Accepted Manuscript

A decision support framework considering sustainability for the selection of thermalfood processes

Thi-Thu-Huyen Do, Hans Schnitzer, Thanh-Hai Le

PII: S0959-6526(14)00399-0

DOI: 10.1016/j.jclepro.2014.04.044

Reference: JCLP 4250

To appear in: Journal of Cleaner Production

Received Date: 30 November 2012

Revised Date: 24 March 2014

Accepted Date: 20 April 2014

Please cite this article as: Do T-T-H, Schnitzer H, Le T-H, A decision support framework consideringsustainability for the selection of thermal food processes, Journal of Cleaner Production (2014), doi:10.1016/j.jclepro.2014.04.044.

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A decision support framework considering sustainability for the selection of thermal food processes

Thi-Thu-Huyen Doa,b,* , Hans Schnitzera, Thanh-Hai Leb aInstitute for Process and Particle Engineering, Graz University of Technology, Inffeldgasse 21B, A8010 Graz, Austria bInstitute for Environment and Resources, Viet Nam National University – Ho Chi Minh City, 142 To Hien Thanh, Ward 14, District 10, Ho Chi Minh City, Viet Nam

Abstract: A specific combination of rule-based technique and fuzzy analytic hierarchy process was examined for the development of a decision support framework for selecting thermal process technologies in the food industry considering sustainability. Demonstrating the process sustainability, energy indicators are especially focused and cover four aspects of energy use in the process, including energy consumption, energy efficiency, energy savings and renewable energy use. The selection comprises two steps and results in the ranking of potential technologies for a particular product. The goal is to provide decision support at an early stage of selecting thermal process technologies for the food industry.

Keywords: decision support system, sustainability, food industry, thermal process technologies

1. Introduction

Thermal food processes

Thermal processes, involving heating or cooling, remain the most important methods in processing of foods, vegetables and fruits (Tucker, 2011).

The most obvious characteristic of industrial thermal processes is their high energy consumption. Processes like drying, evaporation, pasteurization, boiling, freezing and cooling consume around 75% of the sector’s energy use (Baldwin, 2009). These processes, on the other hand, have a very high energy saving potential. The International Energy Agency (IEA) estimated the energy saving potential of the food and beverage sector as being 0.7 EJ/y in the industrialized countries and 1.4 EJ/y in developing countries, equivalent to a potential for improvement of 25% in industrialized countries and 40% in developing countries respectively (UNIDO, 2010). The energy improvement potential identified in this report shows, that consequently production costs could be reduced up to 10%.

Energy efficiency measures not only cut the fuel bills but also reduce the negative environmental impacts of the processes. The use of energy indicators incorporates some of the most important environmental pressures including green house gas emission, depletion of biogenic resources, ozone depletion, desiccation, toxicity, waste heat, noise and odor, etc. (Svensson et al., 2006). Therefore energy indicators might be sufficient to reflect the environmental impacts of the energy use as long as their aim and context are apparent (Svensson et al., 2006).

*Corresponding author. Tel.: +84 83 865 1132, fax: +84 83 865 5670, email address: thuhuyen@hcmier.edu.vn (Thi-Thu-Huyen Do).

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Sustainability appraisal in the selection of thermal food processes

The application of indicators to measure the sustainability of manufacturing processes is complicated (Joung et al., 2013) and context-specific (Zeijl-Rozema et al., 2011). This is particularly the case in energy-intensive manufacturing processes. For the reasons mentioned above energy indicators should drive the investment decisions for thermal food processes. However, it has been found that:

- Most of the available sets of sustainability indicators are focused on the sustainable development on national or regional levels (Joung et al., 2013), only a limited number of published indicator sets is primarily focused on the process/product level for sustainable manufacturing.

- Most decision support frameworks use traditional economic indicators that are not true measures of sustainability (Joung et al., 2013).

- In the indicator sets for sustainable manufacturing at process or product level in practice the importance of energy performance has been widely underestimated. IChemE (2002) proposed measuring sustainability performance of an operating unit on the balance of 3 components: environmental impact, economic return and social development, in which energy usage is only measured as one environmental impact of the process. Similarly, energy consumption and renewable energy usage are the only two indicators related to energy performance among a large number of indicators of sustainable production (Veleva and Ellenbecker, 2001). Nordheim and Barrasso (2007) also included these two indicators in the sustainable development indicator set of the European aluminium industry.

