companies accepting to collect data and make them available to the scientific community for performance improvement.

• IndustrializedspecificsetofequipmenttargetingsolutionsintermsofWSmotes,dedicateddesigns,protocols and algorithms for sensor operation and mote power supply saving strategy.

Data banks, collections, data facilities :

• Databasesofinformationcollected,processedandcategorized,derivedbydistributedwirelesssensor infrastructures.

• Central(common)datastorageforfurthercomplexprocessing

• Knowledgebaseavailable

E-infrastructure enabling operation of networks, process control, data bases, computing facilities, collection, screening and structuring information

• Web-basedaccessofdatacollectedbysensorsofdifferentpilotplants,aswellastopilotwirelesssensor network implementations.

• Integratedapplicationmethodsincollaborativeoperativeenvironment

Expert systems

• Decisionsupportsystemsbasedondatacollectedbysensors

Specific single functions, common to aforementioned

WSN should provide :

• Abilitytobeinstalledin-line/maintainedon-line

• Abilitytobeinstalledaloneand/orintegratedincablebasedenvironment

• Ultralowpoweroperation,eventuallytuneablefromoutside

• Abilitytoproviderealtimesignalsandtoserverealtimeprocesscontrol

• Robustnessandresistancetotemperatureandmajor“harmingfactors”(e.g.humidity,acceleration)

• Selfre-configurability

• Abilitytosupportsensorstomeasurewiderangeofparameters/propertiessuchas:

– CO2,

– pressure,

– temperature,

– ( relative ) humidity

– lightning condition

– colour

– counting/ presence or absence of certain kinds of objects

– ( vehicular ) movement

– mechanical stress levels on attached objects

– frequency

– speed

– acceleration

– magnetism

– voltage, power

– etc.

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Description of technical parameters• Power : The wireless sensor node, being a micro-electronic device, can only be equipped with

a limited power source ( <0.5 Ah, 1.2 V ). In some application scenarios, replenishment of power resources might be impossible. Sensor node lifetime, therefore, shows a strong dependence on battery lifetime. Nevertheless, in a multihop ad hoc sensor network, each node plays the dual role of data originator and data router.

• Radio chip power consumption typically required ranges

– Sleep : tens μ A

– Idle/Rx : tens μ A-mA

– Tx : tens mA

• Therefore desired values are in terms of mW power draw / battery life with ( some ) ten years duration in sleep mode ( wake up as they need to transmit and receive data ).

• Power related aspects will consider both :

– single use batteries – e.g. lithium thionyl chloride

– energy harvesting supplying rechargeable batteries

• Size / overall dimension : easy installation in different food technology ( into equipment and/or raw material ) - As for dimensions, practical WSN nodes, or “motes”, currently range in size from disc-shaped boards having diameters less than 1cm to enclosed systems with typical dimensions less than 5cm square.

• Weight ( No battery ) : 5 – 30 g

• Technical specs :

– Bus>=32bit

– Clock>=300Mhz

– Memory :

- RAM>=1M

- Flash>=4M

– nesC – Java compatibile

• Cost : tens of Euros

• Efficiency range : dynamically adjustable from 1 m to 2 km outdoor and 4 floors indoor range ( communication with conventional wireless technology, e.g. WiFi, Bluetooth, ZigBee ) through concrete and water

• Operational conditions : flexible sensors which can be applied at different environmental conditions ( humidity, size, movement, temperature, etc. )

• easy and quick installation

• highly scalable

• Data rate : dynamically adjustable normal operational range from 2.4 kbps to 57.6 kbps ( max 500 kpbs )

• Frequency : 433.92 MHz ( available worldwide )

• Signal propagation : penetrates/walkthrough walls, concrete, water

• latency : few milliseconds

• real time response : sec

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• P2P messaging : yes

• multi-hop support : yes

• IPv4/IPv6 support : on gateway

• security : 128-bit AES, pre-shared key

Other key features :

• Self-organizing – WSN solution provides simple installation : the nodes just have to be placed and they can first discover each other, and then form a network with optimal communication routes.

• automatic error correction – The nodes periodically monitor their neighbours availability. If a node is unreachable they automatically rearrange communication routes. Combination of cooperative strategies for automatic correction is desired by means of new policies in power management plane, mobility management plane, and task management plane.

• multihop data transmission – To propagate information to the center database, sensor network does not require equipping all nodes with GPRS modems. Instead, the nodes can send the data via each other to a local center. Innovative approaches such as data-centric routing ( e.g. adaptive sensor protocols for information via negotiation ) can significantly reduce costs.

• Security – The security of the network communication should be achieved ( e.g. 128-bit AES, pre-shared key )

• Focusing on the data transport and storage the WSN sensors can be able to store the data temporarily if the communication is down/rearranging ( SPIN protocol ), showing - as WSN - fault tolerance and reliability properties for transporting data inside the network ( toward WSN sink ) and outside the network ( to target processing computer/database ).

applicable standards• IEEE 802.15.4( a )

• WirelessHART, Hart Communication Foundation

• ISA 100.11a

• Potential ANSI specification Wireless Systems for Automation

Difficulties / limitationsMain technical difficulties/limitations are related to identification of robust WSN mote, both in terms of absolute performance ( nominal operating conditions vs required performance in food sector ) and in food production environment ( differentiated pressure, temperature humidity ).

Risk mitigation actions could consider both :

• Differentiated set of motes in correspondence to particular identified food production conditions, to have food segment verticalized solutions

• Differentiated strategies in terms of mote power supply and data gathering mechanisms ( e.g. WSN static nodes with mobile sinks )

• Small ( portable ) sets to reproduce food production conditions locally ( so as to pre test applications ).

legal and technical approvalLegal requirements and required hygienic design standards are those addressing food manufacturing plants, as for food hygiene requirements, food contact area, materials of construction, cleanability, etc.

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location Location could be determined by considering availability of basic infrastructures reproducing addressed phenomena ( heating/cooling chambers, dust environment ) which could be oriented and completed ad hoc with WSN, to physically test things in real/adverse operating conditions.

estimated costsAs an upgrading of existing infrastructures, a testbed - capable of emulating WSN operating in a set of specific typical conditions of food production - can be foreseen to complete the spectrum of existing activities orienting them toward food sector. Building of this testbed will cause initial and running costs. Indication about expected costs is dependent on size of addressed phenomena ( heating/cooling chambers, dust environment ), which also affects density of WSN.

2-5 MEuro can be considered as an estimated cost range.


StaffTeam competencies covering the areas of communications ( protocols, routing, coding, error correction etc. ), electronics ( energy efficiency, miniaturization ) and control ( networked control system, theory and applications ).

Knowledge development on building of sensor networks, IT, food technology, food rheology

• Personnel with technical-economical competences capable to assess ( in advance ) the business impact of advanced sensor networks

• Team composition : engineers, technicians, specialists in food inspection

Competence and services• Ad hoc WSN solution development/consultancy for specific food production conditions

• Access to data and information collected by sensors infrastructure by external researchers

• Process improvement based on data collected by the distributed sensors infrastructure

training and technology transfer facilities• Training facilities on advanced sensors networks, based on food oriented test-bed facility :

– used for the prototyping and evaluation of developed protocol solutions and

– serving as a basis for the development of novel mobile context aware services and applications,

• supported by e-learning and virtual reality tools, targeting both internal training and industrial personnel.

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5.4. enhanced traceability and Radio-frequency identification

enhanced traceability and Radio-frequency identification ( RFID )

Short description Food safety and traceability presents today and will present in the future many challenges. The unique identification of each product or each production lot and the continuous monitoring of key environmental variables, especially temperature, along logistics processes can be achieved with RFID technology and some examples can already be found in different countries. This RI platform goal is to promote the development of new, more affordable and integrated solutions for the food supply chain, from farm to fork, to allow the wide exploitation of the benefits of such technologies in key sectors and products.

activities and value proposition The RI platform will :

• identifythekeyresearchareaskeyforthediffusionofRFIDsolutionsinthefoodindustry

• promotetherealizationofresearchanddemonstrationprojects

• promotethedisseminationandwidespreadoftheavailablesolutions,successstoriesanddemonstrators.

• ThemainresearchdomainstargetedbythisRIinclude:

• newSWsolutionsusingRFID,includingcloudbased,tosupportthefoodtraceabilityandsafetyalong the supply network

• newlowcostRIFDtagsandtechnologiesforspecificfoodapplications

• newbusinessscenariosandarchitecturesinthefooddomain

What is the benefit ( value proposition ) for the user of this RI platform ? RFID technology and solutions are key enablers to support the traceability of products along the supply network, buy providing reliable unique identification of products or production lots. These technologies can also support the monitoring of the range of variation of environmental variables key to food preservation. Current examples are the identification of situations where the product temperature reached higher thresholds than allowed for food safety ( for frozen or fresh products ) and also situations where the temperature has reached levels below the allowed ( for fresh products ).

Such solutions in general can provide business benefits such as assurance of the temperature range during transportation and warehousing, automation and control of logistic processes, product life control ( for perishables ), reduction of expired products, assurance that the right product is in the right place at the right time, increased supply chain speed, increased accuracy, increased visibility, etc. In general RFID solutions can contribute to food security and traceability, increased efficiency, quality and profitability.

How does the platform close the gap identified ?The barrier for wider implementation of these technologies is still the costs involved and the lack of sufficient business cases that prove its business benefits. Further research is needed to allow new approaches and new solutions that allow the achievement of the identified benefits with higher return of the investment for a wider range of products. This involves the development of lower cost

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components, new and optimized system architectures and solutions and naturally the wide diffusion of such solutions, enabling higher volumes and as a consequence lower unit costs. The definition, selection and/or adoption of standards is also a key prerequisite.

Integration of existing research infrastructures Each RI platform will be a sub-network of existing pilot plants and pilot size factories that are in close contact with research institution dealing with this issue.

This RI platform can benefit from the results achieved and from the interaction with the ongoing Future Internet initiatives, especially the FI-WARE Test bed of Generic Enablers and Open Innovation Lab in the Technical Chapters Internet of Things ( IoT ) Services Enablement and Security.

technical requirements for new RI elementsIn the previous chapters we have analyzed what the new RI should deliver, which existing RI might contribute to this RI platform and which RI elements are missing. This chapter will describe the physical and technical specifications and requirements for new RI elements needed. It includes the description of the expected functions and the technical futures and the estimated costs if applicable.

Scope of applicationRFID applications are relevant and can cover the complete food chain. In fact, the benefits and the return on the investment of RFID solutions are maximized when its application covers as much as possible of the supply chain, because the investment can benefit several actors of the supply chain and different improvements can be achieved.

expected functionsAs mentioned earlier, the goal is to promote the development of new and cost efficient solutions to support food traceability and monitoring of variables key to the food safety.

Description of technical parametersThe main requirement is the low cost of RFID tags and readers, and the easy integration with existing ICT systems. These are critical requirements because normally the deployment of these systems require a large number of such items.

legal and technical approvalRegulations on the privacy of buyers apply that at least for some products require the RFID tags to be destroyed after the product is sold.

location Because of the nature of this RI it can be distributed over Europe. Pilots that cover the complete supply chain will include several EU countries and in some cases even the implications in other countries should be considered ( either suppliers or end customers of food products ). The research and development activities can also be distributed over Europe.

estimated costs The costs will be dependent on the number and scope of activities and pilot plants to be implemented and in total difficult to estimate.

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StaffIn the domain of RFID tags and readers strong competencies in electronics and micro-electronics are needed. In the application domain strong competencies on ICT, including cloud based systems and interoperability, are needed.

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5.5. advanced membrane technologies

Short description Membrane technology is a generic term for a number of different, very characteristic separation processes. These processes are of the same kind, because in each of them a membrane is used. Membranes are used more and more often for the creation of process water from groundwater, surface water or wastewater. The membrane separation process is based on the presence of semi permeable membranes. The principle is quite simple : the membrane acts as a very specific filter that will let water flow through, while it catches suspended solids and other substances. There are various methods to enable substances to penetrate a membrane : examples of these methods ( driving force ) are the applications of high pressure, the maintenance of a concentration gradient on both sides of the membrane and the introduction of an electric potential. Membranes occupy through a selective separation wall : certain substances can pass through the membrane, while other substances are caught.

Membrane filtration processes are classified according to the membrane pore sizes, which dictate the size of the particles they are able to retain : membrane filtration can be divided up between micro and ultrafiltration on the one hand and nanofiltration and Reverse Osmosis ( RO ) on the other hand. When membrane filtration is used for the removal of larger particles, micro filtration and ultrafiltration are applied. Because of the open character of the membranes the productivity is high while the pressure differences are low. When salts need to be removed from water, nanofiltration and Reverse Osmosis are applied. Nano filtration and RO membranes do not work according to the principle of pores : separation takes place by diffusion through the membrane. The pressure that is required to perform nanofiltration and Reverse Osmosis is much higher than the pressure required for micro and ultrafiltration, while productivity is much lower.

The membranes are made from materials such as thin organic polymer films, metals or ceramics, depending on the application. They are manufactured in different forms such as hollow fibers or flat sheets, which are incorporated into housing modules designed to produce optimal hydrodynamic conditions for separation. Complete systems comprise arrangements of modules, together with the interfaces and control systems needed to integrate them into the various process configurations

Membrane filtration can be used as an alternative for flocculation, sediment purification techniques, adsorption ( sand filters and active carbon filters, ion exchangers ), extraction and distillation. Membranes are now competitive for conventional techniques : their intrinsic characteristics of efficiency, operational simplicity and flexibility, relatively high selectivity and permeability for the transport of specific components, low energy requirements, good stability under a wide spectrum of operating conditions, environment compatibility, easy control and scale-up have been confirmed in a large variety of applications and operations, as molecular separation, fractionation, concentrations, purifications, clarifications, emulsifications, crystallization, etc., in both liquid and gas phases and in a wide spectrum of operating parameters such as pH, temperature, pressure.


Which activities, services offer the RI platform to industry and to researchers ?

Because of its intrinsic properties that well fit the requirements of Process Intensification Strategy, Membrane Technology has today well established applications in many industrial processes including water desalination, wastewater treatments, agro-food, chemical and petrol chemical industry, etc. The most interesting opportunities for industrial applications of membrane technology are related to the possibility to integrate various membrane operations in the same productive cycle or to integrate membrane operations with conventional operations, with important benefits in product quality, plant compactness, environmental impact and energy use.

