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About Process Systems Enterprise

PSE (www.psenterprise.com) is the world's foremost provider of Advanced Process Modelling software and services to the process industries. Advanced Process Modelling is transforming the way that process companies design and operate processes by enabling better, faster and safer design and operating decisions and reducing uncertainty.

Use of PSE's technology and services results in faster innovation, improved designs of processes and products, enhancement of existing operations and more effective R&D and experimental campaigns. It also facilitates capture and transfer of corporate knowledge across the organisation. Results are achieved with relatively low investment compared to alternative approaches – where these exist – with rapid return on investment.

PSE's global customer base of Fortune 500 process industry companies is served by operations in the UK, USA, Germany, Japan and Korea, and agencies in Saudi Arabia, China, Thailand, Malaysia and Abu Dhabi. PSE is a spin-out of Imperial College London, and its software is also used for reseach and teaching in some 200 universities around the world.

The company's own ability to innovate was recognized with the award of the prestigious Royal Academy of Engineering MacRobert Award for Engineering Innovation, the highest UK engineering prize.

About gPROMS®

gPROMS® is the world's leading Advanced Process Modelling environment. gPROMS models are used to explore the design or operational decision space to provide accurate predictive information for decision support. This helps companies reduce time-to-market for new processes or products, manage development risk, improve designs, enhance production, reduce capital and operating expenditure and ensure better compliance with safety, health and environmental requirements.The package is applied in all sectors of the process industries, with particular focus on modeling of complex operations such as reaction, separation, and polymerization. PSE

also supplies a range of process engineering tools built on the gPROMS platform, including gFUELCELL®, gSOLIDS®, gCRYSTAL,® gCCS® and gFLARE®.gPROMS is applied across the 'process lifecycle' and at multiple scales, from laboratory experimentation through process and detailed design to online operation, and is central to a model-based engineering approach. PSE is committed to maintaining gPROMS at the leading edge of process modeling technology.

About gSOLIDS®

gSOLIDS is used to increase R&D efficiency and reduce the risk associated with the design, scale-up and operation of manufacturing processes involving particulate materials.

gSOLIDS is a second-generation integrated drag & drop graphical flowsheeting environment for model-based engineering and optimisation of solids processes. Developed in conjunction with Procter & Gamble, Pfizer and Novozymes, it is aimed at process engineers and scientists in industries where particulate processes play an integral part, such as pharmaceuticals, fine chemicals, agrochemicals, food processing, consumer goods and minerals and mining.

A key advantage of gSOLIDS is that it is built on the gPROMS advanced process modelling platform, which provides the ability to perform full steady-state and dynamic modelling, handle large numbers of recycles robustly, model complex operating procedures for batch and semi-continuous processes, and easily add custom models to reflect users' actual unit operations. It can also handle multiple solid phases, each with its own particle size distribution and size dependent composition.

gSOLIDS uniquely includes a wide range of advanced features including parameter estimation for fitting process parameters from laboratory or operational data, rigorous mathematical optimisation of design and operation, and sensitivity analysis for risk management. It also integrates with PSE's gCRYSTAL and gas-liquid process models to enable simultaneous whole-process design and optimisation.

gSOLIDS helps engineers to optimise the operation of units such as high shear wet granulators, fluidised bed dryers, mills, screens, spray dryers, hoppers and conveyors in order to ensure product quality, size recycles and surge bins for new plants and achieve the required throughput. A key use is to determine the optimal trade-off between capital and operating cost and make informed purchasing decisions.

Typical benefits include increased R&D efficiency, more reliable scale-up and tech transfer, reduced capital investment, reduced operating costs, improved product quality, increased throughput, more flexible process design, reduced CO2 footprint, and a better process understanding. gSOLIDS also provides a flexible and powerful platform for integrating companies' internal R&D and third party research.PSE works closely with leading research consortia such as the US NSF Engineering Research Center for Structured Organic Particulate Systems (C-SOPS), which is developing science and engineering methods for designing, scaling, optimising and controlling manufacturing processes for the life sciences industries, to maintain the technology at the forefront of innovation and enable knowledge transfer between academia and industry.

Model-Based InnovationAccelerated, effective R&D through modellingModel-Based Innovation (MBI) involves combining high-fidelity models of processes or products with modern R&D methodologies to provide high-quality information for innovation decision support.

