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MLSEV Virtual. Classification and Regression for Quality Optimization

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Applying Classification and Regression to Quality Optimization, by José Cárdenas, Technical Services Manager at Indorama.

*MLSEV 2020: Virtual Conference.

Published in: Data & Analytics
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MLSEV Virtual. Classification and Regression for Quality Optimization

  1. 1. 2nd edition
  2. 2. #MLSEV 2 Applying Classification and Regression to Quality Optimization José Cárdenas Indorama Ventures Química Technical Services Manager
  3. 3. #MLSEV 3 Index •Background •Analysis methodology applied •Example of results obtained •Summary
  4. 4. #MLSEV About Indorama Ventures and PET Carboxylic acid process as raw material for PET production (polyethylene terephthalate) Background
  5. 5. #MLSEV About Indorama Ventures
  6. 6. #MLSEV About Indorama Ventures
  7. 7. #MLSEV About Indorama Ventures
  8. 8. #MLSEV About Indorama Ventures
  9. 9. #MLSEV About Indorama Ventures
  10. 10. #MLSEV Xylene Carboxylic acid PET About the process Process where applied ML
  11. 11. #MLSEV Xylene Carboxylic acid PET About the process Process where applied ML to optimize: - production cost - while keeping quality parameters
  12. 12. #MLSEV FEED PREPARATION REACTION CRYSTALLIZATION VACUUM FILTRATION DRYING COMPRESSION SOLVENT RECOVERY CATALYST RECOVERY ACETIC ACID XYLENE CATALYST CRUDE TA AIR BYPRODUCTS CATALYST RECYCLE SOLVENT RECYCLE About the process
  13. 13. #MLSEV FEED PREPARATION REACTION CRYSTALLIZATION VACUUM FILTRATION DRYING COMPRESSION SOLVENT RECOVERY CATALYST RECOVERY ACETIC ACID XYLENE CATALYST CRUDE TA AIR BYPRODUCTS CATALYST RECYCLE SOLVENT RECYCLE Production cost Quality About the process
  14. 14. #MLSEV FEED PREPARATION REACTION CRYSTALLIZATION VACUUM FILTRATION DRYING COMPRESSION SOLVENT RECOVERY CATALYST RECOVERY ACETIC ACID XYLENE CATALYST CRUDE TA AIR BYPRODUCTS CATALYST RECYCLE SOLVENT RECYCLE Background Sections where parameters are more relevant for ML
  15. 15. #MLSEV BigML METHODOLOGY APPLIED TARGET To obtain a model for optimization for process parameters focused on carboxylic acid quality while minimizing raw material and solvent losses • Some reaction parameters can predict raw material consumption • These parameters are combined with quality to look for optimum conditions
  16. 16. #MLSEV BigML METHODOLOGY APPLIED
  17. 17. #MLSEV BigML METHODOLOGY APPLIED
