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Hard and soft modeling. A case study

Hard and soft modeling. A case study . Alexey Pomerantsev Institute of Chemical Physics, Moscow. Outlines. Background Soft modeling Hard modeling Trade-off between hard and soft . Part 1. Background. Antioxidants.

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Hard and soft modeling. A case study

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  1. Hard and soft modeling.A case study Alexey PomerantsevInstitute of Chemical Physics, Moscow WSC-5

  2. Outlines • Background • Soft modeling • Hard modeling • Trade-off between hard and soft WSC-5

  3. Part 1. Background WSC-5

  4. Antioxidants Antioxidant is a special additive which inhibits polymers thermo-aging, protecting polymers from oxidation during processing as well as at the end-use application. The problem is to verify the quality (activity, effectiveness) of any new prospective chemical. WSC-5

  5. Oxidation Induction Period - OIP Conventional method is a Long Term Heat Aging (LTHA) T = 140°C t = 1- 90 days WSC-5

  6. Oxidation Initial Temperature - OIT Alternative method is Differential Scanning Calorimetry (DSC) WSC-5

  7. Theory Chemometrics Application Chemometrics 1$ Two Goals Soft/hard approaches trade-off Fast method for the antioxidants testing WSC-5

  8. 3 AO Concentrations 25 AO Samples 0.10 0.07 0.05 Polypropylene (PP) Sample Preparation WSC-5

  9. DSC Experiments. Five Heating Rates WSC-5

  10. Data WSC-5

  11. Part 2. Soft Modeling WSC-5

  12. Data Interpretation in Soft Modeling WSC-5

  13. y1 y2 y3 PLS1 Regressions: Xai =yi 3 models for each of initial AO concentration X WSC-5

  14. PLS1 Regression. Three Data Sets WSC-5

  15. Prediction by PLS. Initial AO of 0.05 WSC-5

  16. RPV in parameter space Prediction intervals: SIC & PLS RPV SIC Principles All errors are limited! There exists Maximum Error Deviation, b, such that for any error e Prob{| e | > b}= 0 WSC-5

  17. Test Samples AO19-AO25 Calibration Samples AO1-AO18 SIC Prediction Intervals (PI). A0=0.05 WSC-5

  18. Part 3. Hard Modeling WSC-5

  19. 25 models for each of AOs Data Interpretation in Hard Modeling WSC-5

  20. Step 2 Step 1 How OIP (t) depends on initial AO concentration (A0)t=t(A0; ) and the same parameter set  How OIT (T) depends on heating rate (v), initial AO concentration (A0)T=T(v, A0; )and parameter set  Two Steps of Hard Modeling WSC-5

  21. AO consumption AO critical value OIT model Step 1. Model Building WSC-5

  22. Fitter Calculations. Step 1 WSC-5

  23. AO consumption AO critical value OIP model OIP confidence bounds Step 2. Model Building WSC-5

  24. Fitter Calculations. Step 2 WSC-5

  25. Part 4. Trade-off between hard and soft WSC-5

  26. OIP Prediction with Hard & Soft Methods A0=0.05 WSC-5

  27. Cor (ysoft, yhard) Cor (usoft, uhard) Hard & Soft Statistics WSC-5

  28. CI > PI CI < PI PLS Score Plot. A0=0.05 WSC-5

  29. Arrhenius Law WSC-5

  30. CI > PI CI < PI ^ ln(kc) Correlation Between the Estimates. A0=0.05 WSC-5

  31. Forecast to the Different Conditions A0=0.04 & T=80ºC ÷ 200ºC WSC-5

  32. SIC Object Status Plot (OSP) ? WSC-5

  33. Pros and Cons WSC-5

  34. Conclusions A long LTHA process (conventional approach) can be replaced with a fast DSC technique (novel approach) with further data calibration by the hard (NLR), or by the soft (SIC/PLS) methods. Both calibration methods have a similar quality of prediction. However, each technique has its own advantages and disadvantages. WSC-5

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