- Most decision support systems for the selection of manufacturing processes remain ineligible for a dependable interpretation of the energy issue in any rational decision. For the selection of a machine tool, Çimren et al. (2007) discussed productivity, flexibility, safety and environment, adaptability, while Ayaǧ (2007) discussed space adaptability, precision, reliability, maintenance and service in addition. Er and Dias (2000) investigated technical design considerations and cost factors in the selection of cast components. Nagahanumaiah et al. (2008) evaluated manufacturability, compatibility, cost effectiveness in choosing injection mold. Sun et al. (2001) argued that grinding force, grinding temperature, surface roughness of the ground specimen, wheel wear and metal removal rate can be preferentially show the performance of the grinding fluid. For the selection of advanced manufacturing technologies, Edalew et al. (2001) based on the evaluation of product cost, product performance, quality, delivery time and delivery time reliability, flexibility and innovativeness while Rao (2005) based on total purchasing cost, total floor space, total machine number and productivity. Although drying is claimed an energy-intensive operation that is responsible for up to 15% of the total energy usage (Chua et al., 2001), most of the studies rather recommended dryer selection on the consideration of factors such as process specifications and economic return than on the energy performance of the drying systems. In the system DRYSEL introduced by Kemp (2007), the dryer selection depended on mode of operation, heating, feeding, the material, material flow rate, and moisture content. Another system - the DrySES, developed by Lababidi and Baker (2003), involved drying mode, dryer operation, feed class, dryer type, single/multiple batch dryers and heating mode. Weres et al. (2010) based on drying air properties, grain properties, equilibrium moisture content to select appropriate equipment and conditions for drying cereal grains.

- For thermal processes in particular there is no set of sustainability indicators available.

The sets of sustainability indicators proposed in literature are obviously unsuitable and inapplicable for energy-intensive manufacturing processes. In order to adequately measure the sustainability of

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energy-intensive manufacturing processes, it is necessary to have a specific set of indicators. Such indicator set must focus on energy performance and balance sustainability issues in environmental, economical and social aspects Decision support for the selection of manufacturing processes

The rational selection of manufacturing technologies and equipment is the decisive factor for the design and the overall performance of manufacturing processes (Dağdeviren, 2008). Very often in decision-making problems, the decision makers encounter the problem of selecting option out of a wide range of alternatives. Involving a set of incompatible criteria (Rao, 2007) may also require additional technical assistance. Multiple criteria decision making (MCDM) tools are useful to support decisions in the presence of multiple, generally conflicting criteria.

As reviewed by Rao (2007), various MCDM methods have been widely used in manufacturing environment including:

- WSM: weighted sum method

- WPM: weighted product method

- AHP: analytic hierarchy process

- TOPSIS: technique for order preference by similarity to ideal solution

- ELECTRE. elimination et choice translating reality method

- PROMETHEE. preference ranking organization method for enrichment evaluation

- Compromise Ranking Method (VIKOR)

Among them, AHP represents the most commonly used tool (Wang et al., 2009) since due to its design it is effective in dealing with inconsistencies (Subramoniam et al., 2013), as well as with complex decisions and unstructured problems (Durán and Aguilo, 2008).

AHP is a measurement theory used to generate priorities from both discrete and continuous pair comparisons (Saaty, 1987). It is based on three principles of analytical thinking: (a) constructing hierarchies, (b) establishing priorities, and (c) testing for logical consistency (Subramoniam et al., 2013). In the manufacturing environment, AHP has been widely applied in process selection, supplier selection, and the configuration of manufacturing systems (Manassero et al., 2004). In classical AHP model, the selection procedure comprises 4 steps: (1) develop hierarchical structure with objective, attributes and alternatives, (2) determine the relative importance of different attributes with respect to the objective, (3) compare the alternatives pair wise with respect to each of the attributes, and (4) obtain the overall performance score for the alternatives (Rao, 2007). Some studies developed extended AHP models in which a design tool is integrated with AHP for designing of alternatives and selecting the most suitable alternatives at the same time (Li and Huang, 2009) or the alternatives proposed by the AHP will be measure benefit in a real time model (Ayaǧ, 2007). The serious practical constraints challenging the classical AHP method are the problem of incomplete and fuzzy information in most of the MCDM situations (Kahraman, 2008). For these reasons, the fuzzy set theory (Zadeh, 1965) has been extensively applied. Many AHP-based decision support systems have dealt with the imprecision and vagueness inherent to the information by applying fuzzy logic (Demirel et al., 2008).

