Monitoring of a polymer batch process using in-line optical spectroscopy Michel Huys

1. Monitoring of a polymer batch process using in-line optical spectroscopy Michel Huys DSM Research, Geleen, The Netherlands

2. Point of view from business The aim is to create value Less an academic exercise

3. Content • Introduction DSM • Description of the application • Quantification of end point • Batch-to-batch Consistency • Conclusions

4. (1) Introduction DSM 22.000 employees, 8 billion€ turnover, 200 sites worldwide Life Science (40 %) (penicillin, aspartame,antibiotics...) Performance Materials (30 %) (resins,coatings,EnginPlast,EPDM…) BaseChem & IndusPol (30 %) (melamine, caprolactame, PP, PE...) fig

5. (2) Description of the application Step-1: R2 R2 R1 R1 Diisocyanate -OH (1) R-1

6. Step-2: Polyol addition R-1 Polyol R-2 (2) Description of the application R2 R2 R2 R1 R1 R1

7. (3) Quantification of the endpoint • Lab experiments: NIRS, mid-IR and Raman • Plant experiments mid-IR and NIRS

8. (3) Quantification of the endpoint Lab experiments Fig

9. (3) Quantification of the endpoint Step 1 Step 2

10. (3) Quantification of the endpoint Results of Lab experiments: - NIRS: suitable for NCO and OH determination - Mid-IR:Suitable for NCO determination - Raman: Not suitable for both NCO and OH determination

11. (3) Quantification of the endpoint Plant experiments fig

12. Mid-IR NIR (3) Quantification of the endpoint fig

13. (3) Quantification of the endpoint Results: - NCO measurement with mid-IR, NIR mid-IR: single combined calibration model NIRS: two subsequent models for each grade - OH measurement with NIR NIRS: two subsequent models for each grade Conclusion: Implementation of NCO measurement with mid-IR

14. Implementation in Plant Cleaning of reactor, contamination Mid-IR

15. (4)Batch-to-batch Consistency Value creation with NIR Batch to batch consistency Comparing fingerprints of different batches In cooperation with the University of Amsterdam, prof. A. Smilde.

16. (4)Batch-to-batch Consistency Recorded NIR spectra for one batch run

17. (4)Batch-to-batch Consistency • Data processing • “Grey model”: multivariate statistical “black” box model with “white”prior information • White part of the model: - spectra of the pure components - non negative absorptions or concentrations

18. (4)Batch-to-batch Consistency OH NH CH NCO

19. (4)Batch-to-batch Consistency Modeling Model multiple batch runs = ST + X1 E1 C X2 E2 X3 E3

20. Batch process monitoring NCO OH Product

21. Batch process monitoring: prediction 17-04 NCO OH Product

22. Conclusion At DSM, in the same batch reactor a range of polymer grades are produced. Often, a polymer batch exists of 2 or more stages. To introduce successful optical spectroscopic methods for process control, methods should be robust with low cost of ownership. In order to quantify a process parameter a generic optical spectroscopic method is necessary in order to obtain a model with low maintenance costs. Mid-IR matches in our example these requirements. To monitor the batch to batch consistency, the optical spectroscopic method containing the most chemical information is chosen, in this case NIR. Building grey models could be a feasible method to obtain methods with low maintenance costs.

23. Conclusion NIR and mid-IR optical in-line spectroscopy have a different role and specific meaning for this application. They create value in a different manner for batch polymerization. mid-IR: automation of NCO profile and end point detection NIR: batch to batch consistency