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There are many significant advantages to using membranes for industrial processes. In 2002, an ad-hoc Committee at the International Conference on Membranes ( ICOM 2002 ) prepared a report on membrane technology perspectives and needs. The following advantages to membrane processes were listed in the introduction :

• donotinvolvephasechangesorchemicaladditives

• simpleinconceptandoperation

• modularandeasytoscaleup

• lowenergyconsumptionandtypicallya-thermal

• greaterefficiencyforrawmaterialsuseandpotentialforrecyclingofby-products

• equipmentsizemaybedecreased

All of these advantages translate into cost savings and more environmentally sustainable processes.

Although it seems the main focus of membrane development is for purification of water ( brackish, wastewater etc. ) to potable standards, membranes can also be used in a number of ways for separation of macromolecules ( i.e. protein purification ), in controlled release systems ( smart polymers for drug delivery and other applications ), and in membrane reactors for the production and purification of pharmaceuticals and other bioproducts.

• Thelarge-scaleindustrialutilizationofmembranesbeganabout1970withwaterdesalinationand purification to produce potable and high quality industrial water. Since then membranes have become a widely used tool in process engineering with significant technical and commercial impact. Today membrane processes are used in three main areas. The first area includes applications such as seawater desalination or wastewater purification. Here, the use of membranes is technically feasible, but there are other processes such as distillation and biological treatment with which membranes must compete on the basis of overall economy. The second area includes applications such as the production of ultrapure water or the separation of molecular mixtures in the food and drug industry. Here, alternative techniques are available, but membranes offer a clear technical and commercial advantage. The third area includes membrane applications in artificial organs and therapeutic systems. There is no reasonable alternative to membrane operations. With the development of new membranes having better separation efficiency, new membrane processes such as membrane contactors and membrane reactors are becoming common unit operations in process engineering.

• Today,membranesareusedonalargescaletoproducepotablewaterfromseaandbrackishwater,to clean industrial effluents and recover valuable constituents, to concentrate, purify, or fractionate macromolecular mixtures in the food and drug industries, and to separate gases and vapors in petrochemical processes. They are also key components in energy conversion and storage systems, in chemical reactors, in artificial organs, and in drug delivery devices

• Environmentalapplications:chemicalIndustryandProcessWasteWaterMembranefiltrationcanplay an integral role in processing difficult wastewater streams to reduce BOD, COD, and hydraulic loadings as well as producing a clean water source that can potentially be re-used within the plant.

What is the benefit ( value proposition ) for the user of this RI platform ?

In the ground water processes treatment membranes can be operated on a low energy basis without a need of antiscalants and frequent acidic flushing. The combination of NF and RO membrane types has proven advantageous in the field tests concerning the given targets. The NF membrane contributes in the softening and in a major flow yield parallel to a small nitrate reduction. The RO membrane again takes care among others of nitrate reduction and of course to a complete hardness reduction too. The combination of both membrane types shows an appropriate way to produce high quality water.

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Separation, concentration, and purification of molecular mixtures are major problems in the chemical industries. Efficient separation processes are also needed to obtain high-grade products in the food and pharmaceutical industries to supply communities and industry with high-quality water, and to remove or recover toxic or valuable components from industrial effluents. For this task a multitude of separation techniques such as distillation, precipitation, crystallization, extraction, adsorption, and ion-exchange are used today. More recently, these conventional separation methods have been supplemented by processes that utilize semipermeable membranes as separation barriers. An important advantage of this technologies use is that the separation is performed by physical means at ambient temperature without chemically altering the constituents of a mixture.

How does the platform close the gap identified ?

Membranes may be an option when they enable the removal of contaminants that other technologies cannot. They are also more economical than other alternatives, or require much less land area than competing technologies, since they may replace several unit treatment processes with a single one. For wastewater treatment applications, membranes are currently being used as a tertiary advanced treatment for the removal of dissolved species ; organic compounds ; phosphorus ; nitrogen species ; colloidal and suspended solids ; and human pathogens, including bacteria, protozoan cysts, and viruses.

Water contains beside of the necessary dissolved matters suspended and dissolved impurities. The water sources often bear disease causing bacteria, viruses, and parasites and this water must be treated and purified to meet human needs. Chemical disinfection is in wide use to treat water but disinfection forms again disinfection by-products ( DBP ). To avoid the generation of such DBPs it is necessary to remove natural organic matters efficiently prior to disinfection. Pesticides and nitrates in ground water is a growing problem in agricultural areas [3]. Beside of flocculation, coagulation and clarification membranes have demonstrated excellent results in natural organic matter separation and have gained a high potential in future in the production of drinking water.

Membrane filtration has a number of benefits over the existing water purification techniques : it is a process that can take place while temperatures are low. This is mainly important because it enables the treatment of heat-sensitive matter. That is why these applications are widely used for food production. Moreover it is a process with low energy cost. Most of the energy that is required is used to pump liquids through the membrane. The total amount of energy that is used is minor, compared to alternative techniques, such as evaporation. Lastly, the process can easily be expanded.


The Institute on membrane technology ( CnR-Itm ) is a structure created by the Italian National Research Council ( CNR - Consiglio Nazionale delle Ricerche ) after a process of reorganization for the development, at a national and international level, of membrane science and technology. Prof. Enrico Drioli was appointed as First Director since its founding in 1993. The Institute is currently directed by Dr. Lidietta Giorno. ITM is headquartered in Rende ( CS ), in the Campus of the University of Calabria with a subsection in Padua. ( http ://www.itm.cnr.it/index.php/en/ )

The eFCe Section on membrane engineering was founded in 2007 emanating from the previous Working Party on Membranes. The formal approval of the transformation and the first meeting of the Section on Membrane Engineering of the EFCE has been held at Copenhagen, on 19thSeptember 2007, during the ECCE6 Conference. ( http ://www.efce.info/home.html )

The european Desalination Society ( EDS ) is a Europe-wide organization for individual and corporate members including universities, companies, research institutes, government agencies and all concerned with and interested in desalination and membrane technologies for water. It is a society uniting Europeans interested in promoting desalination, water reuse and water technology. All

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processes are covered and the wide range of roles and activities involved in the desalination field are included : research, applications, consulting, contracting, operation and maintenance, manufacturing, marketing, economics, legislation. Members are welcome from other regions outside Europe. ( http ://www.edsoc.com/ )

The european membrane House is a non-profit making international association established in 2009 by universities and research institutes in 10 European countries working together around membrane technologies within the framework of the Network of Excellence NanoMempro ( FP6, Contract No. NMP3-CT-2004-500623 ). The European Membrane Society is also an EMH founding member. It aims at gathering the best existing entities in order to create a virtual reference structure in this field of activity. ( http ://www.euromemhouse.com/ )

The european membrane Institute twente ( EMI Twente ) was founded by the Membrane Technology group of the University of Twente in 1995, with the purpose to offer industry and public organizations a platform where short-term research and development projects in the field of membrane science and technology can be carried out. The project duration, which also includes literature studies and marketing assessments, typically varies from a few hours up to three years. ( http ://www.utwente.nl/tnw/emi/ )

european membrane Society ( http ://www.emsoc.eu/ems/site/home/index.php )

See also : http ://www.efce.info/efce_media/Downloads/Section+Membrane+Engineering/Report+on+Membrane+Activities_Final.pdf

technical requirements for new RI elementsIn the previous chapters we have analyzed what the new RI should deliver, which existing RI might contribute to this RI platform and which RI elements are missing. This chapter will describe the physical and technical specifications and requirements for new RI elements needed.

Scope of applicationMain membrane technologies for food processing are categorized into five types : microfiltration ( MF ), ultrafiltration ( UF ), nanofiltration ( NF ), reverse osmosis ( RO ) and electrodialysis ( ED ). Of these, NF has recently made a great advance in regard to its practical use as a new field of membrane separation.

The main applications of membrane operations are in the dairy industry ( whey protein concentration, milk protein standardization, etc. ), followed by beverages ( wine, beer, fruit juices, etc. ) and egg products. Among the very numerous applications on an industrial scale, a few of the main separations which represent the latest advances in food processing, are reported. Clarification of fruit, vegetable and sugar juices by microfiltration or ultrafiltration allows the flow sheets to be simplified or the processes made cleaner and the final product quality improved. Enzymatic hydrolysis combined with selective ultrafiltration can produce beverages from vegetable proteins. In the beer industry, recovery of maturation and fermentation tank bottoms is already applied at industrial scale. During the last decade significant progress has been made with microfiltration membranes in rough beer clarification which is the most important challenge of this technology. In the wine industry the cascade cross-flow microfiltration ( 0.2 μm pore diameter ) – electrodialysis allows limpidity, microbiological and tartaric stability to be ensured. In the milk and dairy industry, bacteria removal and milk globular fat fractionation using cross-flow microfiltration for the production of drinking milk and cheese milk are reported. Cross-flow microfiltration ( 0.1 μm ) makes it possible to achieve the separation of skim milk micellar casein and soluble proteins. Both streams are given high added value in cheese making ( retentate ) through fractionation and isolation of soluble proteins ( β-lactoglobulin ;α-lactalbumin ) ( permeate ). At last, a large field of applications is emerging for the treatment of individual process

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streams at source for water and technical fluids re-use, and end-of-pipe treatment of wastewaters, while reducing sludge production and improving the final purified water quality.

Which are relevant types of operations ?

Some of the largest plants in the world for sea water desalination are already based on membrane engineering. The Red-Sea/Dead-Sea desalination project, under discussion today, is based for example on RO with a productivity of 27m3/s of permeate. Membrane operations are practically the dominant technology in desalination and they will confirm this role in the next decades

expected functionsPlease refer to scope of application.

Description of technical parameters The Red-Sea/Dead-Sea desalination project, under discussion today, is based for example on RO with a productivity of 27m3/s of permeate

applicable standardsList standards that are relevant for the type of research that this element will carry out ( e.g. „IEEE standards“ for control and automation or safety standards in the workplace, etc ).

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5.6. management systems for lean manufacturing

Short description This is a management method for cost reduction and improve production efficiency through elimination of waste or non-value added activities. Strategic objectives of achieving zero loss performance through safety, quality, and waste by full employee engagement. Majority of the novel methods used in the industry are developed for mass production in the manufacturing ( car manufacturing, machinery, electronic appliances, IT hardware ) industries. This RI aimed to adapt the currently available Lean tools for the specific food industrial environment.

activities and value propositionOn a global food market cost reduction is a basic requirement for food companies, and particularly for SMEs. The approaches and techniques, which are adjusted to the needs of the food industry offer many options as supply chain management to reduce inventories, logistic costs, etc. Lean manufacturing aims to reduce waste in terms of time, material, energy, labour. The management system is a structured approach for analyzing the operation of food business along the food chain and identifying areas, where reduction of waste, cost can be achieved to time through in order to increase capacity as well. Moreover, these advanced management system are more prevalent in large organizations, which highlights the importance to implement cost-efficient waste reduction techniques at the level of SMEs. In this way, this RI addresses the large need for a food industry based systems approach for the reduction, recycling and reuse of food waste, as well as other sources of waste.

Effective assessments begin with a clear understanding of the business opportunity associated with each line and the assessment team is encouraged to always prioritize improvement efforts in line with business objectives.

Sustainability is one of the other key areas where Lean method can bring significant improvement, such as reducing water consumption as well as water treatment, packing material usage, energy, fuel consumption etc.

In a single sentence this management system deals with elimination the source of waste and recycles instead of handling those.

General value proposition of RI platforms :

Food manufacturing industry :Main beneficiary of the RI will be the industry. Therefore

• Nearly100differentcostreductiontechniquesimprovingproductionefficiency

• Classicalleanapproach:

– Define target

– Build the team.

– Workshop observation

– Determine the measuring process

– Measuring

– Analyzing and brainstorming

– New method written or modification on equipment

– Trial

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– Validation for new method

– Implementation

– Control

Solution and service providing industry :• OtherkeyactorsarethesensorproducersandtheICTsector.

• Designandproductionofdifferenttoolsfordataacquisitionfacilities,sensors,computingfacilities

Define targetThe first is to define the realistic target, which can be based on initial workshop observation ( no waste, less recycle, reduction on water consumption etc. ).

Usually the main goal is to reduce the waste, the future target is zero however to reach that several milestones might be required on the roadmap. Nevertheless the milestones should be realistic as a short term target as well. The waste reduction can start with first technic implementation as 5 S technic, this help definitely eliminates all of the unnecessary tools, idle equipment as well as rarely used boxes , containers. Boxes used to recycle storage are not our target.

Recycle quantity decreasing is also a target exempt if the technology requires ( based on recipe demand ).

Headcount reduction can be also an aim or optimization of human resources, for instance packing area has more headcount during shutting down hence the packing operators can help to other operators on previous workstations in the process flow.

Changeover time reduction with current headcount might be the target as the market demands flexibly production changes.

Build the teamBuild the team is vital as the team members can contribute to reach the target. The operators who are working on the line know much more the process flow and help a lot to find the right process, hence their involvement is the key. Therefore the involvement is the best tool to gain their commitment in the project, course the communication is vital in order to be align with understanding the target. The team composition should be workshop operators, maintenance, engineers, and external professionals if needed.

Project manager should lead the communication to encourage the team, being interactive as it is crucial to get idea for the problem and what‘s more the initial solution. The leader will make clever question to drive conversation.( 5 Whys )

Workshop observationStaying on the line and observe what is going on helps a lot either understand process or problem itself and gives initial ideas of solution..

Observation is also important to make initial validate of existing data

There are two options

• validateexistingavailabledata

• collectnewdatabasedontarget

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validate existing available data :To be sure the data are realistic and representative of the process on the production, provide real picture what is on the line and data should be compatible to target.

Collect new data :If the data is not giving realistic information to target, how the data collection should be changed or based on observation the team defines the new area what should be measured

Determine the measuring process :Define what the team will measure and how, main rule is to keep it simple, no generate more work for operator only if needed. The quick implementation is to add or combine the new reported parameters in the current data sheet or only adjusted accordingly.

If possible put the data into excel or Minitab application immediately in order to make analysis easier. Of course in this case IT system is required, additionally if the PCs connected to one central PC the parameters can be analyzed online and having result immediately. Alarm system if parameters are out of specification is an advance in technology.

measuring Check the measuring device availability on workshop whether this fits to the unit to be measured, for instance : weight kg by scale with mg units. Of course the calibration of devices is crucial.

After the beginning of measuring the first number should be reviewed to see how it is compliant with our target if necessary the adjustment should be done immediately in order to avoid unrealistic data collection and start measuring phase again which might generate more time.