A totally different approach is based on a system of rules and expert knowledge, known as rule-based decision support system. Unlike the aforementioned MCDM techniques that provide a ranking for the given options according to a set of reference criteria, a rule-based decision support system provides solutions inferred from a well-structured set of facts and rules. Such rule-based decision support systems were developed by Edalew et al. (2001) for selecting cutting tools, by Er

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and Dias (2000) for selecting cast components, and by Lababidi and Baker (2003), Kemp et al. (2004) for selecting drying technologies. The rule-based decision support systems exhibit the characteristics of logical reasoning, high-quality performance, and explanation capability (Giarratano and Riley, 1998) that are very useful for the selection of manufacturing equipment. However, the development of a rule-based decision support system might require significant time and effort.

In this study a combination of rule-based tools and fuzzy AHP has been tested to develop a decision support framework (DSF) for selecting thermal food processes. Thanks to its logical reasoning, rule-based tools are firstly employed to pre-select appropriate technology alternatives for a given situation. These alternatives are subsequently rated and ranked according to a set of sustainability criteria using fuzzy AHP technique. Energy indicators are specially focused in the consideration of the process sustainability and cover four aspects of energy use in the process including energy consumption, energy efficiency, energy savings and renewable energy use.

2. The proposed decision support framework

The proposed DSF combines two methods: rule-based technique and fuzzy AHP. Each method has been widely applied in engineering. However, as a combination they offer a new and efficient decision support tool in the selection of manufacturing processes. This tool can be applied to aid decisions both in the selection of new facilities and in changing technologies of existing facilities.

2.1. Subdivision of the thermal process systems

In this study, the thermal process systems are subdivided into three subsystems following the sequence of energy flow in the process (Figure 1). The purpose of this subdivision is to cover a wide range of technologies available on the market and to facilitate the evaluation and selection of the processes always in consideration of the sustainability of the process. Among the three subsystems, the processing subsystem is representative for the whole process due to the fact that it is the most critical operation for many thermal processes.

Figure 1. Thermal energy flow of industrial processes and the division of the DSS into sub-systems

The procedure of selecting subsystem technologies and overall system integrations is totally based on this division.

Heating subsystem

Processing subsystem

Heat recovery subsystem

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2.2. The selection process

Figure 2. The integrative selection process

The selection procedure comprises two main steps. The preliminary selection narrows the range of thermal process technologies suitable for a given product. The final selection provides the ratings and rankings of technologies within the predefined range according to a set of sustainability criteria. In Figure 2, the 2-step selection process is presented.

2.2.1. Preliminary selection

In the preliminary selection step, a rule-based technique has been applied. The selection is based on the technical specifications of the technologies and the food products. There are three interactive databases (as in Figure 2), one covering the thermal process technologies (technology database), the second covering food materials and products (product database) and the third containing the capability of technologies and products (capability database).

The technology database comprises three separate groups of data: technologies with respect to the foregoing subsystems; their specifications and the overall system technologies. Hence the database can cover a wide range of technologies but still ensure the detailed specification of each part of the system.

The product database contains the technical specifications of a variety of food products having undergone thermal processes. In this database, the technical specifications of the products are related to both the preliminary selection and the final selection processes. For the preliminary selection purpose, it covers the common technical characteristics of the substances to be processed, e.g. the form of the feed and the product, the requirements of the product, the allowable process temperature. For the final selection purpose, these are the data necessary for the appraisal of the process’ sustainability.

FIN

AL

SE

LEC

TIO

N

PR

ELI

MIN

AR

Y

SE

LEC

TIO

N

Pre-selected alternatives

Available alternatives

FUZZY ANALYTIC HIERARCHY PROCESS

RULE-BASED TECHNIQUE

Ratings and rankings of alternatives

FINAL DECISION

Technology database

Product database

Capability database

Technology evaluation database

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The capability database contains the domain of expertise on the capability of the subsystem technologies with respect to each attribute of the technical specification of the products. Thus, there will be three separate capability expert domains regarding the three subsystems in the process.

Subsequently, for a particular product only the subsystem technologies capable of all technical specifications for that product will be selected. The selection is carried out by a set of production rules, presented in the form of an IF-THEN rule. In general, the selection of the subsystem technology T for a given product P characterized by a set of specifications (p1, p2,..., pn) follows these steps:

IF <T is capable of p1>

AND <T is capable of p2>

AND <T is capable of p…>

AND <T is capable of pn>

THEN <select T>

A number of technologies will be selected for each of the three subsystems. The technologies of the overall system will then be configured following the combinations of the pre-selected technologies of the modular subsystems.

2.2.2. Final selection

In the final selection step, the technologies chosen in the first step are prioritized using fuzzy AHP. There are 4 levels in the proposed hierarchical criteria system including objective – criteria – sub-criteria – alternatives, each of them consisting in several constituents (Figure 3).