This helps companies to manage risk in innovation, design and operational enhancement based on accurate quantitative information.

The result is faster innovation, improved designs of processes and products, enhancement of existing operations and more effective R&D programmes.

PSE's ModelCare® Model-Based Innovation service covers all process industry sectors, and can be applied from laboratory R&D, through process and detailed design, to online operation.In fact, MBI is a key mechanism for integrating R&D, design and operational activities, to achieve mutually beneficial objectives.

The benefits of Model-Based InnovationThe application of MBI can result in significant competitive advantage through modelling. Some specific benefits are:

effective risk management. Modelling provides quantitative data on which to base R&D and

design decisions, allowing you to manage risk with confidence.

fast screening of alternatives. Modelling quickly shows you which alternatives to discard and

which to pursue.

streamlining of experimental programmes. Model-based experiment design can reduce time

and cost of experimentation significantly.

direction of Research & Development spending. The information generated by MBI

techniques can be used to rank R&D investment alternatives by value.

understanding the relationship between experimental uncertainty and design margin.

availability of accurate validated models for design, leading to effective and accurate process

or product design right from the research stage.

PSE's ModelCarePSE's ModelCare service is designed to help companies innovate rapidly with relatively low investment and fast payback.A key aim of ModelCare is to transfer MBI know-how to customers to help them build their own internal Model-Based Innovation capability.

Model-Based Innovation methodology: the fundamentalsPSE's Model-Based Innovation methodology is underpinned by major advances in modelling technology and thinking:

1. Modelling technology has come of age

Advanced Process Modelling tools and methodologies now allow modelling of many types of complex process, products or equipment - from detailed reactor systems to tablets delivering drugs in the human body - to a degree of predictive accuracy which is capable of supporting real innovation in design and operation.Hybrid "APM-CFD" methodologies make it possible to investigate the detailed effects of equipment geometry on the behaviour of complex processes, enabling accurate scale up.Beyond simple trial-and-error simulations, models can now be used directly for rigorous optimisation of design and operating conditions involving both continuous and discrete decisions (e.g. the structure of the plant flowsheet).

2: Modelling and experimentation can be closely coupled

As shown below for the simultaneous design of a new catalyst and reactor, model-supported micro-experimentation[1] can use small catalyst samples[2] together with models of the experimental apparatus[3] to generate accurate values of model parameters[4].Model-based experiment design[1] minimises the number and cost of such experiments by ensuring that each one generates the maximum amount of information.

Once the parameters are known, then they can be used in models of full-scale equipment[5] coupled with rigorous optimisation[6] techniques to determine the optimal reactor design[7] and operation[8] that are achievable using this particular catalyst.This provides a screening mechanism, reserving pilot plant testing[9] only for catalysts which appear to be promising.

3: Modelling can establish a quantitative link between uncertainty and risk

Even formally validated models are still subject to uncertainty in their parameter values.

It is important to be able to map this uncertainty to uncertainty in Key Performance Indicators (KPIs) of the process, to allow a trade-off between research and risk.

All parameters are uncertain, but not all of them are critical.

Some parameters may have a small effect on KPIs; uncertainty in other parameters may be counteracted by control actions.

Models can help establish thequantitative relationship between fundamental R&D uncertainty in process development and technological risk during process operation.By establishing which parameters affect KPIs most, research investment can be directed towards the most critical parameters.

Model-Based EngineeringIntegrating models and data to accelerate engineering

A Model-based Engineering (MBE) approach applies advanced process models in combination with observed (laboratory, pilot or plant) data to the engineering process.The objective is to enable exploration of the process decision space as fully and effectively as possible, and support design and operating decisions with accurate information.

Typical application is in new process development, including scale-up, or optimisation of existing plants.

What does model-based engineering involve?MBE typically involves high-fidelity, first principles process models validated against data in the "model validation cycle".

MBE places high-fidelity predictive models at the heart of process design or operational analysis.

Initial project effort is put into constructing a high-fidelity model of the plant or process that is predictive over the entire range of interest.

This model is then used to optimise design or operation, exploring a wide design space rapidly and at low cost, and applying optimisation techniques to determine answers directly rather than by trial and error simulation.

MBE is based on three core approaches:

First-principles modelling, where all relevant phenomena are described to an appropriate level of chemical engineering first principles representation. This typically involves detailed mass transfer, heat transfer and reaction equations.