  18. 18. #MLSEV 3FC201_PVUJD24_GM93FC202_PV3FC202_PV_RATIOUJD22_W30UJD24_W30UJD24_432UJD24_517UJD24_GI4UJD24_314UJD24_B503FC113_PVUID22_B49 MX FEED 3FC201_PV 1,00 0,25 0,43 -0,06 -0,17 -0,06 -0,01 0,01 0,29 -0,04 0,05 0,76 0,27 % MX IN FEED UJD24_GM9 0,25 1,00 -0,01 0,00 -0,15 -0,14 -0,12 0,02 0,23 0,02 0,03 0,15 0,36 SOLVENT FLOW 3FC202_PV 0,43 -0,01 1,00 -0,04 -0,18 -0,25 -0,20 -0,16 0,17 -0,07 0,06 0,34 0,12 SOLVENT RATIO 3FC202_PV_RATIO -0,06 0,00 -0,04 1,00 -0,01 0,02 0,01 -0,01 -0,03 0,02 0,00 -0,05 0,00 WATER SOLVENT DRUM UJD22_W30 -0,17 -0,15 -0,18 -0,01 1,00 0,59 0,19 0,02 -0,23 0,09 -0,05 -0,23 -0,49 WATER IN FEED DRUM UJD24_W30 -0,06 -0,14 -0,25 0,02 0,59 1,00 0,27 0,11 -0,17 0,21 -0,04 -0,14 -0,39 CO ACETATE UJD24_432 -0,01 -0,12 -0,20 0,01 0,19 0,27 1,00 0,61 0,41 0,33 0,04 -0,05 -0,01 MN ACETATE UJD24_517 0,01 0,02 -0,16 -0,01 0,02 0,11 0,61 1,00 0,43 0,23 0,02 0,07 0,13 BR TOTAL UJD24_GI4 0,29 0,23 0,17 -0,03 -0,23 -0,17 0,41 0,43 1,00 0,36 0,10 0,26 0,43 SODIUM UJD24_314 -0,04 0,02 -0,07 0,02 0,09 0,21 0,33 0,23 0,36 1,00 0,05 -0,04 0,05 NBA IN FEED MIX UJD24_B50 0,05 0,03 0,06 0,00 -0,05 -0,04 0,04 0,02 0,10 0,05 1,00 0,09 0,02 CATALYST IN FEED PREPARATION DRUM3FC113_PV 0,76 0,15 0,34 -0,05 -0,23 -0,14 -0,05 0,07 0,26 -0,04 0,09 1,00 0,24 METHYL ACETATE IN SOLVENT DRUMTO FEED MIXUID22_B49 0,27 0,36 0,12 0,00 -0,49 -0,39 -0,01 0,13 0,43 0,05 0,02 0,24 1,00 TOTAL AIR TO REACTOR A 3FC301A_PV 0,94 0,27 0,43 -0,05 -0,25 -0,15 -0,06 0,01 0,29 -0,12 0,07 0,71 0,35 PARTIAL AIR TO REACTOR A 3FC304A_PV 0,94 0,21 0,42 -0,06 -0,21 -0,11 -0,05 0,00 0,23 -0,14 0,06 0,71 0,27 PARTIAL AIR TO REACTOR A 3FC305A_PV 0,94 0,21 0,42 -0,05 -0,20 -0,11 -0,05 0,00 0,23 -0,14 0,06 0,71 0,26 TOTAL FEED TO REACTOR A 3FC302A_PV 0,90 0,21 0,44 -0,05 -0,25 -0,17 -0,04 0,01 0,28 -0,12 0,10 0,69 0,31 PARTIAL FEED TO REACTOR A 3FC302A1_PV 0,71 0,13 0,53 -0,06 -0,13 -0,13 -0,15 -0,06 0,12 -0,24 0,04 0,54 0,09 PARTIAL FEED TO REACTOR A 3FC302A2_PV 0,67 0,45 0,30 -0,04 -0,23 -0,14 -0,25 0,00 0,27 -0,12 0,10 0,54 0,37 CURRENT AGITATOR A 3IIA301AR_PV 0,64 0,37 0,35 -0,03 -0,46 -0,38 -0,07 0,04 0,46 0,01 0,12 0,48 0,65 LEVEL REACTOR A 3LC301A_PV 0,54 0,09 0,45 0,00 -0,12 -0,13 -0,20 -0,19 -0,07 -0,32 0,00 0,35 0,09 RXA PRESSURE AFTER 3RD 3PC311A_PV 0,75 0,14 0,18 -0,06 -0,16 -0,02 -0,03 0,03 0,11 -0,22 0,05 0,53 0,14 RXA PRESSURE OVERHEAD 3PI310A3_PV 0,77 0,15 0,20 -0,06 -0,17 -0,03 -0,03 0,03 0,12 -0,22 0,05 0,55 0,15 TEMP. VAPOUR REACTOR 3TI306A_PV 0,80 0,15 0,20 -0,06 -0,14 0,00 -0,01 0,03 0,11 -0,20 0,04 0,56 0,14 TEMP. TOP REACTOR 3TI305A_PV 0,74 0,15 0,15 -0,06 -0,16 -0,02 -0,01 0,04 0,13 -0,20 0,05 0,52 0,15 TEMP. MEDIUMREACTOR 3TI304A_PV 0,74 0,15 0,15 -0,06 -0,16 -0,02 -0,01 0,04 0,13 -0,20 0,05 0,52 0,15 TEMP. BOTTOMREACTOR 3TI302A_PV 0,74 0,15 0,15 -0,06 -0,17 -0,02 -0,01 0,04 0,13 -0,20 0,05 0,52 0,16 TEMP. CONTROL REACTOR 3TC301A_PV 0,75 0,15 0,18 -0,06 -0,19 -0,05 -0,03 0,03 0,13 -0,21 0,05 0,53 0,16 O2 REACTOR A 3AC301A_PV -0,31 -0,12 0,24 -0,01 0,12 -0,09 -0,23 -0,26 -0,23 -0,09 -0,03 -0,24 -0,18 CO REACTOR A 3AC304A_PV 0,35 0,14 -0,06 -0,04 -0,24 -0,09 0,03 0,12 0,18 -0,17 0,04 0,23 0,23 CO2 REACTOR A 3AI303A_PV 0,44 0,12 0,06 -0,06 -0,20 -0,07 0,06 0,10 0,19 -0,12 0,06 0,29 0,20 WWD REACTOR A TO 3T701 3FC306A_PV 0,41 0,18 0,14 -0,03 -0,11 -0,07 0,01 0,02 0,19 0,07 -0,09 0,20 0,28 WWD REACTOR A TO FEED PREPARATION3FC307A_PV -0,02 -0,01 -0,02 0,00 0,00 -0,02 0,02 0,08 0,00 -0,01 0,01 0,01 0,02 Feed Preparation Reactor A Feed Preparation Pears on correlation Matrix BigML METHODOLOGY APPLIED