Figure 3. The hierarchical criteria system

A1

A2

A3

A…

An

Level 1 Objective

Level 2 Criteria

Level 3 Sub-criteria

Level 4 Alternatives

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Considering the sustainability of the processes, the evaluation and selection of thermal food processes are based on three main criteria: environmental, economic, and social performance. Each criterion is decomposed into a number of sub-criteria. Energy performances are especially highlighted in this study since it can describe the environmental, economic and social perspectives of sustainable manufacturing at the same time as discussed by Bunse et al. (2011).

The final selection is implemented in four main steps. First, the justification for constituents at different levels in the hierarchy is defined. They can be in the forms of qualitative judgment or quantitative judgment and will be expressed in triangular fuzzy numbers (TFNs). In the normalization step, the judgment data are converted to dimensionless values to represent the local weights of constituents in each level of the hierarchy. Next, global weights of alternatives are step-by-step aggregated along the hierarchy. In the final step, the global weight is defuzzified and converted to scrip number to facilitate the ranking process. The results of this step represent the rankings of the alternatives in ascending order indicating the advisability of technologies regarding sustainability. This calculation process is intentionally developed for two different applications: selection of the new facilities and changing the technologies in existing facilities.

Justification for constituents in the hierarchy

In this step, two methods are proposed for assigning the justification: absolute justification and comparative justification. In the case of absolute justification, preferences of constituents are interpreted regarding to their real performance. This procedure is proposed for the justification of thermal process technologies against the sub-criteria in the hierarchy (level 4 against level 3). Absolute justification can be in the form of quantitative or qualitative judgments. Quantitative judgment is intentionally applied for the justification of alternatives with respect to the sub-criteria of energy performance. Triangular fuzzy numbers of the quantitative judgment are presented as the lower bound value (LBV), most occurring value (MOV) and upper bound value (UBV) within the range of the available data (adapted from Dubois (1980), see Figure 4). Qualitative judgment is applied for the justification of the alternatives with respect to the sub-criteria of economic and environmental performances. Several scales have been used to converted linguistic assessments to TFNs, such as scale 1-3-5-7-9 (Naghadehi et al., 2009), scale 1/5-1/3-1-3-5 (Chan et al., 2000), or dynamic scale (Amelia et al., 2009). In this study, the triangular fuzzy scale presented by Chan et al. (2000) has been applied as shown in Figure 4.

Figure 4. Triangular fuzzy numbers representation of quantitative judgment and qualitative judgment (left part is adapted from Dubois (1980), right part is from Chan et al (2000))

Comparative justification describes the relative preference of each constituent compared to the others in a given set of constituents. It is used to evaluate the importance of the sub-criteria and criteria using the pair-wise comparison method developed by Saaty (1980) and is specified in the reciprocal matrix as also described in Chan et al. (2006).

Normalization of judgment data

VH M L VL

3

1

5

1 5 x 3

1

µA(x)

0

H

1 x

1

LBV

µA(x)

0 MOV UBV

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The justifications of constituents are then transformed to relative weighting values by normalization. Normalization converts judgment data to dimensionless values in order to ensure the compatibility between absolute and comparative justification on the one hand and between quantitative and qualitative judgment on the other hand. This can also be interpreted as the local weights of the constituents, meaning the weights with respect to the constituent at the upper level in the hierarchy.

Local weight of constituents at level 2 and level 3 (comparative justification) is defined by geometric row mean method as follows:

(1)

g�� � �c���⊕ c��⊕…⊕ c���⊕…⊕ c���p �

(2)

w�� � g��g��⊕ g��⊕…⊕ g��⊕…⊕ g��p

Where: cjkl denotes the element in the reciprocal matrix of sub-criteria Cjk

gjk denotes the geometric row mean of sub-criterion Cjk versus criterion Cj

wjk denotes the local weight of sub-criterion Cjk

p denotes the number of constituents in the set (and also the elements of the corresponding reciprocal matrix)

Unlikely, the local weights of constituents at level 4 (absolute justification) are dependent on the application of the DSF. The local weights for selecting new facilities are calculated by the aggregated normalization method as given in Equations 3 and 4. The local weights for the technology change of the existing facilities are calculated by the base-cased normalization method as given in Equation 5.

