Integrating Control and Process Monitoring Technologies ksmith@perceptive-engineering.co.uk

1. Integrating Control and Process Monitoring Technologies ksmith@perceptive-engineering.co.uk

2. Overview • Motivation • Process Monitoring meets Control • Simulation case study • Summary

3. Motivation • Model Predictive Control (MPC) is widespread and industrially proven • However, MPC has its weaknesses • Dealing with sensor failures • Coping with process abnormalities • Best model for current operation • Obscuring process deterioration • Can Multivariate Statistical Process Control help?

4. Operator Alert MVSPC Supervision Process MPC Process Process Monitoring meets Control • Multivariate Statistical Process Control (MVSPC) • Detects process abnormalities (special cause variation) • Classifies operating conditions • Validates & infers sensor measurements • Model Predictive Control (MPC) • Regulates the process (reduces common cause variation) • Complementary objectives

5. Multivariate Statistical Process Control (MVSPC) • MVSPC is a data driven technology • Models (PCA, PLS) capture principal correlations that exist between process variables • ‘Scores’ or principal components • A small number of scores adequately describe the process data • Single metrics (T2, SPE) computed from the scores indicate • Process Abnormalities • Shifts in operating conditions

6. Model Predictive Control (MPC) • Multivariable model used to account for process interactions • Actuation moves are determined using the model to meet control objectives over a future horizon • Set point regulation • Soft and hard constraint management • Prioritisation • Models are typically of a linear incremental ARX/FIR format

7. DI05 F88 T12 T13 T22 T23 DI06 PC14 FC11 DC05.OP Product Feed Simulation Case Study Control of a multiple effect milk evaporator OBJECTIVES Control: DI05 to set point Maintain all other process outputs within soft constraints Maintain FC11 at a target feed rate Manipulate: Steam pressure, feed rate, bleed valve

8. T12 MSPC prediction clearly detects fault MPC prediction does not indicate a fault Effect 1 temp Effect 1 temp Effect 2 temp Effect 2 temp Effect 3 temp Effect 3 temp Effect 4A temp Effect 4A temp Effect 4B temp Effect 4B temp Effect 5A temp Effect 5A temp Effect 5B temp Effect 5B temp 85 minutes 85 minutes Sensor Fault A sensor fault of 0.5 °C is gradually added to the first effect temperature over 85min

9. DI06 DC05.OP FC11 DI05 T12 T13 T23 PC14 Product Feed 56 min Sensor Fault inferential control controller reacts Feed rate FC11 Steam pressure PC14 Control valve DC05.OP Int. Density DI06 Fin. Density DI05 Effect 1 T12 inference Onset of fault Control switches to inferential 56 min Onset of fault Constraint violation

10. DI05 PC14 FC11 DC05.OP Squared Prediction Error (SPE) Probability of Error (0-100%) Product SPE from MVSPC process monitor indicates a problem Feed Process Abnormality • E.g. a change in the response of final density to changes in process inputs

11. DI05 PC14 FC11 DC05.OP Product Feed Process Abnormality Which part of the process is affected? MVSPC process monitor shows that final density is the biggest contributor to error

12. DI05 PC14 FC11 DC05.OP Int. Density DI06 Fin. Density DI05 Effect 1 T12 Effect 2 T13 Feed rate FC11 Product Steam pressure PC14 Feed Control valve DC05.OP 2.7 hours Process Abnormality Which part of the process is affected? Predictions from MVSPC process monitor show that final density is the biggest contributor to error

13. DI06 T13 T12 T23 DI05 PC14 Feed rate FC11 Steam pressure PC14 Control valve DC05.OP Int. Density DI06 Fin. Density DI05 Product Effect 1 T12 Effect 2 T13 Feed Effect 3 T14 2.7 hours Process Deterioration • E.g. fouling of evaporator effects MPC maintains control objectives

14. DI05 PC14 T12 T13 T23 DI06 Product Feed 2.7 hours Process Deterioration Detecting the abnormality Squared Prediction Error (SPE) SPE from MVSPC process monitor indicates a problem Probability of Error (0-100%)

15. T23 T12 T13 DI06 PC14 DI05 Int. Density DI06 Fin. Density DI05 Effect 1 T12 Effect 2 T13 Effect 3 T14 Product Effect 4A T15 Effect 4B T16 Feed Effect 5A T17 2.7 hours Process Deterioration Which part of the process is affected? Predictions from MVSPC process monitor show that ALL parts of the process are affected. ALL of the controlled variables

16. DI05 PC14 T12 T13 T23 DI06 Product Feed Process Deterioration Which part of the process is affected? Predictions from MVSPC process monitor show that ALL parts of the process are affected. ALL of the controlled variables

17. DI05 T12 T13 T23 DI06 PC14 FC11 DC05.OP Product Feed Shift in Operating Conditions MVSPC process monitor can classify current operating conditions and direct model switching in MPC

18. Summary • MVSPC process monitors can detect special cause variation and supervise the MPC • SENSOR FAULTS. Process monitor provides inference and switches MPC to control on inference • PROCESS ABNORMALITIES. Process Monitor can identify effected parts of the process and re-tune MPC accordingly. • PROCESS DETERIORATION. Process Monitor can determine that the process has changed even though MPC maintains control. • SHIFTING OPERATING CONDITIONS.Process Monitor can classify operating conditions and direct model switching in the MPC. • Can Multivariate Statistical Process Control help? YES !!