Practical workshop training is strongly recommended to operators who will collect data due to avoid misunderstanding and recognize invalid data after first review.

analyzing and brainstormingThe initial analyzing part should be done before the brainstorming session in order to see where the team should focus for.

Three basic solutions usually in Lean process :

• Newmachineorder,requiresinvestment

• Processmodificationbasedonoutcomeofanalyzingstage

• Immediatelyaction,notrequiredmoreobservation–“justdoandsee”thissmallchangeimpactsalotwithout investment.

Based on Pareto, 5ws, Ishikawa problem solving methods, whatever additional problem or solution are recognized the team alignment is vital, brainstorming should be organized on that rule of no limits for ideas ( cost impact will be later ). Additionally the lack of device ability on the market should not be the barrier to get ideas.

Select the ideas or make a group ( which ideas belong to one scope ) if the given ideas number is huge ; n/3 method can be used to limit number of ideas. Prioritize the ideas first 20% which impact on the final process 80% changes ( Pareto ).

new method written as new instruction or equipment modification The detailed instruction to internal/external supplier to modify the equipment, workshop presentation is strongly recommended based on lack of understanding.

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Responsible person and deadline should be nominated ( one person from team if it is a team- work ) in order to follow progress up.

The new instruction should include the new parameters set up and any modification which related to target. The process modification might be together with device modification. The delivering time of equipment or spare part should be considered in the roadmap.

The new parameters set ups should be aligned with operators through training if needed.

trialBased on new method instruction the trial should be measured with same technique as it was previously applied to see any differences ( apple to apple ). During trial the minutes is the key and all of the parameters or facts must be reported if they impact on production trial. The new instruction training should be given to all workshop staff that is part of trial ( manufacturing, maintenance, quality, engineers, and external staff ).

validation for new methodAfter trial the new instruction or equipment should be reviewed to see how is compliant to the target and either any more modification is required.

Based on trial outcome new modified parameters should be installed in the normal process. In this case other trials might be done and after the final one where the parameters are conformed approval should be asked.

Approval from :

• alignmentfromteammembers

• approvalfrommanagementteam,presentationofnewmethods/equipment

Implementation• Approvednewprocess,modifiedprocessorequipment,newinvestmentshouldbeinstalledonthe

line, the key information is when the new process or equipment started to work, this is important to follow changes in the flow diagram. If new equipment was installed on the line the approval is crucial to see how this is targeted with or specification what is more this is the validation to close equipment supplier contract.

ControlMeasure either the targeted KPI’s or parameters are the key to see how the modified system works and define additional changes required. Moreover from time to time the whole implemented process/ item should be reviewed to see how follows the approved specification and if necessary as deviation is appeared the review is a must.

R+D sector :• knowledgetransfer

Integration of existing research infrastructures Lean management techniques are dealing with all existing pilot plants, pilot size factories. This practical, easy to use tools are frequently used in high-tech industry, where the high capacity, automatized production lines that produce large volumes, large batches of products, made of materials of relatively uniform quality and a limited number of designs are very typical. In the food industry there are significantly lower volumes of batches. Significant numbers of manual or less automatized operations are involved. Therefore, using these tools requires adaption of lean management to the needs of food SMEs.

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Most of these techniques can be integrated into an effective measuring system. The data can be systematically collected and the parameters can be also calculated with various IT tools.

Usually there are KPI’s on the existing system which can help to identity future developed area in order to start the classic Lean circle.

One of the most useful technique is the “do and see” on the existing line, as several cases small changes might generate a huge impact based on online observation, in this case the advantage is not to do the whole Lean circuit and spend long time for analyzing.


Scope of applicationCost reduction management techniques are applicable in all food sectors, depending on the objectives what we would like to achieve ( e.g. reduction of maintenance cost or line operational cost ), the applied techniques ( waste identification method or sort of cost reduction tool ). When taking into consideration the manufacturing and food industrial specialties, these techniques are equally applicable in both sectors.

Some tools are more effectively applicable for more automatized plants, others for hand making techniques. The operational environmental conditions are much dependent on the applied tools.

expected functionsThe unique characteristics of the food sector with respect to product, process and plant poses some challenges to replicate these management systems in the food sector. In addition, the organizational structure of SMEs also creates a fundamental barrier to implement cost efficient management systems.

The lean management is the tool using resources on the most optimized way and generate high production outcome.

The principle of lean manufacturing is to eliminate waste along entire value streams and, creates processes that need less human effort, less space, less capital, and less time to make products and services at low costs and with much fewer defects, compared with traditional business systems. Companies are able to respond to changing customer desires ( high variety, high quality, low cost ), and ensure very fast delivery times. Similarly, statistics based on six sigma methods enable companies to achieve fewer defects and improve quality.

There are systematic frameworks to implement lean manufacturing and six sigma methods in industrial organizations. However, it is very important to adjust these implementation methods by taking into account the special needs and characteristics of the food sector.

Finding the solution might be in the pilot plant or at the manufacturing site depend on problem characteristic ( internal –pilot plant, external-production line ).

The main requirements of pilot plant : available software ( for instance Minitab or any relevant software ), of course online sensors on moisture, colour, density, velocity, speed ; temperature would be added value to avoid manual data collection.

If the problem is more related to production plant ( outside of pilot plant ) the mobile measuring devices with linked sensors give huge impact on the real production line in terms of analyzing.

Measuring system includes sensors linked to one PC and linked to Minitab can give such a following advantage : less manual documentation on paper or PC-less headcount required during any trial, immediately possibility to stop or making adjustment on measuring or during trial if something is not realistic. This completed system works with less time generates more capability of making more trial on line as well.

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Measuring system ( sensors and software ) can be developed for category such as moisture, weighting, colour, density etc. and to be used as best practice. As well as generate possibility to transfer this mobile measuring system into the client facility.

Difficulties / limitationsDeveloping or implementing this new RI element may cause technical difficulties. Its implementation might be also limited to a certain extent. It will be crucial to identify and describe these difficulties and limitations and, if possible, provide ex-ante solutions for it.

One of the barriers is the time if the solution is quite complex to reach target and requires time for analyzing.

Safety issuesThe responsible team has to specify the methods and procedures to be used for determination if the established system is working correctly/efficiently with regard to the performance criteria and food safety issues which are the most important targets of food processing companies. Thereby, food safety and quality should always have the first priority. For example, a reduction of water consumption should not result in hygienic problems.

estimated costs Strongly depend on the applied techniques, area, etc. An initial, minimal investment of 500,000 Euro will be essential to start the project.

Measuring devices targeted for example to moisture, humidity, colour, density, flow rate, viscosity, weighting, speed etc. The devices with sensors should be mobile and transfer easily to client facility.


StaffStaff and outsourced experts need knowledge on different cost reduction techniques or simply analyzing method as well as technical, food technology background.

Competence and servicesRelevant food technology, technical, manufacturing, organizational skills and experiences and competences for the food processing and manufacturing industry. Training, technology transfer between the sector and the consultant and production side has to be initiated.

training and technology transfer facilitiesOrganizations such as the lean enterprise institute, American Society for quality and IEE, could be involved to support the training and knowledge transfer activities.

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5.7. Supporting e-infrastructure to run cloud services

“Supporting e-infrastrutcure” to run cloud services

Short description Pilot size factories include a variety of technologies like process control systems, decision support tools, simulation tools, operation planning systems that will have a basic need of interoperation and integration. In this context, integration and interoperation of these heterogeneous technologies can be achieved through the specification, deployment and interaction of loosely coupled, coarse-grained, and autonomous components, encapsulated in what is commonly known today as a service. In this service-oriented software architecture ( SOA ), each service exposes processes and behaviour through contracts, which are composed of messages at discoverable addresses ( endpoints ). The behaviour of the service is governed by policies that are external to the service itself. Contracts and messages are used by external components called service consumers.

On the other hand, Cloud computing offers a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources ( e.g., networks, servers, storage, applications, and services ) that can be rapidly provisioned and released with minimal management effort or service provider interaction ( 2011, “NIST Definition of Cloud Computing” ). In this context, the “Platform as a Service” ( PaaS ) service model is defined as the capability provided to the consumer to deploy onto the cloud infrastructure consumer-created or acquired applications created using programming languages, libraries, services, and tools supported by the provider ( 2011, “NIST Definition of Cloud Computing” ). The consumer does not manage or control the underlying cloud infrastructure including network, servers, operating systems, or storage, but has control over the deployed applications and possibly configuration settings for the application-hosting environment.

The proposed RI Platform is a set of services, tools and methods available in a “Platform as a Service” ( PaaS ) environment enabling technologies in the pilot size factory to deploy onto the cloud infrastructure their specific functionality and resources in the form of services and artifacts so as to facilitate their interoperation and integration and to enable the sustainable creation of a more open data infrastructure.



• Asetofservices,toolsandmethods,availableina“PlatformasaService”(PaaS)environment,enabling and facilitating industry and researchers to encapsulate specific ICT application functionality ( present in technologies like process control systems, decision support tools, simulation tools, operation planning systems ) in the form of services, to deploy and exploit these services in the cloud infrastructure so that they don’t have the need to manage or control the underlying cloud infrastructure ( network, servers, operating systems, or storage ), thus enabling the creation of an ecosystem of applications and services.


• UsersofthisRIPlatform(mainlyICTdevelopers)willnotbeconfrontedwiththetechnologicalchallenges and the specific skills/expertise ( know-how ) needed to manage an open ICT infrastructure where research resources ( hardware, software and content ) can be readily shared and accessed where necessary to promote better and more effective research.

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• Allpilotplantsshouldhaveadequatesupportinge-infrastructure,suchasauthenticationandauthorization technologies and policies, middleware, data infrastructures and persistent data storage grid, cloud and virtualization services. The proposed RI Platform aims to address the issues of middleware and cloud services so as to enable ICT applications in a pilot plant to interoperate and share their functionality and data.

Integration of existing research infrastructures

Which existing pilot plants, pilot size factories and research institutions in europe are dealing with this topic ?

• TheFutureInternetcoreplatformFI-WARE,specificallytheGenericEnablersimplementingthe“Cloud Hosting”, the “Applications/Services Ecosystem and Delivery Framework”.

Which main RI elements ( e.g. large equipment, specialized lab, databases ) are existent in those institutions already and which elements are explicitly missing ?

• ProprietaryandOpenSourcesoftwareimplementationsofFI-WAREelementsarebeingdeveloped.

What would be strength and weakness of the existing institutions mentioned if we include them in this RI platform ?

• ThestrengthliesinthefactthattheEuropeanFutureInternetinitiativeaimstoconceiveandimplement a very wide set of software elements in order to build the foundation of the Internet in the future. As such, the specifications and the “open source” software implementations that are being developed in that initiative can offer a solid basis to build the proposed RI Platform.

• Regardingtheweakness,astheFutureInternetEuropeaninitiativeisongoing,technicalinformation about it is not yet in a format that could be exploited by “outsiders”. Additionally, significant parts of the Generic Enablers ( software ) that are being developed are proprietary and only a minor part are Open Source and easily reutilized in the proposed RI Platform.

technical requirementsThis chapter will describe the physical and technical specifications and requirements for new RI elements needed. It includes the description of the expected functions and the technical futures and the estimated costs if applicable.

Scope of applicationThe proposed RI is applicable to every branch of the food sector or manufacturing sector. It provides a “Platform as a Service” ( PaaS ) cloud computing services and as such it is applicable to any ICT service provider aiming to develop ICT applications and services to the food manufacturing sector and to any food manufacturing company acting as a user of those ICT applications and services.

expected functionsTwo types of users are foreseen for the proposed RI Platform : ICT service providers and food manufacturing companies. The “Supporting e-infrastructure to run cloud services” Platform can be considered a success once it provides the services, tools and methods allowing pilot size factory ICT-based enabling technologies to be deployed in a cloud infrastructure and after their interoperation and integration is accomplished. This will be mostly measured with the ICT service providers that act as users of the proposed RI Platform.

At its core, the RI Platform should ensure the following five key cloud characteristics ( 2011, “NIST Definition of Cloud Computing” ) :

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• On-demandself-service.Consumerscanrequestandreceiveaccesstoaserviceoffering,withoutan administrator or some sort of support staff having to fulfil the request manually. The request processes and fulfilment processes should be all automated ;

• Broadnetworkaccess.Cloudservicesshouldbeeasilyaccessedthroughabasicnetworkconnection( Internet ) to connect to services or applications ;

• Resourcepooling,asserviceclientswillnothaveaconstantneedforalltheresourcesavailableinthe cloud, allowing thus computing/networking/storage resources to be used when not being used by one client.

• Rapidelasticity,i.e.,theabilityofthecloudenvironmenttoeasilygrowtosatisfyuserdemand,capabilities in the cloud environment are elastically provisioned and released.

• Measuredservice,theabilitytomeasureusageofalltheresourcesused(timeused,bandwidthused, data used, etc. ).

At a higher level, the RI Platform should provide integration and interoperation services to deployed ICT services and applications so their specific data and communication protocols are adapted and may interoperate.

Difficulties / limitations• Ownershipandprivacyofdata.Thisisacommondifficultywhileusingacomputingcloud

infrastructure : who owns the data ? How does one ensure data privacy between the several organizations that are using and sharing the same resources in the cloud ?

• Maturityofcloudcomputingtechnology.

Safety issuesSafety issues to consider are the usual ones related with building a computing and data processing site.

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5.8. pilot plants for implementation of robotics and automation in food production

Short description The starting points are the main challenges found in food industry elaborated in Part I.

In this RI on robotics and automation in particular 2 challenges are in focus : flexibility and cost-effectiveness. Flexibility in this context means coping up with variations in the properties of the input, change in the functions of the machinery ; and change in capacity needs. E.g. handling and picking apples in June, picking oranges all year, picking plums in autumn : all are different in size, number, shape, surface, period etc. The main challenge is being flexible and cost efficient at the same time. I.e. fixed line processes for mass production are not in the scope of this RI.

As identified before as well, this RI is a response to a need for automation and mechatronics infrastructures equipped with specific food manufacturing facilities. The RI platform for robotics and automation consists of facilities in which different automation technologies can be developed, tested, adapted, demonstrated and validated to food processing. In particular, relevant facilities are :

• facilities to develop, demonstrate and validate robotics and advanced automation ( e.g. flexible robots, robotic co-workers and mobile manipulators ) in a food processing context.