Aggregated normalization:

(3)

e��� � ∑ a�������n

r��� � a���e��� (4)

Where: aijk is the absolute judgment of alternative Ai versus sub-criteria Cjk

rijk is the local weight of alternative Ai versus sub-criteria Cjk

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n is the number of alternatives in the set

Base-cased normalization:

The local weight of alternative Ai with reference to the base-case alternative Ao is given by:

(5)

r��� � a����e���⊗ a���

Where: aojk is the absolute judgment of the base-case alternative Ao versus sub-criteria Cjk

Aggregating the rating along the hierarchy

Subsequently, weightings of alternatives are aggregated step-by-step along the hierarchy (see Equations 6 and 7). The result of this step is the global weight Ri of alternative Ai with respect to the overall objective O.

(6)

r�� � 1p⊗ �r���⊗w��⊗ x��⊕…⊕ r���⊗w��⊗ x��⊕……⊕ r���⊗w��⊗ x���

(7)

r� � 1m⊗ �r��⊗w�⊗ x�⊕…⊕ r��⊗w�⊗ x�⊕……⊕ r�!⊗w!⊗ x!� Where p, m are the number of constituents in the sub-criteria sets (level 3) and criteria set (level 2) respectively; xjk and xj: factors represent the positive priority and negative priority of the sub-criterion Cjk and criterion Cj respectively (Saaty, 2006). x=1 if positive priority, x=-1 if negative priority

Defuzzification

After the aggregating step, the ratings of the alternatives (values ri) are still in the form of TFNs, which are not easy to compare and therefore it might not be possible to determine the more preferable one. Therefore, they will be defuzzified and converted to crisp numbers in order to facilitate the ranking process. A number of defuzzification methods have been checked and the centroid point method (Cheng, 1998) was evaluated most appropriate. Ranking of the triangular fuzzy number i(i1,i2,i3) using centroid point method is presented in Equations 8-10.

(8)

x"� � 13 $i� & 2i & i()

(9)

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y"� � i� & 4i & i(3$i� & 2i & i()

(10)

R� � �$x"�) & $y"�) The final results of the defuzzification step represent the rankings of the alternatives in ascending order indicating the advisability of technologies regarding sustainability.

The above rating and ranking procedure is schematically presented in Figure 5. This calculation process is repeated for each of the three subsystems. As a result, there are three separate ranking lists. The ratings of the whole drying systems are subsequently determined using Equation 11 below:

(11)

R-. � W01⊗R01.⊕W21⊗R21.⊕W31⊗R31.3

Where

RAi is the rating of the system alternative Ai

WHS, WPS, WRS are the importance factors of the heating subsystem, processing subsystem and heat recovery subsystem

RHSi, RPSi, RRSi are the ratings of heating technology HSi, processing technology PSi and heat recovery technology RSi

The important factors of the subsystems are determined by the comparative justification method, in analogy to the determination of the weightings of the criteria. The processing subsystem is the core component of the process, thus it should be given higher priority than the heating and heat recovery subsystems.

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Figure 5. The rating and ranking procedure

3. The decision support system for the selection of food drying technologies

A decision support system (DSS) for selecting food dryers has been programmed based on the proposed framework. Facing the complexity and diversity of the drying process, the DSS is developed to be comprehensive, yet simple enough for being applied by any user even without a technical background. Considering sustainability of the food drying process, the selection criteria are broken into a number of proper sub-criteria in the hierarchy as in Table 1.

Table 1. The hierarchical criteria system for food dryer selection

Comparative judgment

Normalized geometric row means

Defuzzification

Base-cased normalization

Comparative judgment

Aggregate ratings of alternatives with respect to objective

Aggregate ratings of alternatives with respect to criteria

3. Aggregating the rating along the hierarchy

1. Justification of constituents in the hierarchy

Absolute judgment

2. Normalization of judgment data

4. Defuzzification – ranking of alternatives

Comparative judgment

wj

Normalized geometric row means

Aggregated normalization

r ij

Ri

wjk r ijk

r i

aijk cjk cj

Objective Level 1 (O)

Criteria Level 2 (Cj)

Sub-criteria Level 3 (Cjk)

Alternative Level 4 (Ai)

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O

Sustainable food drying system

Environmental performance

C11 Specific thermal energy consumption PS01 Contact cabinet dryer C1 C12 Thermal energy efficiency PS02 Contact tunnel dryer C13 Heat recovery ratio PS03 Plate dryer C14 Renewable energy utilization PS04 Contact conveyor dryers C2

Economic performance

C21 Investment cost PS05 Rotary steam tube dryer C22 Operation cost PS06 Drum dryer

