INCOOP Workshop Düsseldorf, January 23-24, 2003

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1 Plant-wide on-line dynamic modeling with state estimation: Application to polymer plant operation and involvement in trajectory control and optimization. Philippe Hayot Global Process Engineering The Dow Chemical Company INCOOP Workshop Düsseldorf, January 23-24, 2003

2 This presentation is referring to the following trademarks or registered trademarks STYRON is a trademark of The Dow Chemical Company Aspen Custom Modeler (ACM), Aspen SEM, InfoPlus.21, Aspen Process Explorer and SpeedUp are trademarks or registered trademarks of Aspen Technology Inc. INCA and PathFinder are trademarks of IPCOS gproms is a trademark of PSE Ltd 2

3 Content Dow and its Polystyrene business Model based applications as enabler of the business strategy elements Advanced Process Information on-line dynamic model with state estimation application examples implementation status Trajectory control within IMPACT project Trajectory optimization within IMPACT project Future Directions 3

STYRON is a Trademark of The Dow Chemical Company Dow and its Polystyrene business Dow is the global leader in Polystyrene with around 20 plants worldwide Consequently: consistent product quality

4 STYRON is a Trademark of The Dow Chemical Company Dow and its Polystyrene business Dow is the global leader in Polystyrene with around 20 plants worldwide Consequently: consistent product quality maximizing unit production leveraging of standardization and operating discipline are key business strategy elements Dynamic modeling with state estimation has proven to be a powerful enabler in achieving these objectives. 4

5 Polystyrene solution process Styrene + Solvent Styrene Solvent (Initiator) Rx Rx Rx Polystyrene 5

6 Implementation using Aspen SEM and Aspen Custom Modeler Aspen SEM is a general purpose non-linear dynamic data reconciliation solver using an Extended Kalman Filter linked with ACM for model predictions and time varying linear state space models. Main applications : continuous, real-time estimation of relevant process variables that are unmeasureable or infrequently measured Rejection of unknown disturbances and model deficiencies by adjusting parameters via introduction of stochastic states (disturbance model) Look-ahead capability Process monitoring and decision support tool allow Data Reconciliation to be combined with Multivariate Statistical Process Control techniques for fault detection and diagnosis Model Predictive Control 6

7 State estimation architecture PROCESS PLANT Plant INPUTS DCS Plant OUTPUTS Model OUTPUTS Integrate Dynamic Model Initialise Dynamic Model Model/Plant Mismatch Linearisation Discretization Model PREDICTIONS Kalman Gain Kalman CORRECTIONS Kalman INNOVATIONS 7

8 Aspen SEM applications Particularly suited for : Multi-Product Processes Frequent and Significant Transitions Frequent Unknown Disturbances Steady-State approximations not valid Batch Processes Different operating modes : On-line real time with plant real-time database Off-line faster than real time with historical data (MS Excel as repository) Synchronized with ACM emulation model or with a control application (via dbase) On-line emulation with plant database populated by an ACM virtual plant model Key building block for model based Process Information, Monitoring and Control systems 8

9 Applications for Polystyrene at Dow Increased production rates : better understanding and timely information to plant operation ability to relax some constraints with same reliability Reduced transition times and off-spec product : staying longer on Grade A and moving faster to Grade B no waiting for lab results in many cases understanding and removal of limiting steps Preventing upsets : look-ahead gives early warning leading to preventive action estimates of unmeasured process variables are used to diagnose and decide how to address operational issues Dynamic reconciliation of recycle stream composition 9

Architecture of implementation at Dow Library of models Library of models Main Dynamic Model Main Dynamic Model Aspen SEM Look-Ahead Styron is a Trademark of The Dow Chemical Company

10 Architecture of implementation at Dow Library of models Library of models Main Dynamic Model Main Dynamic Model Aspen SEM Look-Ahead Styron is a Trademark of The Dow Chemical Company Database and Historian DCS Single Loop Control Positions for valves, pumps,... Process equipment and instrumentation Inputs User User Interface Measurements/Inferentials Information 10

11 Application example (1) : Process Inferentials Measured and unmeasured components in a stream 11

12 Application example (2) : Product Inferentials Continuous property estimates with infrequent sampling 12

13 Application example (3) : faster from prime to prime Look-ahead prediction indicates prime time 13

14 Application example (4) : troubleshooting Relative behavior of PV estimates explains overshoot 14

15 Application example (5) : off-line FTRT Off-line study on the effect of KF updates on final product property 15

16 Advanced Control and Optimization Product specifications and objective function Off-line simulation Off-line simulation Main Dynamic Model Main Dynamic Model Dynamic Optimization leading to optimized setpoint trajectories Best Practice trajectories Product setpoints MIMO constrained Model Predictive Control with given setpoint trajectories State Estimation Look-Ahead Recipe and ramps Process setpoints Database and Historian SISO unconstrained ModV Single Loop Control Positions for valves, pumps,... Process equipment and instrumentation Inputs User User Interface Measurements/Inferentials Information 16

17 Project IMPACT IMproved Polymer Advanced Control Technologies European Research Project (Eureka) Belgium : ISMC, KU Leuven, Dow Belgium The Netherlands : IPCOS, TU Delft, Dow Benelux Dow Scope : Feasibility of MPC and Trajectory Optimization applied to the Polystyrene process Application on dynamic model of a Polystyrene plant Design and simulate the application of a constrained multivariable controller for trajectory control Design and execute trajectory optimization based on existing process model Economic evaluation 17

