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Industrial Process Modelling and Control Ton Backx Emeritaatsviering Joos Vandewalle Outline • • • • • • History Process performance and process control Model predictive control essentials Process modeling Current developments Future perspective Emeritaatsviering Joos Vandewalle 4 september 2013 Page 1 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 4 september 2013 Page 2 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 4 september 2013 Page 3 Process performance and process control disturbances manipulated variables Process controlled variables Process performance is governed by: • Critical process and product variables –”Controlled Variables”- need to meet specifications • During startup, shut-down and product changeovers offspec products are produced - Need for minimization of transition losses • During production disturbances cause variations in critical variables - Need for disturbance rejection Emeritaatsviering Joos Vandewalle 4 september 2013 Page 4 Process performance and process control Model predictive control is the supervisory control layer that enables process optimization by minimization of production costs ensuring product specifications and production quantities Operating information – optimum operating conditions are determined by an optimizer (setpoints, set ranges, priorities and weights, operating constraints) – the model predictive control system realizes targets set by the optimizer Costs and Specifications Optimizer Operating information Targets (setpoints, setranges, …) MPC Process values setpoints PID Process Emeritaatsviering Joos Vandewalle 4 september 2013 Page 5 Process performance and process control Visualization of benefit realization by MPC Measured process signal 21 probability density function Cpk 0.96 Cpk == 0.96 Cpk = 4.3 1.6 20.8 20.6 20.4 Standard Control Model Predictive Predictive Control Controlwith performance without optimization optimization value 20.2 Economic benefit 20 19.8 19.6 19.4 000 22 0.5 414 66 1.5 828 10 10 2.5 12 12 3 19.2 19 0 0.2 0.4 0.6 0.8 1.2 1.4 4 september 2013 1.6 1.8 2 4 time probability density Emeritaatsviering Joos Vandewalle 1 x 10 Page 6 Model predictive control essentials MPC strength is based on the explicit use of (a) (set of) model(s): • to predict future process output behavior • to determine the best future input manipulations to drive the process to optimum conditions manipulated variables Operating Constraints measured disturbances • to feedforward compensate disturbances • to respect operating constraints and to determine optimum conditions • disturbances To handle non-linearities controlled variables Unit Process f g Disturbance Model Setpoints Set ranges Controller Optimization and constraint handling Model Predictive Controller 4 september 2013 7 Process Process Model Process Model Model - + Model predictive control essentials Past control manipulations Future control manipulations Control horizon Time (t) Predicted future process responses Setpoint value Past process responses Prediction horizon Dead time Past Emeritaatsviering Joos Vandewalle Output horizon applied for optimization Present moment 4 september 2013 Time (t) Future Page 8 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 Past Future Y f (t , N f ) Y fp (t , N f , N p ) Y ff (t , N f , N c ) H ( N f , N p )U p (t , N p ) T ( N f , N c )U f (t , N c ) Past Future In this expression: • Yfp denotes the part of the future outputs stemming from past input manipulations Cannot be influenced any more • Yff denotes the part of the future outputs resulting from future input manipulations Still to be determined by future inputs Emeritaatsviering Joos Vandewalle 4 september 2013 Page 9 Process modeling Process application example Emeritaatsviering Joos Vandewalle 4 september 2013 Page 10 Process modeling Laboratory experiment design and optimisation Process flowsheeting Detailed design and optimisation of process equipment Model-based automation applications for decision support Operator training Model Predictive control DESIGN Equipment performance monitoring Process Health monitoring CONCEPT DESIGN OPERATION EVO LVI N G MASTE R M O D EL New process design Detailed design of complex units Pn + M Pn+1 …. Emeritaatsviering Joos Vandewalle Simultaneous equipment and control design and optimisation Design of optimal operating procedures 4 september 2013 Page 11 Troubleshooting with detailed predictive models 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 4 september 2013 Page 12 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 Emeritaatsviering Joos Vandewalle 4 september 2013 Page 13 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 Emeritaatsviering Joos Vandewalle 4 september 2013 Page 14 Process modeling Back to the core of the problem of data-driven modeling / identification of Linear Time Invariant (LTI) models v v u G + r + - u G y C Emeritaatsviering Joos Vandewalle 4 september 2013 Page 15 + y Process modeling The classical identification problems: open loop closed loop v u G + y r + - u G v + y C Identify a plant model (and possibly r) on the basis of measured signals u, y • 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) Emeritaatsviering Joos Vandewalle 4 september 2013 Page 16 Current developments Next step in the development: • 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 Autonomous economic model-based operation of industrial process systems Emeritaatsviering Joos Vandewalle 4 september 2013 Page 17 Thank you for your attention Emeritaatsviering Joos Vandewalle 4 september 2013 Page 18