Social performance

C31 Final product quality PS07 Direct cabinet dryer C3 C32 Fire hazard and explosion hazard PS08 Direct tunnel dryer C33 Convenience of installation and operation … …

Level 1: Objective Level 2: criteria Level 3: sub-criteria Level 4: Subsystem alternatives

The core components of this DSS are the databases for the preliminary and final selection steps. They contain a wide range of data on configuration of drying processes, food products and performance of various drying technologies with respect to environmental, economic and social criteria. To facilitate the programming, the database is computed in two ways: Boolean data type in the preliminary selection step and triangular fuzzy number data type in the final selection step. A huge amount of data has been carefully screened and collected in order to make the databases as exhaustive and consistent as possible. These databases were originally developed from careful literature surveys and expert consultations during a 4-year doctoral study at Graz University of Technology, Austria (see Do (2012)).

The Boolean data type is used to characterize the technical specifications of the technologies in the drying subsystem and the food products as well as the configuration of the overall drying systems from the combinations of the modular subsystem technologies. It is also used to signify the capabilities of the technologies and the product specifications. In Boolean programming in the DSS TRUE or FALSE values are used to indicate the proposition of the aforementioned specifications, combinations, and capabilities.

Based on Boolean data type, a total of 6 heating technologies, 16 processing technologies and 3 heat recovery technologies have been collected and specified in the technology database so far, configuring 109 overall system technologies. Each subsystem is characterized by a number of technical specifications, for e.g. the heating subsystem is characterized by drying medium, type of primary energy used; the processing subsystem is characterized by operation mode, heating mode, heat input type, drying temperature, operating pressure, residence time within the dryer; while the heat recovery subsystem is characterized by heat recovery method. The product database is compiled in accordance with the NACE code – subsector DA15.3. The technical specifications of food for the preliminary selection purpose include the physical form of feed, nature of wet feed, special requirements of the product, and allowable drying temperature. Two user-defined specifications related to the mode of operation and product throughput are also included. The expertise in the capability of technologies with respect to attributes of said specifications is recorded in logical statements that allow for the implementation of the production rules.

The data for the final selection are all specified in TFNs. A large amount of data has been compiled enabling comprehensive evaluations of environmental, social and economic performances for various drying technologies. Since energy performance plays the most important role in the selection process, it requires adequate estimation. Four distinct models have been developed for calculating the specific thermal energy consumption of four types of dryers including hot air dryers, contact dryers, heat pump dryers and freeze dryers. The compilation of data on economic and social performance is based on information available in literature and practical experiences. Qualitative judgment is used so that the performances of technologies with respect to economic and social

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attributes are firstly specified as linguistic assessments, and then converted to TFNs through a proper scale.

4. Case studies

The DSS has been validated in several case studies for example selecting cassava starch dryers and coconut dryers. Here the test for selecting coconut dryers is presented. Desiccated coconut is an important component of many bakery products. In the manufacturing process of desiccated coconut, drying is the most energy-intensive operation. It accounts for up to 60% of the total energy consumption. In practice, coconut dryers appear in diversified types, including tray dryers, conveyor dryers, fluid bed dryers, rotary dryers and others.

Here, two applications of DSS have been carried out. In the application for the process selection for new facilities, DSS likely runs in the non-interactive mode because all of the data can be taken from the databases, no user-input is required. In the application for changing technologies in the existing facilities, a factory for desiccated coconut in Ben Tre province, Vietnam, was chosen as the base-case. The existing drying system is a fluid bed type. An energy audit was carried out for the drying process to provide justification data of the base-case alternative.

In the preliminary selection step, 7 processing technologies, 5 heating technologies and 3 heat recovery technologies are recommended for the product, comprising 51 process schemes for the overall system. These alternatives and the base-case alternative have then been taken into the rating and ranking process in the final selection step. Figure 8 lists the pre-selected technologies and the rating and ranking results of the subsystems, while Figure 9 displays an extract of the selection results of the overall system.

