Distillation Monitoring with NIR Spectroscopy From Laboratory Experiments to Manufacturing

Transcription

1 Distillation Monitoring with NIR Spectroscopy From Laboratory Experiments to Manufacturing Dr. Christian Lautz, F. Hoffmann-La Roche Ltd, Basel, Switzerland

2 Ammonia in methanol Process: After the conversion of an API with excess ammonia, the reaction solution is diluted with methanol and subsequently concentrated at 70 C / 500 mbar until residual ammonia is removed together with methanol Challenge: Can the concentration of ammonia in the distillate stream be determined quantitatively by NIR? Motivation: On-line monitoring of distillation dynamics, ammonia concentration and endpoint determination 2

3 Calibration design: Spectra Very intense signal with absorbance values above 1.5 au for ammonia concentrations over 7.0% (m/m) (inappropriate for quantitative model) Less intense signals in the remaining spectrum, but nonetheless meaningful spectral changes 3

4 Calibration design: Reference standards 24 binary mixtures of ammonia and methanol as calibration standards, prepared from a stock solution of 12.3% (m/m) ammonia in methanol (titration analysis) Gravimetric samples prepared on analytical balance Temperatures: 5 C and 12 C (per sample) No additional reference analytics (titrations) for the binary mixtures No external validation of the model with independent samples was possible before the actual use in production a critical approach, but the on-line measurement was not intended for GMP purposes in this project phase 4

5 PLS model: Cross validation result First derivative and vector normalization as spectral pretreatment R 2 = RMSECV = 0.04 Rank (PC): 3 5

6 Supply production: Flow cell in external distillate line 6

7 Supply production: Flow cell in external distillate line Temperature sensor The flow cell was positioned directly behind the condenser outlet 7

8 First batch: Overview Ammonia [ %(m/m) ] MD Data in the first hour are above the model limits NH 3 Cell temperature after initialisation phase: ~8 C Time [ h ] Mahalanobis Distance (MD) 8

9 First batch: Detail Mahalanobis Distance Limit: At sampling time, 543 kg condensate of 756 kg were distilled off (72%) Ammonia [ %(m/m) ] MD Mahalanobis Distance NH 3 Vacuum stopped for sampling Result: 0.1% (m/m) in vessel Time [ h ] 9

10 Results overview Batch Total condensate mass to be distilled in batch 756 kg 756 kg 756 kg 756 kg 756 kg 642 kg Distilled condensate at NIR = 0.05% (m/m) 543 kg 535 kg 520 kg 535 kg 575 kg 430 kg Percentage of total mass 72% 71% 69% 71% (76%) 67% Batch Total condensate mass to be distilled in batch 642 kg 639 kg 639 kg 555 kg 756 kg 563 kg Distilled condensate at NIR = 0.05% (m/m) 422 kg 416 kg 423 kg 354 kg 498 kg 363 kg Percentage of total mass 66% 65% 66% 64% 66% 64% 10

11 Conclusions from supply production Good accuracy achieved despite small set of calibration samples (no external validation, only two temperatures, no reference analytics) Good reproducibility of the NIR based distillation end point determination, with condensate masses in the range of 64-72% of the total mass Robust end point for the distillation is reached after 80% of the defined condensate mass have been distilled off Yet no basis for business case to control distillation by NIR, termination will be based on predefined total condensate mass to be distilled 11

12 Manufacturing: Flow cell in external distillate line 12

13 Implementation in manufacturing Only a position behind the distillate discharge system was available for the flow cell a considerable distance away from the previous position directly at the condenser outlet Thus, the temperature of the distillate stream was unknown, but expected to be higher A different temperature is a challenge for the validity of the PLS model On-line NIR measurement was introduced for familiarization with distillation at the beginning of the production; distillation end points were again determined based on the condensate mass 13

14 First batch: Overview Cell temperature after initialisation phase: ~35 C Ammonia [ %(m/m) ] MD NH Mahalanobis Distance (MD) Time [ h ]

15 First batch: Detail Ammonia [ %(m/m) ] NH 3 Analysis with the same PLS model as applied in supply production Mahalanobis Distance (MD) 0.3 MD Mahalanobis Distance Limit: Time [ h ] 15

16 Batches 1-4: Overview (PLS model) Ammonia [ %(m/m) ] Cavitation of water ring vacuum pump can clearly be seen Temperature and vacuum were adjusted in batches 3 and Batch 1 Batch 2 Batch 3 Batch Time [ h ] 16

17 Assessment PLS model developed for the temperature range from 5 12 C gives an accurate representation of the distillation dynamics Distillation end point determination based on the PLS model only reached a minimum value of 0.1% (m/m) ammonia, with the corresponding Mahalanobis distance at 0.35 (limit: 0.042) Adding calibration standards at higher temperatures to the PLS model was not possible in the short timeframe, therefore a quick alternative and preferably quantitative solution was required 17

18 PCA model with SpecAnalyzer from RPD Tool Based on the NIR spectra of plant batch 4, a new PCA model was introduced, using the software SpecAnalyzer from RPD Tool The new model covers the correct temperature in the flow cell, since it is based on an actual plant batch Reference ammonia concentrations are the maximum concentration of 8.5% (m/m) and the distillation end point with 0.0% (m/m) when the predefined total condensate mass is distilled off Thus, the PCA model determines the distillation end point when the total condensate mass is reached, but not a potential earlier distillation termination 18

19 Generation of PCA model: Scatterplot The user develops the model interactively; here all covariance is explained by the first PC thus a straight line is used The two reference concentrations can now be used to project the desired kinetic profile 19

20 Prediction with PCA model: Batch 5 Applying the model to the next batch gives a good representation of the kinetic profile, and the distillation end point of 0.0% ammonia is now reached with the Mahalanobis distance well below its limit 20

21 Batches 6-8: Overview (PCA model) Ammonia [ %(m/m) ] Batch 6 Batch 7 Batch 8 Consistent distillation process Time [ h ] 21

22 Batches 6-8: Detail (PCA model) Batch 6 Batch Ammonia [ %(m/m) ] Distillation end point in the PCA model is reached at the same time as the predefined mass of the condensate stream (1374 kg) Time [ h ] 22

23 Summary NIR spectroscopy is well suited to follow distillation dynamics and to determine ammonia concentration in the distillate stream on-line PLS and PCA models are both very valuable for batch-to-batch comparison and reproducibility Finally, on-line NIR measurements proved to be very helpful for familiarization with the distillation, but in this particular case distillation end points for a long-running production campaign can more easily be determined based on the condensate mass 23

24 Acknowledgement Many thanks for the support and helpful discussions to. PAT W. Deichtmann P. Maggi Synthesis H.-J. Mair R. Schätti Analytics M. Deichmann C. Pfisterer Supply Production R. Fischer F. Wierschem M. Guillemot-Plass M. Weber V. Göckel G. Schmidt Manufacturing A. Hidber M. Haffner G. Zufferey A. Mueller-Graf R. Homburger Engineering A. Kilcher C. Stirnimann Workshops Machine Shop Electricians 24

25 Doing now what patients need next 25