Multiscale modelling, where phenomena at all relevant scales are taken into account. The diagram on the right shows, for example, the scales that need to be taken into account for a multitubular reactor. The phenomena occurring at a microscale in a catalyst pore can have a significant influence on the overall (macroscale) reactor design.

Integration with experimental data, by applying a model-targeted experimentationapproach to refine the model and at the same time maximise the effectiveness of the experimental programme.The guiding principle of model-targeted experimentation is "use experimentation to improve the accuracy of the model (rather than the process itself), then use the model to optimise the process design or operation".

Why apply Model-based Engineering?Key objectives of MBE are to:

Accelerate process or product innovation, by providing fast-track methods to explore the design space while reducing the need for physical testing.

Minimise (or effectively manage) technology risk by allowing full analysis of design and operational alternatives, and identifying and addressing areas of poor data accuracy.

Integrate R&D experimentation and engineering design in order to maximise effectiveness of both activities and save cost and time.

Reduce operating costs or improve throughput and product quality through high-accuracy analysis and model-based optimisation techniques.

Reduce the amount of experimentation, through better-targeted and better designed experiments

Reduce (but not eliminate) the requirement for pilot plant testing. Many design options can be explored and eliminated using predictive modelling before the best candidates are chosen for pilot plant testing.

How does MBE work in practice?MBE applies a family of methodologies centred on high-fidelity predictive models in a structured way.

First-principles models and experimental data are combined to create a high-fidelity predictive representation of the key phenomena occurring in any process, often (particularly in the case of reactor design) through model-targeted experimentation.

These sub-models of the phenomena are then used to build high-fidelity models of the full-scale process.

The model is used in steady-state and dynamic simulation and optimisation studies to achieve the project objectives.If necessary it is possible to deploy a variety of well-established techniques – for example, combining the physics and chemistry models derived above with Computational Fluid Dynamics (CFD) hydrodynamic information, or using population balance models – to take into account mixing and other effects introduced at larger scales.

Examples of applicationFor comprehensive examples of application, see the following articles:

LG Chem article Optimize terephthaldehyde operations [Hydrocarbon Processing, April 2007], describing the model-based design of a multitubular reactor

Süd-Chemie article Enhanced methods optimize ownership costs for catalysts[Hydrocarbon Processing, June 2007], describing the application of MBE techniques to catalyst deactivation analysis for methanol synthesis and optimisation of catalyst loading

Repsol article Improve engineering via whole-plant design optimization [Hydrocarbon Processing, December 2010], describing the simultaneous optimisation of reactor and separation section that improved plant economics by tens of millions of Euros.

Model-Based Engineering: key componentsPSE's MBE approach is founded on:

The gPROMS ModelBuilder high-fidelity predictive modelling platform. ModelBuilder provides all the facilities needed to perform MBE within a powerful modelling and solution engine capable of generating the high-accuracy predictive information on which key design and operating decisions are based.

A set of model-targeted experimentation techniques and methodologies proven over many years of application to industrial problems: parameter estimation, model-based data analysis, model-based experiment design.

The state-of-the-art gPROMS advanced process models for catalytic reaction, complex separation, crystallisation and polymerisation.

PSE's hybrid modelling technologies which combine modelling in gPROMS with computational fluid dynamics (CFD) tools for ultimate scale-up accuracy.

PSE's extensive Consulting expertise.

Advanced Process ModellingHigh-quality information for decision support

Advanced Process Modelling involves applying detailed, high-fidelity mathematical models of process equipment and phenomena, usually within an optimisation framework, to provide accurate predictive information for decision support in process innovation, design and operation.Advanced process models are used to explore the process decision space to enable better, faster and safer decisions by reducing uncertainty. The approach differs significantly from that of traditional process simulation.

ExampleIntegrated oil company Repsol applied advanced process modelling techniques to a new petrochemical process design that involved a complex multitubular catalytic reactor and an extensive separation system, connected by major recycles.

The combination of advanced process models and optimisation techniques resulted in animprovement in process economics of tens of millions of Euros over the original design, which was performed using traditional process simulation techniques.There are many similar examples in all areas of the process industries. Typical application areas are those that involve complex physical and chemical phenomena, such as reaction engineering, crystallization, complex separation processes and fuel cell component and system design.

