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Caterpillar - a chemometric tool for tracking process changes

APACT’04 Geir Rune Fl åten. Caterpillar - a chemometric tool for tracking process changes. Caterpillar. What Adaptive data analytical tool for detecting process changes or upsets. Caterpillar. What Adaptive data analytical tool for detecting process changes or upsets Why

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Caterpillar - a chemometric tool for tracking process changes

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  1. APACT’04 Geir Rune Flåten Caterpillar- a chemometric tool for tracking process changes

  2. Caterpillar • What • Adaptive data analytical tool for detecting process changes or upsets

  3. Caterpillar • What • Adaptive data analytical tool for detecting process changes or upsets • Why • Process changes can be an indicator of a problem in the process. Early detection of an upset increase the chance of successfully reacting and re-stabilise the process

  4. Caterpillar • What • Adaptive data analytical tool for detecting process changes or upsets • Why • Process changes can be an indicator of a problem in the process. Early detection of an upset increase the chance of successfully reacting and re-stabilise the process • How • Compare the now-variation with recent process variation. Significant difference indicates process changes

  5. Case study - BP Process Ethylene + Acetic acid + Oxygen  Vinyl acetate • Occasionally, wet quench or agglomeration events • Is it possible to detect the agglomeration events using acoustics?

  6. Case study - Instrumentation • Acoustic signals collected at one channel (one sensor) using PAA’s Granumet system • Scan rate: 60, 16 points per scan • Roughly, one measurement each minute • The power spectra as saved using Granumet, are used in the further analyses • Measurements performed summer 2002

  7. Case study- Acoustic measurements

  8. Case study - scores plot of acoustic signal

  9. Acoustic signals indicate the agglomeration events in the case study • Is it possible to find some measurement that reflects the state change clearly visible in the score plots? • Measurement criteria • Applicable online • Easy to interpret, i.e. useful as an operator tool

  10. Moving window t0+1 Caterpillar (prediction error) • Use a moving window • Calculate prediction error for samples within moving window • Calculate ‘new’ prediction error for samples just ahead of the moving window • ‘New’ prediction errors larger than critical prediction error indicate process changes

  11. Caterpillar

  12. Approach • Decide which samples to be used in model • Perform PCA decomposition, X=TPt + E • Correct number of components important • Calculate prediction error for model, di,model= ti(TtT)-1tidmax,model= (critical value) * dmax • Calculate prediction error for “new samples”dnew,i= tnew,i(TtT)-1tnew,iAtypical samples: dnew,i>dmax,model • Plot results  operator information • Move to next time point and start over from 1

  13. 3. and 4. Why prediction error? - statistics • From regression: V(êi) = σ2 (1- di) where di= xi(XtX)-1xi

  14. d = constant value Prediction error – two variables - geometry

  15. Sample i • di = xi(XtX)-1xi Prediction error – two variables

  16. dmodel Prediction error – two variables

  17. dmodel dcrit Prediction error – two variables

  18. dmodel dcrit Prediction error – two variables

  19. Prediction error – why adaptive Normal variation canbe larger than upset variation

  20. 5. Operator information • Occurrences plot • The prediction error for a fixed number of ‘new’ samples is calculated in each step • The number of ‘new’ prediction errors are counted and indicated in the occurrences plot • Several “occurrences” indicate a process change

  21. Occurrences plot, example

  22. Occurences plot – four occurences dmodel dcrit

  23. Case studyDoes it work? 15. August 2002

  24. Present conclusions • Acoustic measurements detect signal changes corresponding with agglomeration events • These changes are easily visualised in score plots for historical data • Caterpillar can be used to detect signal changes, and is a promising tool for future process monitoring by means of acoustic measurements • Caterpillar can also be used for other types of data, or for detecting other types of changes

  25. However • Caterpillar detect all sorts of signal changes in the acoustic signals • The acoustic measurements also detect signals not corresponding to agglomeration events

  26. Acknowledgement • Tony Walmsley • BP, Zaid Rawi • PAA, R. Belchamber • CPACT • EPSRC/DTI

  27. Does it work? 15. August 2002

  28. 15. August 2002

  29. Does it work? 11. September 2002

  30. 11. September 2002

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