Table 2. Fuzzy ratings and ranking of subsystem technologies for desiccated coconut

ID Technology APPLICATION 1 - SELECTION OF THE

NEW FACILITIES APPLICATION 2 - TECHNOLOGY CHANGE

OF EXISTING FACILITIES Fuzzy rating Crisp rating Raking Fuzzy rating Crisp rating Raking

PROCESSING SUBSYSTEM PSbc Fluidized bed dryer (-1.945 , 0.217 , 30.702) 9.664 4

PS02 Contact tunnel dryer (-0.88 , 0.309 , 14.384) 4.618 6 (-0.824 , 0.36 , 15.479) 5.017 6

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PS04 Contact conveyor dryers (-1.124 , 0.372 , 26.214) 8.494 4 (-1.01 , 0.431 , 27.643) 9.028 4

PS05 Rotary steam tube dryer (-1.81 , 0.504 , 32.116) 10.276 1 (-1.708 , 0.616 , 34.94) 11.288 1

PS08 Direct tunnel dryer (-1.477 , 0.195 , 13.293) 4.019 7 (-1.321 , 0.258 , 15.003) 4.659 7

PS09 Direct conveyor dryers (-1.722 , 0.355 , 26.272) 8.309 5 (-1.508 , 0.423 , 27.691) 8.875 5

PS11 Direct rotary dryer (-2.233 , 0.27 , 29.321) 9.126 2 (-2.074 , 0.397 , 31.835) 10.058 2

PS12 Fluidized bed dryer (-2.237 , 0.235 , 29.037) 9.018 3 (-2.078 , 0.333 , 30.773) 9.682 3

HEATING SUBSYSTEM

HSbc Boiler-renewable energy (-1.442 , 0.719 , 35.36) 11.551 2

HS01 Boiler/heater-non renewable energy

(-1.384 , 0.577 , 25.719) 8.311 3 (-1.446 , 0.684 , 30.394) 9.884 3

HS02 Boiler-renewable energy (-1.359 , 0.937 , 31.4) 10.332 1 (-1.428 , 0.919 , 33.468) 10.992 2

HS03 Boiler-mixed type (-1.377 , 0.713 , 24.511) 7.957 4 (-1.441 , 0.767 , 28.147) 9.165 4

HS04 Solar collector (-1.452 , 0.367 , 17.796) 5.581 5 (-1.632 , 0.271 , 18.801) 5.824 5

HS05 Heat pump (-0.434 , 1.006 , 28.51) 9.700 2 (-0.456 , 1.182 , 35.622) 12.121 1

HEAT RECOVERY SUBSYSTEM

RSbc No heat recovery (-0.312 , 0.102 , 13.35) 4.393 3

RS01 No heat recovery (-0.2 , 0.212 , 16.785) 5.610 3 (-0.312 , 0.102 , 13.35) 4.393 3

RS02 Exhaust air recirculation (-1.135 , 0.58 , 42.204) 13.890 1 (-1.772 , 0.3 , 40.66) 13.067 1

RS03 Exhaust air heat exchanger (-1.254 , 0.168 , 33.052) 10.660 2 (-1.972 , -0.586 , 30.834) 9.431 2

Table 3. Fuzzy ratings and ranking of overall system technologies for desiccated coconut

SYSTEM ALTERNATIVE APPLICATION 1 - SELECTION OF THE NEW FACILITIES

APPLICATION 2 - TECHNOLOGY CHANGE OF EXISTING FACILITIES

ID Technology Fuzzy rating Deffuzification Ranking Fuzzy rating Deffuzification Ranking

Abc Fluidized bed dryer - Boiler-renewable energy - No heat recovery

(-0.66 , 0.35 , 71.67) 23.79 18

A019 Contact tunnel dryer - Boiler/heater-non renewable energy - No heat recovery

(-0.38 , 0.38 , 43.89) 14.63 47 (-0.38 , 0.43 , 48.16) 16.07 48

A022 Contact tunnel dryer - Boiler-renewable energy - No heat recovery

(-0.38 , 0.48 , 48.51) 16.21 41 (-0.38 , 0.5 , 50.66) 16.93 45

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A025 Contact tunnel dryer - Boiler-mixed type - No heat recovery

(-0.38 , 0.42 , 42.9) 14.32 48 (-0.38 , 0.45 , 46.33) 15.47 50

A055 Contact conveyor dryers - Boiler/heater-non renewable energy - No heat recovery

(-0.44 , 0.42 , 59.02) 19.67 33 (-0.43 , 0.47 , 63.72) 21.26 34

A058 Contact conveyor dryers - Boiler-renewable energy - No heat recovery

(-0.44 , 0.52 , 63.64) 21.24 24 (-0.42 , 0.54 , 66.22) 22.11 31

A061 Contact conveyor dryers - Boiler-mixed type - No heat recovery

(-0.44 , 0.45 , 58.03) 19.35 35 (-0.43 , 0.5 , 61.89) 20.66 36

A073 Rotary steam tube dryer - Boiler/heater-non renewable energy - No heat recovery

