1. Industrial Process Modelling and Control Ton Backx Emeritaatsviering Joos Vandewalle

2. Outline History Process performance and process control Model predictive control essentials Process modeling Current developments Future perspective Emeritaatsviering Joos Vandewalle Page 1 4 september 2013

3. Model Predictive Control History Early developments of Model Predictive Control (MPC) technology were initiated by two pioneers: Dr. Jacques Richalet (Adersa, 1976) ­‘Model Predictive Heuristic Control’ (MPHC) using IDCOM as the MPC software for process identification (IDentification) and for control (COMmand) ­Use of Finite Impulse Response (FIR) models ­Control inputs computed by minimization of a finite horizon quadratic objective function without consideration of constraints ­Plant output behavior specified by reference trajectories Emeritaatsviering Joos Vandewalle Page 2 4 september 2013

4. Model Predictive Control History (cont’d) Dr. Charles Cutler (Shell Oil, 1979) ­‘Dynamic Matrix Control’ (DMC) ­Use of Finite Step Response (FSR) model ­Linear objective function subject to linear inequality constraints using a finite prediction horizon (LP) ­Plant output behavior specified by setpoints ­Optimum inputs calculated by solving a Linear Programming problem Emeritaatsviering Joos Vandewalle Page 3 4 september 2013

5. Process performance and process control Process performance is governed by: Critical process and product variables –”Controlled Variables”- need to meet specifications During startup, shut-down and product changeovers off- spec products are produced ­Need for minimization of transition losses During production disturbances cause variations in critical variables ­Need for disturbance rejection Emeritaatsviering Joos Vandewalle Page 4 4 september 2013 manipulated variables disturbances controlled variables Process

6. Process performance and process control Emeritaatsviering Joos Vandewalle Page 5 4 september 2013 Model predictive control is the supervisory control layer that enables process optimization by minimization of production costs ensuring product specifications and production quantities –the model predictive control system realizes targets set by the optimizer MPC Targets (setpoints, setranges, …) Operating information Process setpoints Process values PID –optimum operating conditions are determined by an optimizer (setpoints, set ranges, priorities and weights, operating constraints) Optimizer Costs and Specifications Targets (setpoints, setranges, …) Operating information Operating information

7. 0 0.5 1 1.5 2 2.5 3 probability density function probability density Cpk = 0.96 00.20.40.60.811.21.41.61.82 x 10 4 19 19.2 19.4 19.6 19.8 20 20.2 20.4 20.6 20.8 21 Measured process signal time value Process performance and process control Visualization of benefit realization by MPC 02468 1012 Cpk = 0.96Cpk = 4.3 02468 1012 Cpk = 0.96Cpk = 1.6 Economic benefit Standard Control Model Predictive Control without optimization Model Predictive Control with performance optimization 4 september 2013 Emeritaatsviering Joos Vandewalle Page 6

8. to predict future process output behavior to determine the best future input manipulations to drive the process to optimum conditions Model Predictive Controller Setpoints Set ranges Controller Disturbance Model measured disturbances Operating Constraints Optimization and constraint handling Model predictive control essentials MPC strength is based on the explicit use of (a) (set of) model(s): to feedforward compensate disturbances to respect operating constraints and to determine optimum conditions To handle non-linearities Unit Process manipulated variables disturbances controlled variables Process Model Process Model fg + - Process Model 74 september 2013

9. Model predictive control essentials Emeritaatsviering Joos Vandewalle Page 8 4 september 2013 Time (t) PastFuture Dead time Prediction horizon Predicted future process responses Setpoint value Past process responses Past control manipulationsFuture control manipulations Control horizon Output horizon applied for optimization Present moment

10. Model predictive control essentials Linear models are used to calculate the responses to past and future process input manipulations and similarly to predict future responses to known disturbances Emeritaatsviering Joos Vandewalle Page 9 4 september 2013 In this expression: Y fp denotes the part of the future outputs stemming from past input manipulations Y ff denotes the part of the future outputs resulting from future input manipulations Past Cannot be influenced any more Past Future Still to be determined by future inputs Future

11. Emeritaatsviering Joos Vandewalle Page 10 4 september 2013 Process modeling Process application example

12. Process modeling Emeritaatsviering Joos Vandewalle Page 11 4 september 2013 CONCEPT OPERATION DESIGN Detailed design and optimisation of process equipment Model-based automation applications for decision support Troubleshooting with detailed predictive models Process flowsheeting Detailed design of complex units Design of optimal operating procedures Simultaneous equipment and control design and optimisation E V O L V I N G M A S T E R M O D E L Laboratory experiment design and optimisation Operator training DESIGN Model Predictive control Equipment performance monitoring Process Health monitoring New process design Pn + M  Pn+1 ….

13. Process modeling System identification is the modeling technique applied in industry for sufficiently accurate modeling of the relevant process dynamics for MPC Data driven modeling ­Model set: Non-parametric, semi-parametric, parametric ­Model structure ­Parameter estimation criterion: Output error, equation error, input error Emeritaatsviering Joos Vandewalle Page 12 4 september 2013

14. Emeritaatsviering Joos Vandewalle Page 13 4 september 2013 Process modeling Required capabilities of models 1.Accuracy on-line assessment of model validity 2.Adaptability flexible on-line updating of models (dynamics and interconnection structure) 3.Active data-driven learning demands on accuracy, autonomy, robustness  active probing for information

15. Emeritaatsviering Joos Vandewalle Page 14 4 september 2013 Process modeling Example of current limitations: MPC projects in industry highly depend on accurate plant models and well-tuned controllers Controllers and models are verified (identified) during commissioning When during operation process behavior changes: MPC’s are switched to “manual” Loss of performance Expensive experimental campaign to re-identify the models is the only way out

16. Process modeling Emeritaatsviering Joos Vandewalle Page 15 4 september 2013 Back to the core of the problem of data-driven modeling / identification of Linear Time Invariant (LTI) models

17. Emeritaatsviering Joos Vandewalle Page 16 4 september 2013 Process modeling The classical identification problems: open loopclosed loop Identify a plant model on the basis of measured signals u, y (and possibly r) Several classical methods available (Prediction Error, subspace, Output Error, non-parametric,..) Well known results for identification in known structure (open loop, closed-loop, possibly known controller)

18. Emeritaatsviering Joos Vandewalle Page 17 4 september 2013 Current developments Next step in the development: Autonomous economic model-based operation of industrial process systems Bring plant operation / automation to higher level of autonomy Monitor plant performance and detect changes on-line Generate probing signals when necessary and based on economic considerations (least costly experiments) Re-identify models and retune controllers on-line Keep high performance control Use economic performance criteria

19. Thank you for your attention Emeritaatsviering Joos Vandewalle Page 18 4 september 2013