These processes are often at the heart of the value chain, and thus where the most value can be realised.

What does Advanced Process Modelling involve?Advanced process modelling is a combination of three elements:

mathematical models based on chemical engineering first-principles theory experimental data – laboratory, pilot or operating plant – used to fit the empirical

parameters in the model (or 'validate' the model) advanced solution techniques – for example, optimisation – to exploit the rich

information in the model and its predictive capability.

Much of the predictive power of advanced process models results from the combination of first-principles chemical engineering, physics and chemistry with observed ("real-life") data.

A properly-constructed model will have predictive accuracy well beyond the area in which it was fitted, allowing – for example – scale-up, optimisation of processes for different operating conditions.

Where are models applied?Unlike traditional process flowsheeting, or so-called multiphysics models, advanced process models are applied:

at multiple scales, from micron-level catalyst models to plant-wide optimisation across the process lifecycle, from initial experimentation and conceptual design

through engineering design to plant operation – and in some cases, decommissioning across system-wide applications. An example is the whole-chain carbon capture toolkit

being developed by PSE for techno-economic decisions across whole networks of plants.

What are the benefits?Having accurate models that predict performance over a range of conditions allows you to explore the decision space rapidly, effectively and at relatively low cost. The result is:

better decisions: better process designs, better equipment designs, better product designs, better operations

faster decisions: faster and more confident scale-up, faster process development, accelerated innovation at all levels

safer decisions: better compliance with health & safety requirements, better environmental compliance, more effective management of risk associated with introducing new technology.These can all add up to significant competitive advantage, and can be achieved at relatively low cost – especially when compared with building pilot plants or prototypes.A key benefit for many organisations is the ability to capture, deploy and transfercorporate knowledge effectively across the organisation, including streamlining workflows between R&D experimentation and the engineering design process.

What is PSE's role?PSE is the leading supplier of advanced process modelling technology and services to the process industries.

Our gPROMS modelling and optimisation platform provides a sophisticated, modern software environment created specifically for construction, validation and execution of high-accuracy models, and the company is a pioneer in the growing application of model-based engineering.

gPROMS ModelBuilder at-a-glanceBuild, validate, execute, deploy … and maintain

ModelBuilder is a powerful, all-in-one advanced process modelling environment for the expert modeller or process engineer. As an advanced custom modelling tool as well as a process flowsheeting simulator, it delivers industry-leading capabilities in both areas.

The model lifecycleModelBuilder supports all elements of the model lifecycle – build, validate, execute, deploy – augmented by powerful audit and easy maintenance capabilities. It allows you to:

Build custom process models to virtually any degree of complexity, and construct process flowsheets combining these with library models

Validate models against experimental data, using formal mathematical optimisation techniques to fit model parameters. The combination of first-principles models with observed data provides unprecedented predictive accuracy.

Execute models in a variety of ways – for example, steady-state or dynamicsimulation or optimisation – to generate results rapidly. ModelBuilder is renowned for the speed and robustness of its numerical solvers.

Deploy models into various contexts. For example, it is possible to package models using the gPROMS Objects so that they can be used in execute-only mode behind Excel or VBA interfaces, or within automation or purchasing systems.

Key featuresModelBuilder inherits all the powerful features of the gPROMS platform. The diagram shows some of these:

Custom modelling capabilities

ModelBuilder provides industry-leading custom modelling capabilities based on the powerful gPROMS language.

Users transcribe equations as they appear in research papers or other literature sources, using the library models supplied with ModelBuilder as a starting point if necessary .

Validated models can be incorporated into libraries with icons, dialogs, reports and other attributes, and used in flowsheets just like any other gPROMS model.

Process flowsheetingModelBuilder is a process flowsheeting package, just like any other process simulator. However, unlike other process simulators, you can:

incorporate your own custom models of arbitrary complexity utilise the full power of the gPROMS equation-oriented approach – for example,

setting downstream values and calculating upstream values. perform steady-state and dynamic simulation within the same framework apply powerful dynamic and mixed-integer optimisation with many decision variables,

to determine optimal answers directly rather than via trial-and-error simulation export models for execution in other engineering software environments using

thegPROMS Objects.

gPROMS ModelBuilder.Advanced custom modelling capabilities within a process flowsheeting environment.

gPROMS ModelBuilderThe world's leading Advanced Process Modelling environment

gPROMS ModelBuilder® is the custom modelling and flowsheeting environment at the heart of thegPROMS platform products.

