1. Delft Center for Systems and Control 1 Model-based process control and optimization Okko Bosgra Paul Van den Hof Adrie Huesman Delft Center for Systems and Control

2. 2 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

3. Delft Center for Systems and Control 3 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

4. Delft Center for Systems and Control 4 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

5. Delft Center for Systems and Control 5 Our approach Smart operation and design of industrial processes through control and optimization on the basis of dynamic models rt operation Smart operation

6. Delft Center for Systems and Control 6 The research ingredients Modelling First principles, nonlinear DAE’s/PDE’s, large scale, model reduction to goal-oriented models tractable for simulation/optimization/control, hybrid systems Data analysis Experiment design, data-based modelling, uncertainty bounding parameter estimation, model validation, soft-sensing state and performance monitoring, NL observers, learning Control and optimization Economic performance criteria, operational constraints, sustainability, performance limitations, instrumentation, MPC, RTO and their interaction, adaptation

7. Delft Center for Systems and Control 7 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

8. Delft Center for Systems and Control 8 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

9. Delft Center for Systems and Control 9 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 Bosgra TNO-MEP

10. Delft Center for Systems and Control 10 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

11. Delft Center for Systems and Control 11 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 Cooperation with PURAC and IPCOS

12. Delft Center for Systems and Control 12 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

13. 13 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 VR AR F1, A1 VT, AT F2

14. Delft Center for Systems and Control 14 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 Identificatie Identification/ calibration model Experiment data Experiment evaluation exp. design Xavier Bombois, Paul Van den Hof

15. Delft Center for Systems and Control 15 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