• facilities to develop vision systems and gripper technology adapted to food manufacturing ( related to platform „Artificial vision and augmented reality“ )

• computing facilities for designing, developing and testing advanced sensing technologies capable of collecting, storing and analyzing a large amount of data

• facilities for machine tool technology providers enabling adaptation, upgrading, configuration and tele-maintenance/tele-servicing of production technologies

• facilities ( test-beds ) for collecting reliable data on systems operation in pilot-plant and real factory environment.



Main beneficiary of the RI platforms will be the food manufacturing industry. Therefore, RI robotics and automation offers the following activities and services to its target clients :

Food manufacturing Industry :

Will be able to take full advantage of play-ground facilities with a wide range of flexible robots, robotic co-worker and mobile manipulators in order to develop, adapt and validate their use in a real food processing environment. Combined with that, test-bed facilities will provide industry real-life conditions to develop and test vision systems, grippers, sensing technology and to adapt tele-service and tele-maintenance of the factories machinery. The combination of these possibilities enables the food manufacturing industry to have an Research Infrastructure to help in developing and improving its production processes supported in robotics and advanced automation.

Additionally, the serving as test users in technology development creates a strong advantage as the food manufacturers may get technologies targeted to their exact challenges and needs.

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Solution and service providing industry :

Will provide the technology and their integration into the processes as demanded by the food processing industry.

The production technologies developers in robotics and advanced automation will have a privileged field to develop and test their technology in close cooperation with the food manufacturing users. This collaboration allows not only minimizing the risk and cost of technology development, but also creating a strong competitive advantage to developers as they can produce technologies ^directing responding to the needs of the food manufacturing industry.

provider of applied and fundamental research :

Will provide lab facilities, where new research can be tested in an industry near facility. This allows not only expanding their research base, both applied and fundamental, as well as building new industrial partnership, beneficial to their research activity.

For all stakeholders, confidentiality is an important requisite.

Integration of existing research infrastructures Each RI platform will be a sub-network of existing pilot plants and pilot size factories that are in close contact with research institutions dealing with this issue.

The list of existing pilot plants, pilot size factories and research institutions in Europe are dealing with advanced automation and robotics in the food sector which can be adopted is fairly short. In the following they are categorised according to 4 main criteria : research installation, pilot plant, database/collection, and advanced equipment. For details, it is suggested to consult D 3.10.

• DIL(researchinstallationandpilotplant)

• DTI(researchinstallation,pilotplant,databasecollection,instruments)

existing main RI elements and missing elements Currently, most of the facilities dealing with R+A are scattered and do not share their infrastructure and applied technologies into a networked RI. Furthermore, for the time being, some RI elements are missing ( see also D3.14 )

• Design,developmentandtestingfacilitiesforadvancedsensor-networks

• Wirelesssensornetworkwithdata-miningfacilitiesandsoftware

• Playgroundfactories(full-scaleindustrialtestbeds,emulatedindustriallaboratoryfacilities,ortest/pilot lines in industrial environment ) for developing and testing pilot equipment, e.g. modular manufacturing systems, autonomous, flexible and/or mobile manufacturing solutions, and etcetera. The “factories” must resemble real-world industrial environments ( including human-machine- and machine-machine-interactions ), but are able to withstand disturbances and adapt to them.

• Disseminatingcentersprovidinginformationonautomation

Data modeling and data bases to simulate processes are tackled in collaboration with RI virtual/augmented reality, for simulation of process steps, process lines and training.

technical requirements for new RI elementsThis chapter will describe the overall physical and technical high-level specifications and requirements for new RI elements needed. It includes the description of the expected functions and the technical futures and the estimated costs if applicable.

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Pilot plants must operate under real food industry conditions and must cover entire productions chains from raw material to packed food products. Pilot plants must comply with the requirements which apply to the food processing industry, as hygiene and food safety.

To describe technical requirements and associated requirements, this document follows a top-down systematic.

This RI has got to implement state of the art technology demonstrating the already mentioned flexibility and cost effectiveness to be attractive both to manufacturing solutions providers and food processing industry. This will apply to SMEs with – in average - very low level of innovation and to large companies which otherwise would do applied research company internally. Since state of the art is rapidly evolving in robotics and automation, it is important to continuously update the technology inside the RI. This should be done by creating close collaboration with technology providers supporting this RI with in kind contributions which will also have the benefit of helping to boost technology transfer and cross fertilization from and to other manufacturing sectors.

Besides demonstrating state of the art, pilot plans must be easily adaptable to new production tasks and new technologies which may appear in the near and midterm future. Easy adaptability and fast integration could be achieved by open access architecture and a systematic and modular design of the RI.

• High-levelTechnicalrequirements:

– Implementing state of the art automation technology.

– Demonstrating an entire production chain.

– Prototyping workshop to produce mechanical and electrical components ; e.g. simple grippers for robots ; electrical adapters, brackets, fixtures, feeders, etc.

– IT and communication equipment open as much as possible and well documented – this is not a trivial task due to large variations used by industry / consortium members

• Otherrequirements:

– Data modeling and data bases to simulate processes - this is a requirement tackled in collaboration with RI virtual/augmented reality for simulation and training

– Skilled technical staff

– Project management tools

– Powerful servers for running the real plant in a virtual environment ( in order to allow internal/external researchers evaluate conceptual ideas )

Scope of application• ExamplesoftechnologiesalreadyidentifiedinWP3(GapAnalysis),whichcouldbeincorporatedin

this RI are :

• Automationforfoodproductscounting

• Man-machineinterface

• IntelligentdataacquisitionandprocessinginindustrialenvironmentapplyingWirelesssensornetwork ( WSN ) technology

• Advancedsensingandcontrol

• Manufacturingassistantsorroboticco-workers;MobileManipulators

• HyperspectralandMultispectralImagingSystem

• Roboticsforincreaseautomationflexibilityinfoodmanufacturing

• Visionsystemsforrobotguidanceandfoodinspection

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• ApplicationofOverallEquipmentEffectivenessinfoodSMEs

• Internallogisticsautomationandmanagement

• SupplyChainManagementwithRFID

• Onlinelabelingofpackagedfood

• Packagingondemand

expected functionsPlease refer to “scope of application”.

Description of technical parameters NA.

applicable standards• Technicalstandardsformachineryinstalledforfoodprocessingandfoodproductiondoapply:

• EN1672-2(2009)Foodprocessingmachinery–Basicconcepts–Part2:Hygienerequirements

• ENISO14159:2004Safetyofmachinery-Hygienerequirementsforthedesignofmachinery

• TheRIwillhavetocomplywiththeexistingsafetystandardsforrobotsandindustrialsystems.

• ITstandardsareachallengeastheyexistbutthereisnoonecommonstandardandtheydochangein time.

Difficulties / limitationsDifficulties and limitations are :

• continuouspilotplantupdating.

• coveringtheverylargevarietyofproductiontasksfoundatSMEsandatthefoodindustryingeneral. Focus will have to be on specific segments.

• havinganopensystemsothattechnologyprovidersandresearcherseasilycanintegratetheirtechnology into the food chain. It will additionally create opportunities for standardization.

Safety issuesThe pilot plat has to comply with the existing safety regulations as well as to serve as test bed for new ones

legal and technical approvalAuthorisation for food production is needed in case products are foreseen to be sold on the market. The RI has to be authorized for food production.

location It is envisioned that there are in minimum 4 - 5 pilot factories to be established throughout Europe in order to fulfill electively the need of different segments ( e.g. meat, fish, bakery, frozen vegetables+fruits, and packaging ). It is also envisioned that the question where each pilot plat are actually located and which segments are selected are driven by the customers of the pilot plants.

estimated costs Per facility : A very basic estimation to set up, not to run, of 1 pilot plant need an investment of around 8 – 15 Mio €. Running costs could roughly be estimated to be around 4 Mio € per annum ( staff of

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around 30 - 50 with an average salary of 60.000 – 80.000 €/year, including overhead, maintenance, accounting, human resources, personnel to operate machinery etc. )


Staff• Toapplyandimplementroboticprocessstepsintocomplexprocesslined,adedicatedskilledstaffis

required including personnel to install, set up, adapt, regulate and operate machinery and robotics.

Competence and servicesIn the field of production technologies : robotics and advanced automation, software ( supply chain, scheduling ), ICT for manufacturing, sensors, etc.

In the field of food manufacturing : supply chain, logistics, collaboration networks, operations planning, decision support, technology planning, etc.

training and technology transfer facilities• ThisRIcanbeusedtopromotetrainingandeducationprograms,duetoitsuniquecharacteristics

of testing, piloting, and demonstrating food manufacturing processes and their technologies. This RI utilization may be offered as a paid service in order to promote its economic independence and sustainability.

• Moreover,aspartofanEuropeannetworkofRIs,thisonecanprovideservicesrelatedtoinnovation management, such as technology transfer, as it is a privileged setting to this kind of activities to occur, as referred before. The RI may offer formal services and competences to support these needs, which will certainly arise in this innovation setting.

• Inaddition,facilitiessuchasmeetingrooms,conferencecallequipment,andothercommunicationtechnologies are mandatory to support a proper working environment that fosters innovation.

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5.9. virtual / augmented reality for simulation and training

Short description Virtual and augmented reality ( VR/AR ) and related technologies are considered vital tools for overcoming some of the key challenges for the food manufacturing sector as they are described in this report.

Mathematical modeling in terms of flow sheeting, process control, unit operations and food products on a molecular to a macro disperse scale will contribute to the understanding of food properties and functionality and how they are formed or modified during processing. The use of simulation tools can help companies to select the appropriate process for manufacturing of added-value food products and minimize the amount of pilot plant trials. In addition, holistic models of product flow and factories will support faster investments and decisions. Using modeling and simulation systems as basis will enable the use of staff training in virtual or mixed environments. This platform will accordingly deal with virtual environments and augmented reality at three levels with a number of conceivable sub-levels :

• Digitalmodelingofproductionsystems

– Development of mathematical models of elements in the production system ( raw material, equipment, supply chain etc. )

– Disseminating already existing technologies and models for the mutual benefit of industry and research.

• Simulationofproductionsystems

– Simulating whole value chains.

– Designing production plants and developing equipment.

– Analyzing performance of production plants.

– Performing process optimization and intelligent process control.

• Traininginvirtualenvironments

– Visualizing and simulating operation of equipment.

– Training of practical skills and process understanding.

– Training and performance evaluation for complex tasks.

– Contribute to overcome the increasing need for smart user assistance systems, which can help people interact with complex environments in an efficient and healthy way.

The Platform should be seen as a meeting place where relevant experts from universities and research institutes can be coupled to relevant experts from industry to provide insight in the application areas.

activities and value proposition A significant technological “know how” about VR/AR related technologies already exists, but is currently not well-distributed and therefore not easily available for potential users. In other sectors, such as automotive or aviation, VR/AR technologies are used for simulation and training to a broad extent [2-8]. But there is a gap between the existing VR/AR technologies and their actual use in the food manufacturing sector ( as identified in WP3 ).

There are different layers of activities and services related to this Platform. First of all it will offer tools in the design and development phase of products, equipment and plants.

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Secondly, the Platform will utilize and integrate existing technologies within a VR/AR framework for the purpose of simulation and training in the food manufacturing sector.

A key factor for virtual simulation or use of augmented reality is the availability of sufficiently accurate mathematical models and data to feed into the various simulation and training software systems. In this context data could be models of business systems, machinery, technologies, raw material, logistics etc. The model-based approach can be seen as the next step in computer-aided manufacturing. By incorporating model-based strategies into the development process the prototyping phase can often be replaced or speeded up significantly. In the context of a ‘V’-model for the development process ( see Figure 1 ) the integration & verification phase can be predicted by models already in the definition & decomposition phase.

Figure 1 : ‘V’-model describing a typical development process

In order to be truly assistive, the VR/AR systems need to have cognitive capabilities enabling them to understand the user’s activities in relation to the underlying workflow patterns. The constant interaction between user and system during task execution is needed for providing timely instructions and messages tailored to the action under performance. This is a key factor for the user cognitive process [1]. Even more important, for practical applications, is the possibility of such systems to learn workflow patterns from examples, enable generalization according to the user profile, surrounding environment, application domain, and independent of the existing infrastructure.

The Platform will offer the following activities and services :

• Adatabase(orcollection)ofmathematicalmodelswillbecreatedandmadeavailablefortheusersof the Platform. The database will comprise a large portfolio of mathematical models, relevant for describing the different elements of the food manufacturing process, and is the core element in this Platform. The database will be created and refined using an open innovation mindset where various stakeholders can contribute and/or utilize the content.

• Modelstobevalidatedbycomparisonwithrealdatacollectedatselectedpilotplantsandfrom outside by using an open innovation mindset. The easy and inexpensive availability of models implies many advantages for industry, such as : support decision for investments in new technologies, optimization of unit operations and manufacturing systems towards food quality, energy and water saving and a reduction in the number of pilot plant trials before product development.

• Aninfrastructureinsidewhichprovidersofsimulationsystemscandemonstrateandtesttheirsystems in a larger food processing context.

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• Existingstate-of-the-artvirtual/augmentedrealitytechnologyforsimulationandtrainingaswellas computer aided manufacturing will be transferred from more advanced industries to the food manufacturing sector. Universities, research institutions and national institutes of technology can be key resources in this process.

• Anumberoftrainingfacilities,someinconnectiontothepilotplantsdescribedin1.1+1.2,willbe established or build on already existing RI’s ( see section III ). The facilities will be supported by relevant staff and might be targeting different types of users within the food manufacturing sector.

The Platform will have enough critical mass to offer different value propositions for the various users :

• Thelarger companies will have easier access to data and models for simulation related to their own manufacturing process or their entire business system. They will be able to get introduced to and possibly link new suppliers or technology providers into their value chain by meeting them through the open innovation mindset.

• Sme’s will be able to start using simulation methodology to support the optimization of their manufacturing and business processes. The modeling support offered by the Platform is especially relevant for SME’s who do not have personal or financial resources to develop complex simulation software solutions.

• Companiescanaccessthefoodmanufacturingsectorastechnology providers e.g. by making their technology or competences available through the model database.

• Simulation system suppliers can demonstrate the capabilities of their systems in a food processing context.

• Researchers will be able to access state-of-the-art data and will thus be able to conduct relevant industry-orientated research.

• Companies ( both Sme’s and large companies ) will benefit from easy to setup platforms for faster and easier training of their collaborators before start performing new tasks and workflows. Companies will also be able to establish links to an international network of researchers and research institutions.