  19. 19. #MLSEV BigML METHODOLOGY APPLIED
  20. 20. #MLSEV BigML METHODOLOGY APPLIED
  21. 21. #MLSEV BigML METHODOLOGY APPLIED
  22. 22. #MLSEV BigML METHODOLOGY APPLIED
  23. 23. #MLSEV Examples of results-Model evaluation
  24. 24. #MLSEV = !"#"$%&'& &()% = !"#" !!%* = !"#" !!*# Examples of results-Model evaluation
  25. 25. #MLSEV Decision Tree PDP Sundburst 90%-99% 73%-80% 80%-83% Examples of results
  26. 26. #MLSEV Example of sunburst
  27. 27. #MLSEV Plan for PDP analysis Variable 1 Level 1 Level 2 Level 3
  28. 28. #MLSEV Plan for PDP analysis Variable 1 Level 1 Level 2 Level 3 Variable 2 Variable 2 Variable 2 Level 1 Level 2 Level 8
  29. 29. #MLSEV Plan for PDP analysis Variable 1 Level 1 Level 2 Level 3 Variable 2 Variable 2 Variable 2 Level 1 Level 2 Level 8 Variable 3 Variable 4
  30. 30. #MLSEV Plan for PDP analysis Variable 1 Level 1 Level 2 Level 3 Variable 2 Variable 2 Variable 2 Level 1 Level 2 Level 8 Variable 3 Variable 4 Fixed Variable 5 Variable 6 Variable 7 Variable 8 Variable 9 . . . Variable n
  31. 31. #MLSEV Variable 2-level 1Variable 2-level 2Variable 2-level 3Variable 2-level 4 Variable 2-level 5Variable 2-level 6Variable 2-level 7Variable 2 –level 8 90%-99% 80%-83% 92%-99% 80%-83% 73%-80% 85%-92% 72% 64%-74% 87%-95% 60%-70% 65%-77% 90%-91% 90 %-99 % 73 %-80 % 80 %-83 % 73%-80% 75%-78% 60%-70% Variable 3 Variable4 Example of PDP Variable 1-level 1 Variable 3 Variable4 Variable 3
  32. 32. #MLSEV 85%-98% 79% 94%-98% 79%-80% 90% 80% 77% 92%-97% 84%-86% 92%-97% 88%-93% 80% 74%-78% 60%-70% 57%-60% 66%-70% 78%-87% 65%-74% 76%-88% Variable 2-level 1Variable 2-level 2Variable 2-level 3Variable 2-level 4 Variable 2-level 5Variable 2-level 6Variable 2-level 7Variable 2 –level 8 Variabe 3 Variable4 Example of PDP Variable 1-level 2 Variable 3 Variable 3
  33. 33. #MLSEV 68%-85% 70%-74% 68%-82% 73%-77% 77%-82% 85%-89% 76%-95% 91%-95% 60%-80% 90%-95% 80%-90% 50%-66% 71%-83% 60%-74% Variable 2-level 1Variable 2-level 2Variable 2-level 3Variable 2-level 4 Variable 2-level 5Variable 2-level 6Variable 2-level 7Variable 2 –level 8 Variable 3 Variable4 Variable 1-level 3 Example of PDP
  34. 34. #MLSEV Summary SUMMARY • Model obtained provides statistically very good results and compared with real effects seen in process. It seems to be adequate to predict probability of succes in quality and efficiency of reaction. • This first exercise made in Indorama Ventures Química opens the door for further investigation by adding more datasets, exploring new ranges of operation for variables and letting the application to learn and recommend even higher levels of optimization.

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