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Model-based process control and optimization

Model-based process control and optimization. Okko Bosgra Paul Van den Hof Adrie Huesman. 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.

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Model-based process control and optimization

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

  2. 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

  3. 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

  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. rt operation Smart operation Our approach Smart operation and design of industrial processes through control and optimization on the basis of dynamic models

  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. Projects • 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).

  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. 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. 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 Goal: Develop a methodology for monitoring using large-scale physical models Application: Monitoring the dryer section of papermaking machine Cooperation with TNO-TPD Robert Bos, Xavier Bombois, Paul Van den Hof

  11. Condensed vapor HX Cooling water Draft tube Fines Skirt baffle Annular zone Malvern Dilution Opus HX Feed Hot water Helos Dissolution vessel Product 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 • Challenge: • Strong non-linearity of the model • Distributed-parameter model • Lack of reliable measurements for • supersaturation and Crystal Size • Distribution (CSD) Cooperation with PURAC and IPCOS Ali Mesbah, Adrie Huesman, Paul Van den Hof

  12. 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. Challenge: 1. Identify geological reservoir properties and uncertainty associated with them. 2. Take this uncertainty into account in optimization procedure. Gijs van Essen, Maarten Zandvliet, Jorn van Doren, Paul Van den Hof, Okko Bosgra

  13. Delft Center for Systems and Control Economic dynamic process optimization General goal: Improve economic performance (profit or cost) by dynamic optimization. VR AR VT, AT F1, A1 F2 • Challenge: • Economics implies plantwide scope so large scale (→ model reduction). • Multiple solutions rather than a unique solution (→ selection by lexicographic optimization). • Deal with uncertainty like disturbances and model mismatch (→ feedback, integration of RTO and MPC). Adrie Huesman, Okko Bosgra, Paul Van den Hof

  14. On-line model and controller calibration/learning Towards an automatic procedure for economic control optimization: exp. design Experiment Experiment Experiment • 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 data Identification/ calibration Identificatie model Control design controller Performance Monitoring On-line iterative procedure evaluation Xavier Bombois, Paul Van den Hof

  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

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