Integration of existing research infrastructures The platform will a web based virtual facility, but will be linked to a network of pilot plants and pilot-size factories.

There are numerous projects, research institutions and existing physical facilities in Europe working with elements relevant for this Platform. These existing research infrastructures will be the cornerstones in the VR/AR Platform. Some are working with different training aspects using ICT while others are working with simulation. Some are combing these objectives, but can be strengthened and made more available. Since training, simulation and modeling are part of many technical universities and research institutes ; only a small selection is presented here, while a more detailed overview will be made available with the Platform. For the details, it is suggested to consult the D 3.10.

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name and location

purpose of facility Specific focus and capabilities

ITIA-CNR ( Italy )

Pilot plant for flexible automation and new business models in mechatronics De-manufacturing

•Modularityandre-configurabilityofmanufacturingtechnologies and control systems

•Distributedautomationandcontrol

•Roboticsandhuman-robotcooperation

•Visionsystems

•Virtual/augmentedreality

•Interoperabilitythroughsemanticwebtechnologiesandontology

•Systemengineeringandintegration

MTA SZTAKI ( Hungary )

Hungarian Academy of Sciences Computer and Automation Research Institute

•3DInternet-basedControlandCommunicationsLaboratory.

•CognitiveInfocommunications.

•ControlTheoryResearch.

•ComputerIntegratedManufacturingLaboratory.

•GeometricModelingandComputerVisionLaboratory.

•DistributedEventsAnalysisResearchLaboratory.

Bilgem TübitakMarmara Research Center ( Turkey )

AttheBilgemTübitakMarmara Research Center there are two organizations working in ICT solutions, BTE and ILTAREN.

BTE – the Information Technologies Institute :

•Sensorsystems

•Microsensornetworks

•Embeddedsystems

•Computationalintelligencetechniques

•Decisionsupportsystems

•Softwareprocessandproductmanagement

•Informationnetworksandprotocoltechnologies

ILTAREN :

•Decisionsupportsystems

•Simulationoutputgenerationandanalysisinfrastructures

•Virtualenvironments

•Informationmanagementandintegrationcomponents.

INESC Porto ( Portugal )

INESC Porto was created to act as an interface between the academic world, the world of industry and services, and the public administration. The main activities are scientific research and technological development, technology transfer, consulting and advanced training programs

•Laboratoryforroboticsystems,supportingthedesignandtestof robotic solutions for production and internal logistics.

•Designofproductionsystems,usingdiscretesimulationtoolslike Arena and Simio.

•Decisionsupportsystems,especiallyinthefieldofoperationsplanning.

•EnterprisecollaborationnetworksandB2Binteroperability.Support to design collaboration strategies, decision support tools and systems interoperability.

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CISS – Centre for Embedded Software Systems ( Denmark )

CISS is a world-leading research center within embedded software, based at Aalborg University.

•Embeddedsoftware,.i.e.softwarethatisintegratedintomechanical applications.

•Intelligentsensornetworks,.i.e.systemsthatcollectanddisseminate single pieces of information and are capable of comparing the gathered information and subsequently provide new information or take action on the basis of this received information.

•Embeddedreal-timeoperatingsystemsandplatforms,alsousing Open Source standards in such systems.

•Wirelessnetworks,i.e.usingsensorsthatareself-sufficientinterms of energy.

•ITinautomation,i.e.useofITfortheregulationofmechanicalprocesses.

•Modeling,verification,simulationofmodelsusingnetworksoftimed automata and extensions there of.

TNO ( Netherland )

TNO is a Dutch contract research organization with a staff of approximately 4000 people. TNO is carrying out research in order to achieve impact on the following seven themes : Healthy Living, Industrial Innovation, Safety, Mobility, Energy, Built Environment and Information Society.

•Additivemanufacturingincluding3Dprintingforcomputerbased design and manufacturing of parts, where one of the current activities is translating the 3D print technology into a 3D food printer.

•Modelpredictivecontrol,whichdevelopscomputer,basedmodels for predicting the behavior in chemical plant and oil refineries in order to optimize production and being able to quickly adjust and control processes in the plant.

•Modelbaseddevelopment,wherecomputermodelsarebeingdeveloped and used to replace expensive and time consuming experiments and test. This is an accepted design approach in aerospace, automotive and mechatronics. The models are being further developed, while also new markets are being explored.

SIK – The Swedish Insti-tute for Food and Biotech-nology ( Swe-den )

SIK is an industrial research institute owned by SP Technical Research Institute of Sweden. The purpose of the institute is to strengthen the competitiveness of the food industry.

•Modelingofmass,heatandflowinfoodunitoperations¨

•Modelingofelectromagneticheating

•Modelingofmicrowaveheatingofreadytoeatmeals

•Modelingofthermalprocesses

•Modelingofqualitykinetics

•Modelingoffoodstructureandrheology

•Modelingoffoodproductionandchainmanagements

•Decisiontoolsforselectionofprocesses

SINTEF ICT ( Norway )

SINTEF is the largest independent research organization in Scandinavia. SINTEF creates value through knowledge generation, research and innovation, and develops technological solutions that are brought into practical use.

•Open source simulators for simulation of flow and transport in porous media.

•Visual computing for interactive visualization of large scientific data sets.

•HPC and cloud computing to integrate the power of supercom-puters into mobile devices, such as laptops and phones.

•Transportation planning for automated, optimized planning of transportation to reduce costs and environmental effects, to increase profits, and to improve customer service.

•Robotics for manufacturing in the fields : real time control and communication, sensor fusing, 3D sensing, motion estimation, etc.

•Process control to develop new methodology and tools for process control in order to obtain high product quality and yield.

•Optical measurement systems and data analysis based on competence in optics, spectroscopy, lighting, computer vision, and data analysis.

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Summary – existing facilitiesOnly a small selection of existing European infrastructures relevant for the VR/AR Platform has been presented here. Common to them all and other infrastructures not listed here, is that they encompasses a variety of equipment, information and human capabilities making them valuable for the food manufacturing RI. Most of listed RI’s are not related directly to the food sector but are working with relevant topics in a more generic way – or related to other industries. To ensure the maximum benefits of the capabilities at these facilities they must be linked to relevant parties with specific knowledge about food related issues. Doing this will be a crucial part of the establishment of this Platform.

technical requirements for new RI elementsIn the previous chapters we have analyzed what the new Platform should deliver, which existing RI’s might contribute to this Platform and which RI elements are missing. This chapter will describe the physical and technical specifications and requirements for new RI elements needed. It includes the description of the expected functions, technical features and cost considerations.

Scope of applicationThe Platform will be applicable in different branches within the food manufacturing sector. Important benefits of the Platform are :

• Simulationandtrainingintheoperationof(new)equipmentwillbeveryrelevantinrelationtoadaptation of new technology ( e.g. automation ), which takes place in all branches.

• Optimizationofunitoperationstoreducepilotplanttrailsandspeedupproductdevelopment.

• Simulationofentireproductionsystemswillbeveryrelevantwhencompaniesarefacinga( radical ) technology shift in manufacturing processes or when new factories/production lines are established.

• Computeraidedmanufacturingcanbeusedinthedevelopmentofproductionequipmentandreduce the stock of spares, by opening the possibility for local on demand production.

• Modelingofactions,movementsandincidentsinthefoodchain.

• Easieraccesstostate-of-the-artandaccuratedataandmodelswillbeapplicableandbeneficialformost branches, technology providers and academia.

• OpennessandavailabilityofthePlatformmightfostertheestablishmentofnewcollaborationstructures among industrial partners and new value chains might erupt.

• Interoperabilitywillalsobeakeyfactorforthesuccessoftheimplementationandtheuseoftheplatform.

• Decisionsupporttoolstohelpinselectionofequipmentforfoodprocessingandbusinessintelligence.

• Kineticmodelingofproductproperties,suchascolor,texture,degradationandnutritionalvalue.

• Measurementandcalibrationrelatedtothequalitycontrolinfoodmanufacturing.

• Companyplantstudiesanddesignsupportbeforeinstallationandoptimization.

expected functionsThe proposed Platform will deliver technical services and facilities for research and experimentation in order to develop solutions to the problems, which the FoodManufuture Project has identified as priority needs.

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Important constituents of the Platform are linked to the physical inventory and capabilities at pilot plants and existing RI’s listed in section IV. They are :

• Foodprocessingequipmentandtheirassemblies,researchinstallations,sensors,instrumentsforfood processing research.

• Equipmentandtheirassembliesandinstrumentsformanufacturingresearch,includingroboticsand automation with compliance to hygienic food handling requirements.

• Computersforsimulation,modellibraries,HPCclusters,dataprocessinganddataminingmethodologies.

• VR/ARsystems

The core of the Platform is an e-infrastructure enabling operation of networks, process control, databases, computing facilities and collection, screening and structuring of information.

Important functional aspects of the Platform are :

• Abilitytosimulateman-machinecooperation.

• Abilitytotestnewbusinessmodels.

• Availabilityofavirtualmodeloftheinfrastructure,connectedwiththephysicalmodel.

• Abilitytosupporttrainingwiththeparadigmofthe“teachingfactory”.

• Abilitytoexperimentandtestdifferentsystemcontrolarchitectureandsolutions.

• Abilitytoexperimentandtestdifferentlogisticsandmaterialshandlingsolutions.

• Abilitytosimulateandtestdifferentsupplynetworkconfigurations(beinginterconnectedwithother pilot plants ).

• Abilitytousesimulationtoolsinordertoensureoptimizationandrobustnessofequipmentandmanufacturing processes.

• Abilitytomodelandunderstandconsumers.

Description of technical parameters There are many technical aspects of the proposed Platform that need to be considered. Important decisions have to be made regarding dimensioning of workspace, computers, hardware, software and related equipment and their specifications. Important parameters to be considered are :

• Size/Overalldimensions:Neededarea(m2 ) for :

– Computers, sensors and related equipment.

– Workstations.

– Control rooms.

– Conference Rooms.

– Maintenance and Service.

• Hardware : Required computer related hardware specifications :

– CPU speed.

– RAM memory.

– Hard drive capacity.

– Network capacity.

– GFX equipment ( graphic boards )

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• Hardware:RequiredVR/ARrelatedspecifications:

– 3D projectors for stereoscopy

– Stereo glasses for stereoscopy

– Head Mounted Displays and/or see-through helmets for VR/AR

– Interaction devices as 3D mouse-like devices

• Software : Required computer related hardware specifications :

• RelevantOperatingSystems:

– Windows.

– Linux.

– Android.

– Etc.

• Relevantmodelingtools:

– Process/control tools : Matlab, Simulink, Fortran, Python, Dymola and Aspen.

– CFD tools : Comsol Multiphysics, Ansys Fluent, CFX and OpenFoam.

– VR/AR tools : Virtual Factory, ATOMIC Authoring Tool.

– 3D modeling tools : 3D Studio Max, Maya, Blender

– 3D file formats conversion tools : Okino Polytrans, SAP Visual Enterprise

– DBMS.

– SW development tools : compilers as Microsoft Visual Studio and other IDEs ( Integrated Development Environment ) depending on the languages

( A proper survey of existing software tools must be made as part of the realisation of the Platform. These are merely examples of applicable tools )

• maintenance and Service : Maintenance and service includes :

– Computers and related equipment.

– Software installation and upgrades.

– Hardware installation and replacement.

– Technical personnel.

– Building maintenance.

applicable standardsStandards will be a core issue in the platform. Relevant examples of standards for the proposed Platform :

• Standardsforexchangeofproductmodeldatabetweenvarioussimulationsystems(e.g.STEP).

• StandardforModelingandSimulationHighLevelArchitecture(IEEE1516–2010).

• Etc.

Difficulties / limitationsPossible technical difficulties could be :

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• Muchofdatacollectionwillcomefrompilotplantsetc.andwillbedependingontheuseofsensors,data mining, and other technical issues related to the description of Platform 1.1 and 1.2.

• IPRissuesmighthinderthefullvalueofopeninnovationmindsetforsharingmodels.

• OpenInnovationmindsetmightclashwithcommercialinterestsfromstrongglobalsoftwareproviders.

• ThedataandtechnicalequipmentatthePlatformmustalwaysbestate-of-art.

• Implementationofdifferentstandards.

• Integrationofheterogeneoushardware/softwaresystemsand/orplatforms

Safety issuesThe proposed Platform will deal mainly with computers and computer software. Small risks associated with the handling of electric equipment can be expected. The working environment and its ergonomic factors need to be considered for optimal productivity and health of users.

legal and technical approvalThere are no specific legal aspects of the current Platform that may be mentioned. The technical aspects of the Platform, i.e. infrastructure, communications and computers will however need inspection and approval.

location The Platform for VA/AR for simulation and training will have two levels. There will be a virtual “data-level” where relevant data and models are accumulated in an accessible cloud-based database. On the physical level there will be a number of training centers located around Europe. In many cases it will make sense to locate these as an integrated part of the pilot plants and pilot size factories described in Platform 1.1 and 1.2, but it can also be meaningful to integrate the platform with existing research institutions that are strong and experienced within the field of VR/AR. Importantly, irrespective of location, the ability to have an interplay between the virtual training and simulation and the actual tangible effects on equipment or raw material are considered important.

estimated costs It is difficult to estimate cost for this platform at this early stage. The development in technologies related to VR/AR is widespread and many prices are continuously lowered. Parts of the Platform elements described here will be an upgrading of existing RI elements/infrastructures or linked to pilot plants. Building of these will cause initial and running costs. An estimated calculation will not make much sense at this stage of the project but will have to be elaborated in the next steps towards realization of the Platform. However, the costs structure can be divided into these elements :

• Officebuildingrelatedcosts

• Computer/Hardwarerelatedcosts

• Softwarerelatedcost

• HRrelatedcost

Human resources and training and technology transfer facilitiesThis chapter deals with human resources in general, those operating the Platform and those that can be trained by this Platform.

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StaffThis Platform requires ICT based experts for developing or ensuring quality of the models and system approach in combination with the relevant experts on the application in order to have the right processes and physically relevant parameters inside the models. The Platform is an ideal meeting place where the ICT experts from universities and research institutes can be coupled to relevant experts from industry to provide insight in the application. Additional staff is also needed in order to provide maintenance of the physical and technical parts of the Platform.

