CFD reduced model based process control

1. CFD reduced model based process control Practical application of RMPC to a container glass plant Ton Backx

2. Outline of the presentation • Introduction and motivation of the RMPC technology • Proper Orthogonal Decomposition based model reduction • Application of the technology to a container glass melter • Crown controller • Bottom controller • Forehearth controller • Summary of results

3. Introduction of the RMPC technology Model predictive control explicitly applies a process model for the prediction of expected near future process behavior and for calculating best control actions • Traditional model predictive control uses a local linear approximate process model obtained from process identification • RMPC is based on a validated (CFD) process model. It applies a POD based reduced model and covers the complete process operating range

4. Introduction of the RMPC technology disturbances manipulated variables controlled variables Unit Process Operating Constraints f g Disturbance Model measured disturbances + - Process Model Process Model Process Model Setpoints Set ranges Controller Optimization and constraint handling Model Predictive Controller Model Predictive Control explicitly applies models • to predict future process output behavior • to determine the best future input manipulations to drive the process to optimum conditions • to feedforward compensate disturbances • to respect operating constraints and to determine optimum conditions • To handle non-linearities

5. Introduction of the RMPC technology Theoretical knowledgeChemical Engineering, chemistry, physics, control theory Performance informationOperating procedures, events ‘Real-world’ data:Laboratory data Operating data External calculationsSpecialist properties, CFD, etc. Mechanical data:Equipment geometry Material properties External informationExternal demands, commercial information What goes into a model? Any level of detail

6. Introduction of the RMPC technology Process Design Modeling Licensing 3rd Party Master Model(s) R&D Integrated Technology Solution Validation Layer Product Quality Observers Knowledge Optimization Business & Operating Units Catalyst yield Observers MPC Control Base Control configuration Transition Optimization Commissioning Package Training Simulator Reliability Observers Production Accounting Performance Monitoring

7. Introduction of the RMPC technology Plant or laboratory data Model construction& maintenance Optimal designEquipment & controlOperating procedures OperationsOTSDecision supportTroubleshooting Validated model Initial model Model validationParameter EstimationExperiment Design Offline Online Raw plant data Reconciled plantinformation Data reconciliation Parameter (re)-estimation Yieldaccounting Updated parameters Plant Up-to-date model Softsensing Model-predictive control Modelsimplification Process Health Monitoring Simplified model(s) Decision support Equipment Health Monitoring Optimisation(steady-state, dynamic) Dynamic simulation Diagnosis and troubleshooting Optimal set-points & trajectories High-accuracy control based on up-to-date rigorous models Unmeasured process quantities SoftSensing One model, many benefits !

8. POD based model reduction All on-line applications only require process simulations over well defined, restricted operating windows, whereas process design and process engineering require coverage of large operating windows. • Model reduction technologies derive from an accurate master model those behavioral aspects, which are relevant for a specific application: • Model based predictive control (e.g. balanced operation of main process mechanisms: batch melting, mixing, fining/re-fining, homogenization, prevention of refractory damaging, …) • Process performance optimization (e.g. color transitions, load transitions, batch material composition transitions, …) • “What-if” scenario analysis (e.g. recovery from major process upset, change in operating condition, …)

9. POD based model reduction Proper Orthogonal Decomposition is a model reduction technique that enables: • discrimination between relevant and irrelevant behavioral aspects for a specific application (e.g. concentration on limited range of process operating conditions) • determination of the best approximate model that represents relevant process behavior compactly (e.g. coverage of limited load range, fixed transition recipes, …) • retainment of providing insight in physical/chemical process behavior of main process mechanisms (e.g. reduced models still enable calculation of local temperatures, velocities, pressures, concentrations, …)

10. POD based model reduction Proper Orthogonal Decomposition is a model reduction technique that essentially covers two steps in model reduction: • Creation of a snapshot matrix that can best be described as making a movie of relevant dynamic process behavior • Derivation of an approximate model that enables straigthforward reconstruction of the behavior covered in the snapshot matrix

11. POD based model reduction Snapshot 1 Snapshot 2 Snapshot 3 F(x,t1) F(x,t2) F(x,t3) The movie of the relevant behavior of the process or so-called snapshot matrix has two dimensions: • Pictures that give an accurate representation of all relevant process variables or process states • Sequencing of the pictures in such a way that an accurate representation is obtained of the time-dependant or dynamic process behavior

12. POD based model reduction Like a movie the snapshot matrix represents both a detailed representation of all relevant process variables and their relevant dynamic behavior • each column of the snapshot matrix represents a detailed picture of all relevant process variables at a time instant • each row represents the dynamic behavior of a specific variable

13. POD based model reduction

14. POD based model reduction Two approaches have been developed for determining the POD time function coefficients • System identification technique based estimation of ai(t) • Results in linear approximation of relevant process dynamics • Delivers very fast simulating approximate process model (>10.000 times real-time) suited for model based predictive process control • Back substitution of selected basis functions into original set of partial differential equations and minimization of residual matrix function by means of Galerkin projection • Results in non-linear approximation of relevant process dynamics • Delivers fast simulating approximate process model (>100 times real-time) suited for ‘What-if” scenario analysis and dynamic optimization

15. POD based model reduction

16. Validation POD model on GTM data

17. Furnace control INCACrown Air & ratio set points Boosting set points INCABottom Crown set points

18. Crown controller Temp controlled 2006 Temp. not controlled (2004)

19. Crown controller Crown controller CVs (Crown controller is the slave controller) Crown temp. which is controlled in zone to stabilize batch position, too low temp. indicates batch islands are floating near the back wall Controller switched on Crown temp. that is used as MV for bottom

20. Throat temperature Comparison previous control and RMPC IDEAL TI----.PV Fe Conc.

21. Bottom controller Comparison 2005 and 2006 Color change (no control) Uncontrolled throat temperature Controlled throat temperature Color signal

22. Bottom controller Comparison 2005 and 2006 No control Controlled bottom temperature middle Color signal

23. MPC of the temperature profile of a glass feeder

24. MPC of the temperature profile of a glass feeder

25. MPC of the temperature profile of a glass feeder

26. MPC of the temperature profile of a glass feeder

27. Summary of results • Glass temperature profile is fixed and standard deviation of critical temperatures is reduced to <50% by the bottom controller • Batch blanket coverage and crown temperature profile are stabilised by the control system in furnace control • Glass flow is stabilised and fixed at right conditions for good quality • Use of gas in favor of boosting to reduce costs of operation • Application of RMPC prevents the need for extensive testing • The POD technique results in a model that gives the same simulation result as the original GTM-X. An ultra-fast simulation model results that covers the full process operating window for control. • POD is the missing link between GTM-X based CFD simulations and the required fast models for the rigorous model based predictive control (RMPC). • POD based model reduction is a generic approach for numerical solution methods such as CFD and FEM for melting and forming.