(-0.61 , 0.5 , 66.57) 22.15 16 (-0.6 , 0.58 , 73.05) 24.35 12

A076 Rotary steam tube dryer - Boiler-renewable energy - No heat recovery

(-0.61 , 0.6 , 71.19) 23.73 5 (-0.6 , 0.65 , 75.55) 25.21 5

A079 Rotary steam tube dryer - Boiler-mixed type - No heat recovery

(-0.61 , 0.53 , 65.58) 21.84 20 (-0.6 , 0.61 , 71.22) 23.75 19

A127 Direct tunnel dryer - Boiler/heater-non renewable energy - No heat recovery

(-0.53 , 0.31 , 42.49) 14.09 49 (-0.5 , 0.37 , 47.55) 15.81 49

A128

Direct tunnel dryer - Boiler/heater-non renewable energy - Exhaust air recirculation

(-0.59 , 0.35 , 49.39) 16.38 40 (-0.6 , 0.39 , 54.96) 18.25 39

A129

Direct tunnel dryer - Boiler/heater-non renewable energy - Exhaust air heat exchanger

(-0.6 , 0.3 , 46.9) 15.54 44 (-0.62 , 0.3 , 52.3) 17.33 43

A130 Direct tunnel dryer - Boiler-renewable energy - No heat recovery

(-0.52 , 0.41 , 47.11) 15.67 43 (-0.5 , 0.43 , 50.05) 16.67 47

A131 Direct tunnel dryer - Boiler-renewable energy - Exhaust air recirculation

(-0.59 , 0.45 , 54.01) 17.96 37 (-0.6 , 0.45 , 57.46) 19.11 38

A132 Direct tunnel dryer - Boiler-renewable energy - Exhaust air heat exchanger

(-0.6 , 0.41 , 51.53) 17.12 39 (-0.62 , 0.36 , 54.8) 18.19 40

Some important observations can be drawn from the obtained results:

With regard to the sustainability aspect, rotary steam tube dryers turn out to be most preferable, followed by direct rotary dryers. Direct tunnel dryers are least appropriate for drying coconut. For the heating and heat recovery subsystems, boilers using renewable energy and exhaust air recirculation systems are the most preferable technologies.

The technology of the existing processing subsystem (coded PSbc in Table 2) ranks 4th out of 7 proposed technologies, while the same technology assessed by the DSS (coded PS12) ranks 3rd. These results show, that the drying system in place is not working on a desired level of sustainability, and could be much improved. Two other drying technologies turn out to be preferable as to their sustainability.

Considering the overall technology, the base-case alternative (coded Abc in Table 3) ranks 18th out of 51 alternatives proposed by the DSS.

5. Concluding remarks

In this study, a multiple criteria decision making framework for decision support has been developed, that is able to support the selection of suitable technologies for thermal processes, by considering sustainability aspects at the same time. The framework is proposed as a new and

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improved approach for decision support system because it successfully combines two different methods: rule-based techniques and fuzzy analytic hierarchy process. It is also novel in the division of the decision support system into three process subsystems following the thermal energy flow of the industrial processes. The proposed decision support framework is applicable for a wide range of thermal processes in the food industry including blanching, pasteurization, sterilization, boiling, cooking, drying, etc.

The proposed framework has been successfully tested in the development of a decision support system for food dryer selection. Facing the complexity and diversity of drying processes, the decision support system is developed to be comprehensive, yet simple enough for users without a technical background. The main advantage of this DSS is that it enables a simple selection procedure whereas the complex thermodynamic analyses have been properly settled due to the fact that it requires very little input information. For users who are seeking a sustainable drying technology for a new facility, they only need to define the material and product to be dried, the DSS will process and generate a list of technologies that are ranked for sustainability. For those who would like to replace their existing drying system by a more sustainable one, they need to provide further information on the energy performance of the system in place, the DSS will process and result in the rankings of the existing technology and the proposed technologies.

The DSS has been validated in several case studies. The technologies recommended by the DSS are reasonable and consistent with industrial practice, and the ranking of the technologies correlates well with human expert assessment. Comparing the rating and ranking results obtained from the two applications of the DSS, although the fuzzy ratings of the alternatives are fluctuant due to different calculation processes, the rankings obtained are generally in alignment with each other.

The DSS is currently working well on MS-Excel. The programming of the DSS on MS-Excel requires less computational efforts. However, it is obviously more troublesome and annoying while working with it on Excel. Therefore a more professional software platform is desirable for future applications.

Acknowledgements

Dr. Thi-Thu-Huyen Do was supported by the Swiss Agency for Development and Cooperation (SDC) for her PhD study on “Development of a decision support framework considering sustainability for the selection of thermal food processes”.

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