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Delft Center for Systems and Control
Model-based process control and optimization
Okko BosgraPaul Van den HofAdrie Huesman
Delft Center for Systems and Control
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Delft Center for Systems and Control
Delft Center for Systems and Control
• Established 1 January 2004, as a merger between 3 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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Delft Center for Systems and Control
Delft Center for Systems and Control • Coordinated courses in system dynamics and control in 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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Delft Center for Systems and Control
Sustainable Industrial Processes
Sustainable Industrial Processes
Technology demands
• Increase of scale in process operation/optimization
unit 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 increased opportunities for and need of actuation/sensing• Higher level of autonomy in economic process operations• Towards model-based process management, using all available resources: knowledge, (historical) data
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Delft Center for Systems and Control
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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Delft Center for Systems and Control
The research ingredientsModelling
First 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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Delft Center for Systems and Control
• 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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Delft Center for Systems and Control
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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Delft Center for Systems and Control
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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Delft Center for Systems and Control
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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Delft Center for Systems and Control
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)
Delft Center for Systems and Control
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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Delft Center for Systems and Control
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.
Delft Center for Systems and Control
Video Clip
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Delft Center for Systems and Control
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).
Delft Center for Systems and Control
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Delft Center for Systems and Control
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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Delft Center for Systems and Control
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