Model-based process control and optimization...1 Delft Center for Systems and Control Model-based...

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Delft Center for Systems and Control

Model-based process control and optimization

Okko BosgraPaul Van den HofAdrie Huesman


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• Established 1 January 2004, as a merger between3 systems and control groups from EE, ME and AP

• One of the six departments within Faculty 3mE

• Interdisciplinary research program,around fundamental development of S&C in connection with 3 technology domains:

• Mechatronics and Microsystems• Traffic and Transportation• Sustainable Industrial Processes

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• Coordinated courses in system dynamics and controlin the BSc/MSc programs of ME, EE, AP, ChemE, ..and in the independent MSc Systems and Control

• Composition includes: 5 full profs, 12 academic staff,10 Postdocs, 35 PhD students, 30 MSc students.Different backgrounds: ME, EE, ChemE, AP, Aero, Math

• Involved in process control and optimization:Paul Van den Hof, Okko Bosgra, Adrie Huesman,Xavier Bombois, Robert Babuska,…+ around 7 PhD students

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Sustainable Industrial Processes

Sustainable Industrial Processes Technology demands

• Increase of scale in process operation/optimizationunit � plant � site � market

• Increase of flexibility in operation (change-over's)

• Economic optimization of (dynamic) processes,under operating constraints (.., life cycles, supply chains)

• New processes (process intensification) with increasedopportunities for and need of actuation/sensing

• Higher level of autonomy in economic process operations

• Towards model-based process management, using allavailable resources: knowledge, (historical) data

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Our approach

Smart operation and design of industrial processesthrough control and optimization on the basis of dynamic models

rt operationSmart operation

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The research ingredients

ModellingFirst principles, nonlinear DAE’s/PDE’s, large scale,model reduction to goal-oriented models tractable for simulation/optimization/control, hybrid systems

Data analysisExperiment design, data-based modelling, uncertainty boundingparameter estimation, model validation, soft-sensingstate and performance monitoring, NL observers, learning

Control and optimizationEconomic performance criteria, operational constraints, sustainability, performance limitations, instrumentation,MPC, RTO and their interaction, adaptation

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• Model-based monitoring, control and optimization in large scale nonlinear industrial processes

• Modelling and control of waste incineration plants (TNO-MEP)

• Generic tools with case study in paper production process (TNO-TPD)

• Smart wells operation in reservoir engineering (CiTG, Shell, MIT, TNO)

• Modelling and control of crystalization processes (EU, PURAC, BASF, P&E)

• Water purification processes (Amst. water supply, ABB, DHV, Senter)

• Modelling and optimiz. of emulsification processes (EET, Unilever)

• Bubble/flow control in chemical reactors (Kramer’s Lab)

• Economic dynamic process optimization (Shell Global Solutions)

• Reduction of computational effort for on-line control and optimization (PROMATCH) (EU project with IPCOS, Cybernetica, PSE, Norwegian University of Technology, Imperial College London, RWTH Aachen, DCSC and TU/e).

Projects

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Projects

• Smart parameterizations (orthogonal basis functions) in identification and optimization (NWO)

• Data-based modelling for control; (closed loop) system identification

• Nonlinear modelling and control

• Identification of LPV models (NWO)

• Robust and scheduled controller synthesis

• Complexity reduction in modelling and control

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Model based Control of MSW Combustion

Goal: Develop control strategy that

• minimizes influence of disturbances due to variation in waste composition

• maximizes waste throughput and energy output

• guarantees fulfillment environmental regulations

Martijn Leskens, Paul vd Hof, Okko BosgraTNO-MEP

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Monitoring using large-scale physical models

Physical models of large-scale systems tend to be high order, nonlinear and computationally intensive. This makes them unusable for standard monitoring techniques

Cooperation with TNO-TPD

Goal:

Develop a methodology for monitoring using large-scale physical models

Application:

Monitoring the dryer section of papermaking machine

Robert Bos, Xavier Bombois, Paul Van den Hof

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Model Predictive Performance Control of Industrial Crystallizers

General goal:Design and implementation of an observer-based Model Predictive Control system for industrial crystallization processes

Ali Mesbah, Adrie Huesman, Paul Van den Hof

Challenge:

• Strong non-linearity of the model• Distributed-parameter model• Lack of reliable measurements for

supersaturation and Crystal Size Distribution (CSD)


Product

Fines

FeedHX

Malvern

Helos Hot water

Dissolution vessel

Opus

Condensedvapor

Dilution

HXCooling water

Annular zone

Draft tube

Skirt baffle

Cooperation with PURAC and IPCOS

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Control in reservoir engineering

General goal:Find optimal valve settings of water injection and oil production wells that are robust against geological uncertainty.

Gijs van Essen, Maarten Zandvliet, Jorn van Doren, Paul Van den Hof, Okko Bosgra

Challenge:

1. Identify geological reservoir properties and uncertainty associated with them.

2. Take this uncertainty into account in optimization procedure.


Video Clip

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Economic dynamic process optimization

General goal:Improve economic performance (profit or cost) by dynamic optimization.

Adrie Huesman, Okko Bosgra, Paul Van den Hof

Challenge:

1. Economics implies plantwide scope so large scale (→model reduction).

2. Multiple solutions rather than a unique solution (→ selection by lexicographic optimization).

3. Deal with uncertainty like disturbances and model mismatch (→ feedback, integration of RTO and MPC).


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On-line model and controller calibration/learning

Towards an automatic procedure for economic control optimization:

• Automatic control performance monitoring• Economic criteria for model calibration (when is it profitable/necessary to do additional experiments)

• Least costly experiment design for control-relevant model update(experiment as short as possible, directed towards the control-relevant parts)

• Controller calibration

On-line iterative procedure

Performance Monitoring

Control design

controller

IdentificatieIdentification/calibration

model

Experiment

data

ExperimentExperiment

evaluation

exp. design

Xavier Bombois, Paul Van den Hof

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Particular research challenges

• From complex physical models to reduced models feasible for use in operational strategies

• Integration of design and control

• From control to dynamic economic (plantwide) optimization

• Merging of physical and experimental models

Model-based process control and optimization...1 Delft Center for Systems and Control Model-based...

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