Competence and servicesThe proposed Platform may offer and absorb competences within the field of VR/AR and modeling from ICT experts, academia and industrial experts. Technological transfer between the Platform and industry can be ensured through training and organization of courses, seminars and workshops, targeting specific areas of interest. Further, the Platform may also provide consultation within the field of food processing and manufacturing for the benefit of companies.

Possible connections with EuroVR ( European Association for Virtual and Augmented Reality ) and other relevant organizations are also foreseen to be a valuable task in the Platform.

References[1] Stork S, Schubö A ( 2010 ) Human cognition in manual assembly : Theories and applications. Advanced Engineering Informatics 24 : 320-328.

[2] Bowling S, Khasawneh M, Kaewkuekool S, Jiang X, Gramopadhye A ( 2008 ) Evaluating the effects of virtual training in an aircraft maintenance task. International Journal of Aviation Psychology 18 : 104-116.

[3] Malmskold L, Ortengren R, Carlson BE, Svensson L ( 2007 ) Virtual training towards a design framework. In : Proceedings of World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education. Chesapeake, USA.

[4] Morkos B, Taiber J, Summers J, Mears L, Fadel G, et al. ( 2012 ) Mobile devices within manufacturing environments : a BMW applicability study. International Journal on Interactive Design and Manufacturing ( IJIDeM ) 6 : 101-111.

[5] Benbelkacem S, Zenati-Henda N, Belhocine M, Bellarbi A, Tadjine M, et al. ( 2010 ) Augmented reality platform for solar systems maintenance assistance. In : Proceedings of International Symposium on Environment Friendly Energies in Electrical Applications. Ghardaia, Algeria.

[6] Zauner J, Haller M, Brandl A, HartmanW( 2003 ) Authoring of a mixed reality assembly instructor for hierarchical structures. In : Proceedings of International Symposium on Mixed and Augmented Reality ( ISMAR ). Washington, USA : IEEE Comput. Soc.

[7] Henderson SJ, Feiner S ( 2009 ) Evaluating the benefits of augmented reality for task localization in maintenance of an armored personnel carrier turret. In : Proceedings of International Symposium on Mixed and Augmented Reality ( ISMAR ). Orlando, USA.

[8] Andersen M, Andersen R, Larsen C, Moeslund T, Madsen O ( 2009 ) Interactive assembly guide using augmented reality. In : Proceedings of

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5.10. Business models and food manufacturing strategies

Short description This platform will carry out research and provide innovative results, data, methodological support and training to industry for systematic evaluation and exploitation of the business potential of new technologies and manufacturing solutions for food applications. It will complement the activity of the other RI platforms, that will deal with specific technological topics, by providing a “business layer” that will allow companies to properly address innovation inside their manufacturing processes, to develop innovative business models based on new technologies and, on the other hand, to dictate technology requirements to the other platforms in order to enable new promising business models.

• Theplatformaimstosupportfoodmanufacturingindustryin:

– carrying out research and innovation activities ;

– defining market scenarios, data analysis, historical and experimental information ;

– business models simulations and methodological support ;

– training managers and employees on strategic issues and identified business models.

• Theplatformwillsuggestandsupportwithabusinessperspectivenewtechnologiesdevelopmentto allow the deployment of new strategies for food manufacturers and technology providers in order to increase their business.

• Theplatformwillidentifyandevaluatesupplychainmodelsandtechnologiesabletosupportfoodmanufacturers in the implementation of a global sourcing and distribution strategy.

activities and value proposition The main activities that this platform will carry out will be :

• permanent observatory of technology and business model innovation in food processing and of manufacturing solutions for the food sector.

• Collection of best practices and new business models in processing and manufacturing solutions for the food sector.

• Developmentofqualitative,quantitativemodelsanddecisionsupportsystemsandmethodsassisting companies in :

– defining the business strategy and the values which are distinguishable and potentially unique ( value proposition ), which can be offered for different user/ stakeholder segments and for the society, on which their competitive advantage can be built ;

– estimating economic, environmental and social benefits ;

– designing business processes, pricing mechanisms, supply chain and organizational structure to produce value and deliver benefits to customers through products and services that are accessible and affordable to the users/stakeholder segments ;

– managing innovation risks.

• Marketresearchatallsupplychainlevelstoexplore:

– the potential of product, process, market, supply chain and organizational innovations ;

– new consumers needs , thus new markets for food manufacturers ;

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– new technology needs by food manufacturers, thus new markets for equipment suppliers and technology providers ;

– new needs of equipment suppliers and technology providers, which will represent triggers for new research activities ;

– best practices & new ways of collaboration and networking between supply chain members, particularly between technology providers and food companies.

• Testingofnewtechnologydevelopments and technical solutions in order to collect data and forecast reliable performance indicators allowing to estimate the potential of technology and business models.

• Businessintelligenceactivityon:

– regulations ( technical regulatory ) ;

– relevant business movements in the food and manufacturing sectors ;

– latest technology developments available to companies and emerging technologies ;

– success stories.

• Provisionofservicesonmeasuringperformanceofmachineryandtechnology,datacollecting,structuring knowledge and consultancy on that

• Provisionofaplaceandfacilitatingnetworkingonthedevelopmentandapplicationofbusinessmodels.

The benefits for users ( both technology/service providers and food manufacturers ) will be :

– to gain a holistic business, market and technology perspective in the food manufacturing sector by integrating information that are usually dispersed in different research centres, countries and companies.

– to have constantly available business-related information useful to operate consciously in the market, to make reliable forecasts, to react fast to changes and to trigger internal innovation processes according to the change of the context.

– To understand what are the key-success factors and barriers of technology and business innovation and to design the innovation process and exploitation strategy accordingly.

– to access best practices, success and failure stories of companies that undertake strategic, managerial and technological innovation. This will make companies aware on the suitable paths to follow and on the difficulties that they will encounter when they exploit innovation in strategy or technology.

– To access data for benchmarking.

– To have a research infrastructure available to run real processes in the industrial laboratories in order to estimate reliably performance parameters, which are usually theoretically estimated in current research practice.

– To improve current competences in food technologies and manufacturing to build up a strong network of excellence on such topics.To exploit the business potential of innovation and to receive inputs and incentives to target further technological research.

– To access to a network of peers, customers and suppliers where experiences, challenges and difficulties can be discussed.

– To explore the potential of new approaches and technologies through the use of joint funded programs, thus sharing the risk ( which would be too high for single companies ).

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Integration of existing research infrastructures Considering the mission of this platform, it will be constituted by a group of core nodes developing research on food manufacturing business models, management strategy, processes and sustainability as core mission. To accomplish this task, which implies the collection and manipulation of a wide and multi-disciplinary set of data and information, the platform will necessarily be linked to all the platforms of the food manufacturing RI dealing with technological and manufacturing research, in order to exchange technology-related information that is needed to carry out the business research. Thus, the platform will be constituted by a set of core-nodes closely interconnected, linked with the rest of other platforms.

The following research infrastructures were identified as European potential candidates to become core nodes of the Business models and food manufacturing strategies platform, since they embrace ( also ) a business-oriented approach to food manufacturing research. They are below classified per area of specialization :

• Infrastructuresdealingwithstrategy,businessmodelandmanagementresearch

– CNR-ITIA : manufacturing business models and supply chain ;

– INESC : network management and operations management ;

– IRTA : agro-food economics ;

– Leatherhead : marketing intelligence in the food sector ;

– SIK : marketing, information and sales in the food sector ;

– WUR : market and chain strategies in the food sector ;

– UGENT : marketing, network and chain management in the food sector ;

– High Tech Europe : innovation and knowledge management in the food sector ;

– Campden BRI : food technology and related business strategies along the food chain ;

– ETP Food for Life :up-to-date balanced view on industry led strategic research and innovation agenda in the food sector ;

– Manufure ETP : Up-to-date balanced, strategic roadmap on research and innovation in the manufacturing sector.

• Infrastructuresdealingwithsurveysandobservatories:

– From the business and innovation side : - EMS : European survey on manufacturing innovation ( with focus on technology and services ) ; - CIS ( Community Innovation Survey ) : European survey on organizational innovation ( with

focus on organization and management ) ; - OECD Oslo Manual.

– From the social side : - ESSi : European survey improving social measurement ; - BCFN ( Barilla center for Food and Nutrition ) : multidisciplinary approach , idea observatory on

economic, scientific, social and environmental factors that have an impact on the food chain and on the wellbeing.

• Infrastructuresdealingwithregulatoryresearchandservices

– Leatherhead

– Campden BRI

– FERA

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For all the aspects of interconnection between the economic, social and the environmental side of sustainability, the platform will be strongly linked with the “Sustainability Assessment of Food technologies, products and value chains”.

The mentioned research infrastructures can provide existing databases on business models, strategies, management practices, economic, environmental, social measures and regulations focusing on the joint business potential of the food and manufacturing sectors. However, many of them were not created to address specifically the food manufacturing sector or the manufacturing solution providing sector issues. Consequently, their mission, objectives, investigation areas and methods should be extended and modified to fulfil the needs of the new target. In addition, a common methodological integrated framework should be designed to offer reliable and coherent information and research capabilities. Finally, elements that are missing in the existing infrastructures are pilot plants enabling companies and end users to test new technologies and processes, as well as new business models associated to them ( for example reconfigurable technologies for food manufacturing machinery ). Besides the research activities, the new RI will develop a new model of pilot plants network where both existing and new public and private pilot plants are integrated and interconnected for demonstration purpose and scale-up technology, sharing expertise on a non- competitive base.

Due to their multi-disciplinary nature, thematic complementarity and multi-annual tradition, including these infrastructures in the new food manufacturing RI would offer the possibility to integrate a wide set of information and research capabilities to build a holistic business perspective in the food manufacturing sector.

The design of the infrastructure will move from the results and structures already established by previous and on-going EU research ( such as for example, “Truefood”, “Leonardo – Open New Food”, “Capinfood”, “Explore” EU projects ).

technical requirementsIn the previous chapters we have analysed what the new platform should deliver, which existing infrastructures might contribute to this platform and which elements are missing. This chapter will describe the physical and technical specifications and requirements for new RI elements needed. It includes the description of the expected functions and the technical features and the estimated costs if applicable.

Scope of applicationThe platform aims at building business support, concepts, methods, information and tools for the food processing and manufacturing solution provider sectors in general. Thus, its application is suited to all stages of the food manufacturing chain and to all the related processes and supporting technologies. In particular, the platform will investigate business phenomena and the business impact of technologies at customer/consumer level, food manufacturers side and technology suppliers side ( delivering enabling technologies and solutions to food manufacturers ). In this way, all the relevant actors determining the success of food manufacturing processes will be included. Obviously, the coverage of all the application areas will require time for developing facilities, databases and competences. As important added value, the new RI will give a holistic business point of view on food manufacturing which will overcome the current fragmentation and the local supply chain optimization approach will be a relevant added value offered by the new RI.

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expected functionsThe platform will offer added value to the users because it will have the capability to :

• Buildreliable,complete,detailed,constantlyupdateddatabasesandbusinessscenariosspecific to the food manufacturing sector including a wide set of data on :

_ consumer needs and behaviour, food processing and manufacturing solution providing businesses ;

– the industrial structure, the business performance and sustainability indicators of the food manufacturing companies ;

– the adoption of enabling technologies by food manufacturing companies ;

– the strategies, business models, management practices and tools adopted ;

– the environmental and social impact of food manufacturing processes and technologies ;

– performance and operation data of machinery ;

– best cases, success stories, benchmarks ;

– regulations and normative trends ;

– business intelligence data ( mergers and acquisitions, personnel search, market actions, etc. ) ;

– observatory on the state of the art of emerging technologies.

• Tobuildthesedatabases,thecorenodesoftheplatformwillhavetobeconnectedinrealtimewitha wide set of other RI nodes and platforms that will provide the sub-part of information they are specialized in, under a common coherent frame of information structure that will be designed on-purpose.

• Processdataandinformationwithscientificandstructuredmethodsandsoftware,takingbothaqualitative case-study approach and a quantitative econometric approach ;

• Developqualitativeandquantitativedecisionsupportmodelstailoredtothefoodprocessingand manufacturing solutions provider sectors useful for companies to take managerial decisions ( dealing with organization, networked supply chain, product development, global sourcing, finance, risk management, distribution and sales, etc. ) ;

• Testthesustainabilityofnewstrategiesandbusinessmodelsbothbysimulatingtheireconomicand environmental impact, and by testing in laboratories the performance of new technological solutions developed for new business models ( for example, reconfigurable machines and transport systems, new machine features, emerging technologies, etc. ).

Description of technical parameters• Datacollectionsystemconnectedtolaboratory-scalefoodprocessingequipmentembedding

innovative manufacturing solutions in order to provide reliable performance data and parameters to estimate impacts at business model level.

• Connectionwithmachineryoperatinginrealfactoriestocollectoperationdataandmachineryperformance parameters during the phase of use.

• Realtimeweb-basedconnectionofcoreplatformnodeswithnodescollectingcomplementarydetailed data and information ( on environment, regulation, etc. ) ;

• Softwareroutinestoprocessinformationandperformstatisticalanalysestointerpretdataatbusiness level ;

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• Stockofmachines,replacementpart,components,modulesandreplacementmachinesforatleast three pilot testing cases for each business model and for demonstration on models and real products, collecting data for calculation of fees and costs.

Difficulties / limitationsTwo main types of difficulties will be faced when building and operating the platform. The first one deals with the possible reluctance of companies to share business and strategic information, which is very sensitive. To cope with it, confidentiality will be guaranteed to companies. The possibility to receive summary of results and periodic bulletins will be offered as incentive to participate in the surveys and to the observatories and organizations providing data from the field. In addition to that a significant amount of useful information and methodological knowledge can be collected from other sectors without the fear of disclosing confidential information which help to understand the concepts.

The second difficulty will deal with the building-up of infrastructures necessary to test new technologies in order to provide performance parameters to assess their positive impact at business level. Due to the variety of possible technology types and to the unpredictability of the future technological innovations that will be developed, it is not possible at present time to build-up a research infrastructure ready to test future technological innovations. To cope with this difficulty, the approach of the platform will not be building a centralized testing facility for business model simulation, but to establish connections with all the platforms of the RI that will be equipped with different type of innovative technologies. This will facilitate collecting from these nodes the type of information that will be necessary for business simulations. To this purpose, equipment should have the capability to register and communicate performance data to the business model platform.

location As above discussed, the platform will be composed of already existing nodes that will have to be upgraded and interconnected.

estimated costs The cost for setting-up the platform will include :

• Thecostforconnectingexistingnodesinordertoexchangereal-timeinformation;

• Thecostforupgradingexisting“monitoring”nodesinordertoextendtheirmonitoringcapabilityto food manufacturing companies ( both at company and technical machine level ) and to a real European dimension ;

• Thecostofsettingupsoftwareroutinesandmodelsfordatamining;

• Thecostforsetting-upanewwidecoherentdatastructurewhichwillovercomethefragmentationof the of single nodes ;

• Thecostforequippingexistingpilotplantswithstockofmachines,replacementpart,components,modules and replacement machines to simulate new business models ;

• Thecostforengagingresearchersneededtomaintainthedatainfrastructure,toruntheanalysesand to provide the service to stakeholders :

• Thecostofasmallgroupofskilledstaff.