ModelBuilder combines industry-leading custom modelling capabilities

with a process flowsheeting environment, to provide the process

industries' most powerful advanced process modelling tool.

What is it used for?

ModelBuilder is used to build, validate, execute and deploy steady-state and dynamic

process models of any complexity.

Its powerful process modelling language is used to create high-fidelity predictive

models of chemical processes, which can be validated against experimental or plant data

using built-in advanced parameter estimation techniques.

Once you have a validated model, you can use ModelBuilder's steady-state and dynamic

simulation and industry-leading optimisation capabilities to generate high-accuracy

predictive information for decision support in product and process innovation, design

and operation.gPROMS is now the leading modelling product within the chemical industries

D R G E O F F R E Y R O B I N S O N F R E N G , R O Y A L A C A D E M Y O F

E N G I N E E R I N G

What is ModelBuilder and how does it work?ModelBuilder is a process flowsheeting tool and an advanced custom modelling environment within the same environment.

MORE ABOUT MODELBUILDER

Why is ModelBuilder different?Process simulation has been around for many decades. Why is ModelBuilder now transforming the way the process industries design and operate?

FIND OUT MORE

Model LibrariesModelBuilder's steady-state and dynamic model libraries can be augmented by the Advanced Model Libraries for reaction and separation, leaders in their fields

FIND OUT MORE

Modelling power: a plot from a model of a pulsed catalytic reactor, showing concentration variation over time

Who uses ModelBuilder?

ModelBuilder is designed for a range of modelling users within the typical process

organisation. Some typical groups are:

expert custom modellers creating models of complex processes or units – for example,

a fluidised-bed reactor – for use in process or detailed equipment design

process engineers combining custom models with library models – for example,

distillation columns – from the ModelBuilder process model libraries

reaction engineers, creating and deploying high-accuracy predictive models of reaction

processes for detailed reactor design, operational analysis and troubleshooting, and

catalyst screening and ranking

R&D personnel analysing experimental data and fitting model parameters using

ModelBuilder's advanced parameter estimation capabilities

chemists and scientists analysing experimental data and fitting a mechanistic model to

observed data, or designing optimal experiments.

control engineers using gPROMS dynamic models to design and test control schemes

using high-fidelity non-linear models, or to generate information for use in control

design within MATLAB®

process optimisation experts exploring the decision space to determine optimal plant

economics.

Across the process lifecycleModelBuilder provides a single environment from laboratory R&D and conceptual design through FEED and detailed engineering to operating plant.

SEE HOW

Deploying models within other engineering softwareYou can bring the benefits of advanced process models to groups across your organisation by deploying gPROMS models within a range of software environments.

FIND OUT MORE

Plot showing trimodal molecular weight distribution in a polymer

Application

ModelBuilder can be applied across the process lifecycle all the way from process or

product development in the laboratory to support of online plant operations, by users

from many different disciplines.

It is used across the process industries, from oil & gas and refining, through chemicals,

petrochemicals & polymer, to food & beverage and pharmaceutical manufacture.

It also provides support at all stages of the model lifecycle: model construction,

validation, execution, deployment and subsequent maintenance. This ensures that

models can easily be maintained and extended to provide continual return on

investment.

Instructions: Unrar and install the app using the settings:- License Manager: Use remote license server- Specify license server: 12345@localhostCopy \patch\gPROMS-core_3.5.1.54826\licenses\license.datto <installdir>\gPROMS-core_3.5.1.54826\Licenses\(or elsewhere defined in yourPSELMD_LICENSE_FILE environment variable)Copy \patch\gPROMS-core_3.5.1.54826\bin\*.* to<installdir>\gPROMS-core_3.5.1.54826\bin\Install the FlexLM license manager.Close LMTOOLS when it launches automatically.Copy the included \patch\FLEXlm\bin\*.* to<FlexLMProgDir>\bin\Run copied License_setup.batAlternatively, you can manually setup thepath to the PSELMD license file:lmutil.exe lmpath -override PSELMD "C:\Program Files\PSE\gPROMS-core_3.5.1.54826\Licenses\License.dat"lmutil lmpath -statuslmgrd -z -c license.datmikedavish111