18 Project IMPACT : advanced control (1) Trajectory control Moving from one operating point to another following a best practice path Dealing with non-linearities through : Delta mode configuration Multiple linear models MV traj BEST PRACTICE Trajectory RECIPE TRAJECTORY Optimizer CV traj + + MV LinMod Control engine INCA INCA CV + MV pv Process The (Model) CV pv 18

19 Project IMPACT : advanced control (2) Prioritized control Class 0 MV ROC constraints MV POS constraints Safety Class 1 Requirement 1 Requirement n 1 LS Solution restricted by the solution of class 0 Quality Freedom left? no Ready yes Requirement 1 Class i Requirement n i LS Solution restricted by the solution of class 0 to i-1 Importance Productivity Other... Class last Minimize MV moves 19

20 Project IMPACT : advanced control (3) Related application with model based multivariable control using INCA : Production rate increase for on-aim or within specification mode PROP1 a b c d e Time scale PROP2 Production Increased Ideal Rate a b c d e Time scale a b c d e Time scale Priority : PROP1/PROP2 < Production Controller with around 10 MV/20 CV applied to simulation based plant emulation 20

21 Project IMPACT : advanced control (4) Trajectory control example : Following a best practice trajectory = history data for MV s & CV s Disturbance Original Trajectory Controlled Trajectory CV1 - Constraint MV1 CV2 - Quality MV2 CV3 - Productivity 21

22 Project IMPACT : Trajectory optimization (1) Economically Optimal Grade Transitions AV ( T ) Objective = Maximize Added Value during a transition = T price j ( t) throughput j ( t) dt 0 j T 0 i cost i ( t) feed i ( t) dt REVENUES COSTS Quality Parameter Time 22

23 Project IMPACT : Trajectory optimization (2) PathFinder, a tool for calculation of Economically Optimal Dynamic Grade Transitions Find dynamic MV s such that objective is optimized subject to process operation constraints (Off-line) Trajectory Optimizer Objective Calculation MV CV gproms, SpeedUp, ACM, Process Model 23

Project IMPACT : Trajectory optimization (3) Economically Optimal Dynamic Grade Transitions Fast (Off-Line) Trajectory Optimization Prices Specifications MV s gproms, ACM, SpeedUp, Mass flow Quality

24 Project IMPACT : Trajectory optimization (3) Economically Optimal Dynamic Grade Transitions Fast (Off-Line) Trajectory Optimization Prices Specifications MV s gproms, ACM, SpeedUp, Mass flow Quality Constraints Economic Objective Objective Process Model Smoothly Non Linear Long Calculation Time Economic Objective Highly Non Linear Short Calculation Time REPLACE WITH FAST LINEARIZED MODEL (SSQP) Typically: 10 Process model evaluations/linearizations needed 24

25 Project IMPACT : Trajectory optimization (4) SSQP SQP run on Linearized model + economic criterion Initial Trajectory Intermediate Result Final Trajectory Check on Rigorous Model Improved? No Yes Derive New Linear Time Variant model Adapt Trust Region 25

26 Project IMPACT : Trajectory optimization (5) PathFinder applied to a validated rigorous model of a Dow polystyrene production facility at Tessenderlo, Belgium 14 Manipulated Variables with 13 move times = 182 Degrees of freedom Absolute Boundaries on all MV s Rate of Change Constraints on 10 MV s Path Constraints on 8 Process Variables 26

27 Project IMPACT : Trajectory optimization (6) Example of result : Trajectory Optimization based on market situation Production PC1 PROP2 MV3 PROP1 MV1 Original Case 1 Case 2 Price Onspec-Offspec : Not Applicable Price Offspec-Styrene : Not Applicable Legend : High Negative Low Positive Simulation based results 27

28 Project IMPACT Integrated Trajectory Control and Optimization u Latest Process Model y PathFinder Off-Line u opt Optimal Trajectory Recipe y opt On-Line + + u MPC INCA y + - u Process y Extended Kalman Filter 28

29 Future directions More model based advanced process information implementations and applications Real life application of transition control in a polymer plant Combined application of transition optimization and control Other area of particular interest : Robust dynamic modeling for optimization and control applications Real-time integrated dynamic optimization and control Robust non-linear model predictive controllers Non-linear model reduction Operator training Related papers : W. Van Brempt, P. Van Overschee, T. Backx, J. Ludlage, P. Hayot, L. Oostvogels, S. Rahman, Economically Optimal Grade Change Trajectories: Application on a Dow Polystyrene Process Model, ESCAPE-12, The Hague, The Netherlands, W. Van Brempt, P. Van Overschee, T. Backx, J. Ludlage, P. Hayot, L. Oostvogels, S. Rahman, Grade Change Control using INCA Model Predictive Controller: Application on a DOW Polystyrene Process Model, invited paper at the American Control Conference 2003, Denver, Colorado, USA, June P.Hayot, S.Papastratos, Going on-line with dynamic models using Aspen Custom Modeler and Aspen SEM, AspenWorld 2002, Washington D.C., USA, October

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