• Theestimatedcostoftheinfrastructurewillbeof1–1,5millioneuroforinitialinfrastructuresetup ( considering the needed hardware in terms of interconnection IT systems, machinery and other laboratory staff ), and of 1,5 - 2 million euro for yearly personnel cost.

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StaffTo run the platform it is foreseen that a total of 40 researchers will be engaged. It is estimated that five of them will be engaged full-time to push platform activities, while the rest will offer a part-time support, covering a defined set of regionally-based or thematic data, based on their specialization and regional location. They should deal with :

• overalldesignofsurveydata,collectionmethods,econometricmodels;

• designandexecutionofcasestudies;

• administrationofthesurveysbyidentifyingandcontactingcompanies,following-upanswers,etc.

• collecting,structuring,updatingknowledge;

• softwareprogrammingforeconometricanalyses;

• managementandoperationofthecommunicationandnetworkinginfrastructurefortheinterconnection of different nodes and sensor network ;

• monitoringregulationsandrelatedtrends;

• monitoringandreportingmarketdata,businessnotices,etc.(businessintelligence)

• elaborationanddiffusionofperiodicreportsandbulletins;

• “desingofexperiments”forbusinessmodel-orientedtechnologytesting;

• projectmanagement;

• supporttotestsexecution;

• provisionofon-demandservicesandbusinessconsultancy;

• managetheparticipationofplatformininternationalR&Dconsortia;

• preparestrategicdocumentsforthefoodandmanufacturingsector;

• lobbyingactivities.

• Theseresourcesshouldmainlyhavetheprofileofindustrialmanagementengineers,machinerybuilding/maintenance specialists, sensor and data collection specialists, food engineers, statistics engineers and industrial economists.

Competence and servicesThe competences of this platform will allow the provision of the following services :

• Detailedsectorialreportsontechnologyadoption,innovationandtrendsinthefoodmanufacturingsector ;

• Benchmarkingandbestpractices;

• Businessintelligenceservice;

• Regulationadvisoryandinformationservice;

• Consultancyonfoodmanufacturingstrategies,businessmodelsandmanagementpractices;

• Businessplanningandtechnologyplanning;

• Datacollectionsystemdesignandevaluationservices;

• Leasingmobilesetsofmeasuringsystems;

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• Networkingservices;

• Fundingservices;

• Technologymapping(“who’sdoingwhat”infoodtechnology);

• Foodtechnologyscoutingservices;

• Networkingserviceamongpotentialsuppliers,customersandresearchers.

training and technology transfer facilitiesThe platform can offer training on business models, strategies and management practices, telemetrics, data collection from remote places in food manufacturing and machinery building. Besides traditional training, taking advantage of the wide set of data and case studies that the platform will own, advanced workshops will be organized based on real industrial cases in order to simulate challenging industrial situations and to provide specific business training for the food manufacturing and machinery building industries.

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5.11. Sustainability assessment of Food technologies, products and value chains ( SaF )

Short description The SAF RI platform offers capabilities, services and activities for the life cycle sustainability assessment of food technologies, products and the greening of the value chains. In particular, it develops, applies, manages and transfers methods, models, tools ( software, databases ) and technical guidelines for the quantification of the environmental social and economic impacts, based on a life cycle approach. Moreover, it operates and manages high performance computing facilities.

SAF operates at three levels :

• Performingresearchanddevelopmentofnewmethods,modelsandtools;

• Trainingandtransferringknowledge,methodsandtoolstoservice’snetworksandconsultancyfirms, so as to meet the requests of the whole European food industry ;

• Providingservicestofoodindustry

SAF has to be able to integrate existing and new methods and models that have the potential to contribute to a broader and in-depth life cycle analysis of environmental, economic and social aspects of the food industry. Moreover, it has to be able to satisfy the SME requests of tailored methods and tools, user-friendliness, standardization and transparency.

SAF supports the sustainable design and development of new products and food technologies ; assesses the environmental, economic and social impacts of food products ; supports the efficient and trustable communication of the sustainability quality of the food products.

activities and value proposition Main beneficiary of the RI platforms will be the food industry. Therefore, SAF offers the following activities and services to its target clients :

Researchers will benefit of the creation of knowledge, methods and development of models and tools ( high performance computing facilities, software, databases ) for exploring, developing and analyzing different concept methods and models for Food Sustainability Assessment, sharing their data, results and options. In this way they will able to answer the demand for broadening ( i.e. adding social and economic aspects ) and deepening ( adding mechanisms and scientific rigour ) life cycle analysis taking into account the peculiarity of the food sector. Moreover they will develop also tailored approaches for food industry purposes ( web-based systems offering not only software tools and data base but also comprehensive information, training and tools and the access for specialised services ).

Service and consultancy firms can access high-specialised training on and technology transfer of SAF assessment instruments. Moreover, they can access updated database on food ingredients and technologies, including transport and packaging. Therefore, service and consultancy firms will be able to provide sustainability assessment and related communication services to food enterprises at the most updated level, timely and at a lower price.

Industry with internal competencies on sustainability assessment will be able to perform comparison among technologies, products and suppliers assessment, support strategic decisions, develop more sustainable products thanks to the availability of specialised and easy to use eco-design, assessment and communication tools supported by sector-specific and pre-elaborated data bases, technical guidelines, information and training web-based facilities.

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Industry without internal competencies on sustainability assessment could obtain similar results through service and consultancies firms, trained on SAF.


Industry will benefit the availability of methods, models and tools to evaluate the performance of its products, of current and new technologies, in order to identify areas of improvement, to implement practical solutions and to provide sustainability information to consumers. These instruments will be developed taking into account the requirements of robustness and reliability on the one hand, and the user friendliness on the other hand.

Service and consultancy firms will have access to updated methods, models and tools. Moreover they can deliver high-qualified services to their associates, able to make them competitive in the European market and to position them among the front innovators in Europe.

Researchers will benefit the integration of different disciplines, the sharing of and the knowledge creation arising from the debate on different methods, concepts, models, computing facilities, data etc. Moreover, taking into account that Horizon 2020 is requiring for Technology Readiness Level ( TRL ) 4 and higher an LCA, SAF is the one-stop-shop where to find everything is required for the food sector. For TRL lower than 4, SAF can however support the developers of new concepts with sustainability- and eco-design criteria.


By developing an integrate structure based on models, methods, concepts and data able to provide robust and reliable food specific studies on SAF

By providing a food specific web platform able to meet industry needs of assessing their performance, training and information


Therefore :

Which existing pilot plants, pilot size factories and research institutions in europe are dealing with this topic ?

The list is quite long. In the following they are grouped around three main categories. For the details, it is suggested to consult the D 3.10.

• (elementsof)RIsthatprovideservicesandresourcesforperformingspecifictasksoftheSAF.Forexample, given the need of harmonising and better developing methods for the assessment of the carbon footprint ( CF ), RIs exist that work of the carbon cycle and on climate change. As such, they do not develop CF methods but the knowledge they develop will feed into the CF calculation, making the assessment scientifically robust.

ICOS ( Integrated Carbon Observatory System )

InGOS ( Integrated non-CO2 Greenhouse gas Observatory System )

IMECC ( Infrastructure for the Measurement of the European Carbon Cycle )

LifeWatch

EXPEER ( Distributed infrastructure for EXPErimentation in Ecosystem Research )

ECSIN ( European Center for the Sustainable Impact of nanotechnology )

ESSi ( European Social Survey Infrastructure improving social measurement in Europe )

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EPLCA ( European Platform on Life Cycle Assessment )

• (elementsof)RIsprovidingphysicalresourcesthat,initiallydevelopedforotherfieldsofscience,can be used for sustainability assessment.

CENDARI ( Collaborative European Digital/Archival Infrastructure )

HPC-Europa2 ( High Performance Computing )

• (elementsof)RIsspecificallydevelopedforthefoodsectorthatcanbeeasilyadopted.

UT-AGRI ( Technical Unit for Sustainable Development and Innovation of Agro-Industrial System – ENEA ).

Which main RI elements are existent in those institutions already and which elements are explicitly missing ? In the area of sustainability there is a growing requirement of data and information. Currently, most of the data is stored locally, and cannot be shared with the rest of the scientific community. Therefore, there is a large demand for RIs that connects the already existing infrastructures.

Overall, all the RIS listed above have already a good capability in terms of sharing and storing data. Among these the following are highlighted :

• DistributedInfrastructureforEXPErimentationinEcosystemResearch(EXPEER)isaprojectwhich federates already existing infrastructures in the field of Ecosystem Research, in order to improve the research capacity. Also, EXPEER facilitates access to experimental and observational platforms as well as analytical and modelling facilities.

• TheEuropeanSocialSurveyInfrastructure(ESSi)improvessocialmeasurementinEuropeandprovides an infrastructure for such data and methodologies.

• WithinEPLCA,theILCD(InternationalReferenceLifeCycleDataSystem)DataNetworkisunderdevelopment. It is a web-based infrastructure allowing convenient online access to consistent and quality-assured life cycle inventory ( LCI ) data sets from various providers, globally. The ILCD Data Network is designed as one-stop-shop for life cycle data in a policy and business context. Datasets quality within the ILCD DN is ensured by the development of the ILCD Entry-Level requirements.

What would be strength and weakness of the existing institutions mentioned if we include them in this RI platform ?The available RIis in Europe listed above would potentially fulfil some of the needs and challenges related to the sustainability assessment in the food industry, but they do not cover the whole spectrum of topics required to perform the SAF.

An interdisciplinary approach is necessary, which requires the connection of the existing RIs and the following additional actions :

• Broadtherangeoftopicscovered,bothwithineachdomainofsustainability(e.g.ecosystemservices, toxicity ) and also including other aspects belonging to the economic and social sphere ;

• Increasethenumberofon-lineandcross-datafordifferenttargetofusersandthespeedytoaccessthem, making them available to a wider science community ;

• Increasethecomputationalpowerandabilityforrunningsimulationsandmodels.

• Increasetheaccesstoon-linedatamodelling;

• Strengthenthecollaborationbetweencomputingserviceprovidersandresearchers

• Increasetheuseofcomputationalmethods,simulationsanddataanalysis,largevolumeofdataandcomplex data processing

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Scope of application• SAFRIisofinterestforthewholefoodsector.Consideringthatmostoftheimpactsoffoodproducts

occur in the primary production phase, the agricultural sector is within the scope of the analysis, but the SAF RI will not directly address the research in agriculture.

• RelevanttypesofoperationofSAFare:

• Modeldevelopment,Datacollections,computing,trainingetc.

• SAFwilladoptandfurtherdevelopexistingmethods,suchasProductEnvironmentalFootprint,Organisation Environmental Footprint, ENVIFOOD Protocol, Life Cycle Assessment, Social Life Cycle Assessment, Life Cycle Costing, and other life cycle based methods.

expected functions• SAFmainoperationalfunctionsare:

• Collection,managementandconstantupdateofLifeCycleInventorydatabase

• Softwaredevelopment,dataprocessingandcalculation

• E-infrastructureenablingoperationofnetworks,databases,computingfacilities,collection,screening and structuring information

• Operationofe-learningplatform

Description of technical parametersSAF will be organised similarly to an European Topic Centre ( ETC ), i.e. a network of existing research institutes but centrally managed with common quality assurance and quality control system. Differently from ETC, SAF will have not a single customer but a very large number of clients ranging from research, to industry and service providers.

applicable standardsThe following standards are related to the methods and models developed and used for performing the SAF :

ISO 14000 series

ISO 14020 series

ISO 14040 series

ISO 14060 series

ILCD Handbook

Product and Organisation Environmental Footprint ( PEF and OEF )

ENVIFOOD Protocol

Guidelines on Carbon and Water Footprint ( e.g. PAS 2050 and its sector specification ; GHG protocol ; International Dairy Federation )

etc.

Difficulties / limitationsThe main difficulties in building up or implementing SAF are :

• ThehighdegreeofinteractionneededamongthedifferentexistingRIs.

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A sine qua non condition for developing SAF is the knowledge sharing, from which new approaches and ideas can be generated. In fact, given that the development of a SAF approach ( and related methods, models and tools ) requires new scientific paradigms and a deep knowledge of all the main fields involved ( from social science to engineering ), the collaboration among the different research organisations is of paramount importance. However, this collaboration and dialogue could not be straightforward, given that the process of knowledge creation in the last years occurred in terms of high and vertical specialisation in defined fields.

• Theknowledgedevelopment.

The life cycle-based sustainability assessment requires also the understanding of complex phenomena, which still are under study in many disciplinary sectors. Moreover, even when the phenomena are known, their representation is challenging. From the computational and modelling point of view, in many cases it is necessary to leave the steady-state and linear approach, and to move towards dynamic models, managing at the same time the uncertainty generated and the transferability of the results obtained to the final users ( being policy makers or business managers ).

Safety issuesVideo Display Terminals risks

legal and technical approvalThe only requirements to be met refer to the licenses for the software tools and databases. Specific hygienic design standards do not apply.

location Depending on the RI element described in this template it might be the case that it is very obvious where it will be placed ( e.g. in case it completes the structure of an existent RI ) but it can also be the case that there might be different option where to place the new RI element.

Discuss in this chapter the possibility where to place it and justify your decision.

This RI is envisaged as structured along two main elements : i ) a core module ; ii ) a series of nodes, virtually linked each other.

The core module can be physically located in any European countries, preferably close to already existing infrastructures with which a stronger collaboration is envisaged ( e.g. EPLCA ). The nodes are ( already ) existing RIs, each operating in specific fields of expertise and applications, but connected together by the SAF overarching goal.

estimated costs The RI element describe in this template will be an upgrading of existing RI elements/infrastructures. Building of these will cause initial and running costs. Indeed, the calculation is not very easy at this stage of the project. But it might be possible to go for two scenarios ( optimistic vs. pessimistic ) to give an indication about expected costs.

• AtthebeginningSAFshouldbeabletodeliver20–30person*yearoftotaleffort,requiringanannual budget of 1,6-2,4 M€.

• Investments,intheorderofmagnitudeof0,5-1M€,willberequiredforthecomputinginfrastructure and for the organisation and implementation of databases.

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Human resources and training and technology transfer facilitiesThis chapter deals with human resources in general, those operating the RI element and those that can be trained by this RI element.

StaffThe RI requires competencies in different fields, given the trans disciplinary nature of SAF. Besides technicians expert in agriculture, food processes and technologies, the following skills and competences are required : engineering, social science, earth science, economic ( micro-macroeconomics, econometric ), statistic techniques, deep knowledge on life cycle-based methods, computation and modelling techniques, development of indicators, data managers.

At the beginning it will be able to deliver 20 – 30 person*year of total effort, as technical staff.

Competence and servicesThe RI element will have unique competences relevant for the food processing and manufacturing industry. How can these competences be used to offer specific services regarding training, technology transfer or other activities.

As described under the heading “Activities and value proposition”, this RI does not carry out only research activities, but it offers specialised services to industry, service and consultancy firms. In particular, to the industry the RIs will offer both the analysis of SAF and tailored methods, models and tools to evaluate the performance of its products, of current and new technologies, in order to identify areas of improvement, to implement practical solutions and to provide sustainability information to consumers. These methods and tools will be available also for service and consultancy firms, which in turns could use them for delivering specific services to their associates/clients, after a structured training.

training and technology transfer facilitiesTraining and technology transfer is a central activity of SAF, because it is necessary a continuous process of capacity building in order to develop an adequate network of consultancy and service firms, in order to meet the demand from the food industry. Most of the training and technology transfer shall be based on ICT tools, but SAF will also organise summer schools, will host stagers and researchers in training, in particular for the most advanced and complex sustainability assessment methods. Marie Curie actions, in cooperation with industry and academia, will be instruments for researcher training and exchange.

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vII. annex: aBBRevIatIon

a annual, per year

ANN Artificial Neural Network

CCD Charge-coupled device

CDR Conceptual Design Report

CENDARI Collaborative European Digital/Archival Infrastructure

CMDD Component- and Model-Driven Development

DFD Dark, Firm and Dry meat

DIL German Institute of Food Technology

ECSIN European Centre of the Sustainable Impact of Nanotechnology

EGI European Grid Infrastructure

EIT European Institute for Innovation and Technology

EMS European Manufacturing Survey

ENEA Italian National Agency for New Technologies, Energy and Sustainable Economic Development

ENVI Environment for Visualizing Images

ESFRI European Strategy Forum on Research Infrastructures

ESSi European Social Survey Infrastructure

ETP Food European Technology Platform Food for Life

ETP MANUFUTURE

Food European Technology Manufacturing

EXPEER EXPErimentation in Ecosystem Research

FACCE-JPI Joint Programming Initiative on Agriculture, Food Security and Climate Change

FI Future Internet

FIR Far infrared light

FI-WARE Cornerstone project of the Future Internet Public-Private Partnership (www.fi-ware.eu)

Food TIP Food Tech Innovation Portal

FP7 7th Framework Programme of the European Commisison

Horizon 2020 EU Research and Innovation programme of the European Commission (2014 to 2020)

HPC-Europa2 High Performance Computing Europa2

HSI Hyperspectral Imaging

ICT Information and Communication Technology

IMECC Infrastructure for the Measurement of the European Carbon Cycle

INEGI Institute of Mechanical Engineering & Industrial Management

INESC Porto Institute for Systems and Computer Engineering of Porto

InGOS Integrated non-CO2 Greenhouse gas Observation System

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IP Intellectual Property

IPR Intellectual Property Rights

IRTA Institute for Food and Agriculture Research and Technology

IS-ENES Infrastructure for the European Network for Earth System Modelling

ITIA-CNR Institute of Industrial Technologies and Automation, National Research Council

JTI Joint Technology Initiatives

KIC Knowledge and Innovation Communities

LCA Life Cycle Assessment

MIR Middle infrared

MSI Multispectral Imaging

MWIR Mid-wavelength infrared

NIR Near-infrared light

NoE Network of Excellence

PCA Principal Component Analysis

PIBE Piattaforma Integrata per l’uso di Biomasse e rifiuti di origine vegetalE

PIEP Pole for Innovation in Polymer Engineering, private non-profit organisation

PLC Programmable Logic Controller

PLS Partial Least Squares regression

PPP Public-Private Partnerships

PSE Pale, soft, exudative meat

R&D Research and Development

RFID Radio-frequency identification

RGB Red, green and blue imaging

RI Research Infrastructure

RIS3 Regional Research and Innovation Strategies for Smart Specialisation

ROI Region of Interest

SIK The Swedish Institute for Food and Biotechnology

SME Small and Medium Enterprises

SO Strategic Objective

SRA Stragegic Research Agenda

SWIR Short-wavelength infrared

TNO Netherlands Organisation for Applied Scientific Research

TU Berlin Technical University of Berlin

UV Ultra violet light

VIS Visible spectral range

WG Work Group

WP Work Packages

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vIII. annex: aCKnoWleDGement

acknowledgementThis document represents output of FoodManufuture, a coordination and support action that has received funding from the European Community’s Seventh Framework Program under grant agreement n° 289327. The following experts, beneficiaries and people have contributed to the realisation of this Conceptual Design Report ‘Food Factory of the Future’.

Experts participated in the International Workshop for Future Scenarios Focus Group Activities (March 20, 2012, Copenhagen, Denmark, organized by AAU), in the Success Model Building Workshop (May 24-25, 2013, Brussels, Belgium, organised by ISI), the Road Mapping Workshop (September 3-4, 2013, Brussels, Belgium, organised by ISI) and External Experts Meeting of the RI Platforms (diverse meetings, organised by platform leaders):

external experts:

name of expert Institution

Adler-Nissen, Jens Technical University of Denmark

Akkermann, Renzo Technical University of Denmark

Andersen, Henrik Arla

Andersson, Yvonne TetraPak Processing systems

Arnaut, Filip Puratos N.V.

Axelos, Monique

Børresen, Torger Technical University of Denmark

Böttzau, Lars Amcor

Braga da Cruz, Isabel PortugalFoods

Brandão, Tiago UNICER

Breh, Wolfgang KIT Energy Center

Budtz Bertelsen, Michael Bisca

Carstens, John Langebæk Logistik

Cotillon, Christophe ACTIA

Daasbjerg, Martin Dansk And

Damkjær, Rene AgroTech

de Smedt, Chris NineSigma Europe BVBA

Debrauwer, Pieter TNO

Duivenvoorden, Will Royal Haskoning

Edmond, Jennifer Trinity College Dublin

Eriksen, Svend Danish Crown

Falholt, Per novozymes

Fenger, Anne Lise Fødevarestyrelsen

Fernandes, Migue FoodInTech

Firtha, Ferenc Corvinus University of Budapest

159

Friis, Alan Technical University of Denmark

Frosch, Stina

Funder-Kristensen, Torben Danfoss

Garofalo, Francesca CINECA

Gerner, Niels Foodbest

Ghiraldi, Andrea Dinamica Generale

Golob, Boris Step University of Rijeka

Golz, Peter VDMA

Grøn,Susanne Chr.Hansen

Hjørnet, Preben

Hoogerwerf, Arno CSEM

Ingelbeen, Jan Pinguin Lutosa Food group

Innings, Fredrik TetraPak

Jacobsen, Thomas Abildgaard Daloon

Jensen, Per Alan Lantmännen

Jensen, Peter Bernt Danish Food and Drink Federation

Jørgensen, Bent Aksel Xcelgo

Jovane, Francesco Politecnico di Milano

Juul, Ole Linnet

Kettlitz, Beate FoodDrinkEurope

Knowles, Michael The Coca-Cola Company

Kormelink, Felix Mars Food

Krámer, Kornél

Lammens, Veerle European Commission

Larsen, Rune K. Blue Ocean Robotics

Larsson, Magnus Arla Foods

Lassen, Bent Claudi Foodbest

Lassing, Anders JBT Foodtech AB

Lehmeier, Katharina Eureka

Lennersten, Mats GöteborgKex AB

Losó, József Mirelite Mirsa Co. Ltd.

Malmberg, Christian Lantmännen Food Sector

Mikkelsen, Jacob Private consultant

Morais, Pilar Frulact

Mortensen, Maria Aahus University

Neimann, Jacob inSPIRe

Nielsen, Bjarke Robocluster SDU

Nissen, Pia Landbrug&Fødevarer

Nørgaard, Lars Foss

Ogué, Eva IRTA

160

Olofsdotter, Maria Foodbest

Palotas, Gabor UNIVER

Petronio, Michaela Barilla

Philippon, Jean-Baptiste alimentec

Pintado, Manuela Universidade Católica Portuguesa CCG

Poiesz, Edwin Cosun Food

Quitzau, Anders

Radwanska, Magdalena Science Europe

Rocha, Pedro PRODUTECH

Soares, Pedro Valinox

Steindl, Peter Fawema, HDG

Stöber, Heyko Gestöber

Svensson, Svante Orkla ASA

Thulin fra, Pernille LincoFood

Toft, Annette Landbrug & Fødevarer

Uustalu, Ann European Commission

van Belzen, Nico International Dairy Federation

van Olderen, Dick Heinz (Innovation Center)

Vanrie, Philippe EBN

Vargáné Haluska, Adrienn Bona Farm

Verbeke, Alain The Coca-Cola Company

Verschueren, Maykel NIZO Food Research

Viniczay, Zsolt Seacon Europe Ltd

Visschers, Ronald TNO

Vits, Jeroen Agoria

Wilke, Bernd Bosch Packaging Technology

Zammit, Gine Ørnholt

Zimmermann, Karin LEI Wageningen UR

Beneficiaries and persons of Foodmanufuture:

Aalborg University, DK Alsted Hansen, Jens

Alsted, Jens

Dukovska-Popovska, Iksra

Guldstrand, Kim

Johannsen, Lone Varn

Johansen, John

Kræmmer Sparre, Marlene

Lauridsen, Henrik

161

Løkke, Søren

Lund, Morten

Madsen, Ole

Munksgaard, Lisbeth

Nordahl Jørgensen, Steffen

Pedersen, Torben

Vestergaard, Anders

ANIA, FR Gorga, Françoise

Queré, Vanessa

Campden BRI HU Berczeli, Attila

Homolka, Fruzsina

Jasper, Anita

Sebök, András

Campbell, Alan

Holah, John

Leadley, Craig

CCIS CAFÉ, SLO Medved, Pedra

CCIS, SLO Djurasinovic, Petra Medved

Zagorc, Tatjana

DIL e.V., DE Bolumar, Tomas

Franke, Knut

Heinz, Volker

Holl, Peter

Kircher, Christian

Knoch, Achim

Lickert, Thomas

Lienemann, Kerstin

Mathys, Alexander

Riplinger, Caroline

Schwing, Julia

Shayanfar, Shima

Steinkamp, Helmut

DTI, DK Hinrichsen, Lars

Jespersen, Claus Mosby

Lütje,Henning

Nørby, Merete

162

ENEA, IT Abbadessa, Valerio

Brunori, Andrea

Buonamici, Roberto

Daroda, Lorenza

Ianetta, Massimo

Masoni, Paolo

Zamagni, Alessandra

Federalimentare, IT Branni, Antonietta

Notarfonso, Maurizio

Rossi, Daniele

FEVIA, BE Gouder, Anne Christine

Louwaege, Ariane

Louwaghe, Ariane

FIAA, AT Avila, Concha

Drausinger, Julian

FIAB, ES Mesas, Mar

Mesas, Mar

Morais, Federico

FIPA, PT Queiroz, Pedro

FoodNet, CZ Koberna, Miroslav

Stejnarova, Sarka

Fraunhofer, DE Berner, Simon

Koschatzky, Knut

Meyer, Niclas

Moller, Björn

Stehnken, Thomas

Steiert, Maximilian

Wydra, Sven

INESC PORTO, PT Azevedo, Américo

Caldeira, José Carlos

Figueiredo Teles, Vasco

Pinho de Sousa, Jorge

ITIA-CNR, IT Ballarino, Andrea

Colledani, Marcello

Copani, Giacomo

Molinari Tosatti, Lorenzo

Sacco, Marco

163

SETBIR, TK Coskun, Feyza Basak

Us, Melek

Yucel, Elif

SEVT, GR Fotopoulou, Aggeliki

Papadimitriou, Vasso

SIK, SE Ahrné, Lilia

Hamberg, Lars

Kaunisto, Erik

Tibäck, Evelina

TNO, NL Debrauwer, Pieter

Tencate, Tessa

Visschers, Ronald

Uni Gent, BE de Steur, Hans

Dora, Manoj Kumar

Gellynck, Xavier

Jacobsen, Ray

Kuhne, Bianka

Molnar, Adrienn

•AalborgUniversitet(AAU),Denmark

• Campden BRI Magyarorszag Nonprofit Kortatolt Felelossegu

Tarsasag (CBHU), Hungary

•InstitutodeEngenhariadeSistemaseComputadoresdePorto

(INESC), portugal

•SpreadEuropeanSafetyGeie(SPES),Italy

•NederlandseOrganisatievoorToegepast

Natuurwetenschappelijk Onderzoek (TNO), the netherlands

•InstitutedfoerLivsmedelochBiotechnikAB(SIK),Sweden

•DeutschesInstitutfürLebensmitteltechnik(DIL),Germany

•AgenziaNazionaleperleNuoveTechnologie,l’Energiaelo

Sviluppo Economico Sostenibile (ENEA), Italy

•TeknologiskInstitut(DTI),Denmark

•UniversiteitGent(UGENT),Belgium

•Fraunhofer-GesellschaftzurFoerderungderAngewandten

Forschung E.V. (Fraunhofer), Germany

•ConsiglioNazionaledelleRicherche(ITIA-CNR),Italy

•CampdenBRI(CBRIUK),united Kingdom

For any further information on Foodmanufuture

please visit www.foodmanufuture.eu

Foodmanufuture project is a Coordination and support action funded

by the European Commission under the Food, Agriculture and

Fisheries, and Biotechnology Theme of the 7th Framework Programme

for Research and Technological Development

(Grant Agreement n. 289327).

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