Advertisement

Journal of Pharmaceutical Innovation

, Volume 9, Issue 1, pp 65–81 | Cite as

Simulation-Based Design of an Efficient Control System for the Continuous Purification and Processing of Active Pharmaceutical Ingredients

  • Maitraye Sen
  • Ravendra Singh
  • Rohit RamachandranEmail author
Research Article

Abstract

In this study, an efficient system-wide controlsystem has been designed for the integrated continuous purification and processing of the active pharmaceutical ingredient (API). The control strategy is based on the regulatory PID controller which is most widely used in the manufacturing industry because of its simplicity and robustness. The designed control system consists of single and cascade (nested) control loops. The control system has been simulated in gPROMS TM (Process System Enterprise). The ability of the control system to track the specified set point changes as well as to reject disturbances has been evaluated. Results demonstrate that the model shows an enhanced performance in the presence of random disturbances under closed-loop control compared to an open-loop operation. The control system is also able to track the set point changes effectively. This proves that closed-loop feedback control can be used in improving pharmaceutical manufacturing operations based on the Quality by Design (QbD) paradigm.

Keywords

Process control Continuous processing Flowsheet simulation Powder mixing Pharmaceutical manufacturing Crystallization 

Notes

Acknowledgments

This work is supported by the National Science Foundation Engineering Research Center on Structured Organic Particulate Systems, through Grant NSF-ECC 0540855.

References

  1. Plumb K. Continuous processing in the pharmaceutical industry: changing the mindset. Chem Eng Res Des. 2005;83:730–738.CrossRefGoogle Scholar
  2. Reklaitis GV, Khinast J, Muzzio FJ. Pharmaceutical engineering science—new approaches to pharmaceutical development and manufacturing. Chem Eng Sci. 2010;65:4–7.CrossRefGoogle Scholar
  3. Food and Drug Administration. Guidance for industry. PAT-A framework for innovative pharmaceutical development, manufacturing and quality assurance, Food and Drug Administration September 2004.Google Scholar
  4. Food and Drug Administration. Guidance for industry. Q8 pharmaceutical development, Food and Drug Administration May 2006.Google Scholar
  5. Charoo NA, Shamsher AAA, Zidan AS, Rahman Z. Quality by design approach for formulation development: a case study of dispersible tablets. Int J Pharm. 2012;423:167–178.PubMedCrossRefGoogle Scholar
  6. Singh R, Ierapetritou M, Ramachandran R. An engineering study on the enhanced control and operation of continuous manufacturing of pharmaceutical tablets via roller compaction. Int J Pharm. 2012;438:307–326.PubMedCrossRefGoogle Scholar
  7. Boukouvala F, Niotis V, Ramachandran R, Muzzio FM, Ierapetritou G. An integrated approach for dynamic flowsheet modeling and sensitivity analysis of a continuous tablet manufacturing process: an integrated approach. Comput Chem Eng. 2012;42:30–47.CrossRefGoogle Scholar
  8. Boukouvala F, Chaudhury A, Sen M, Zhou R, Mioduszewski L, Ierapetritou MG, Ramachandran R. Computer-aided flowsheet simulation of a pharmaceutical tablet manufacturing process incorporating wet granulation. J Pharm Innov. 2013;8:11–27.CrossRefGoogle Scholar
  9. Sen M, Rogers A, Singh R, Chaudhury A, John J, Ierapetritou MG, Ramachandran R. Flowsheet optimization of an integrated continuous purification-processing pharmaceutical manufacturing operation. Chem Eng Sci. 2013;102:56–66.CrossRefGoogle Scholar
  10. Sen M, Chaudhury A, Singh R, John J, Ramachandran R. Multi-scale flowsheet simulation of an integrated continuous purification-downstream pharmaceutical manufacturing process. Int J Pharm. 2013;445:29–38.PubMedCrossRefGoogle Scholar
  11. Gnoth S, Jenzsch M, Simutis R, Luubert A. Process analytical technology (PAT): batch-to-batch reproducibility of fermentation processes by robust process operational design and control. J Biotechnol. 2007;132:180–186.PubMedCrossRefGoogle Scholar
  12. Cervera-Padrell AE, Skovby T, Kiil S, Gani R, Gernaey KV. Active pharmaceutical ingredient (API) production involving continuous processes a process system engineering (PSE)-assisted design framework. Eur J Pharm Biopharm. 2012;82:437–456.PubMedCrossRefGoogle Scholar
  13. Benyahia B, Lakerveld R, Barton PI. A plant-wide dynamic model of a continuous pharmaceutical process. Ind Eng Chem Res. 2012;51:15393–15412.CrossRefGoogle Scholar
  14. Lakerveld R, Benyahia B, Braatz RD, Barton PI. Model-based design of a plant-wide control strategy for a continuous pharmaceutical plant. AIChE J. 2013;59:3671–3685.CrossRefGoogle Scholar
  15. Fujiwara M, Nagy ZK, Chew JW, Braatz RD. First-principles and direct design approaches for the control of pharmaceutical crystallization. J Process Control. 2005;15:493–504.CrossRefGoogle Scholar
  16. Ma CY, Wang XZ. Closed-loop control of crystal shape in cooling crystallization of L-glutamic acid. J Process Control. 2012;22:72–81.CrossRefGoogle Scholar
  17. Kleinert T, Weickgennant M, Judat B, Hagenmeyer V. Cascaded two-degree-of-freedom control of seeded batch crystallisations based on explicit system inversion. J Process Control. 2010;20:29–44.CrossRefGoogle Scholar
  18. Drews A, Arellano-Garcia H, Schoneberger J, Schaller J, Kraume M, Wozny G. Improving the efficiency of membrane bioreactors by a novel model-based control of membrane filtration. Comput Aided Chem Eng. 2007;24:773–776.Google Scholar
  19. Zaror CA, Perez-Correa JR. Model based control of centrifugal atomizer spray drying. Food Control. 1991;2:170–175.CrossRefGoogle Scholar
  20. Daraoui N, Dufour P, Hammouri H, Hottot A. Model predictive control during the primary drying stage of lyophilisation. Control Eng Pract. 2010;18:483–494.CrossRefGoogle Scholar
  21. Singh R, Gernaey KV, Gani R. ICAS-PAT: a software for design, analysis and validation of PAT systems. Comput Chem Eng. 2010;34:1108–1136.CrossRefGoogle Scholar
  22. Hsu S, Reklaitis GV, Venkatasubramanian V. Modeling and control of roller compaction for pharmaceutical manufacturing. Part I: process dynamics and control framework. J Pharm Innov. 2010;5:14–23.CrossRefGoogle Scholar
  23. Hsu S, Reklaitis GV, Venkatasubramanian V. Modeling and control of roller compaction for pharmaceutical manufacturing. Part II: control and system design. J Pharm Innov. 2010;5:24–36.CrossRefGoogle Scholar
  24. Ramachandran R, Chaudhury A. Model-based design and control of continuous drum granulation processes. Chem Eng Res Des. 2011;90:1063–1073.CrossRefGoogle Scholar
  25. Burggraeve A, Monteyne T, Vervaet C, Remon JP, Beer TD. Process analytical tools for monitoring, understanding, and control of pharmaceutical fluidized bed granulation: a review. Eur J Pharm Biopharm. 2013;83:2–15.PubMedCrossRefGoogle Scholar
  26. Kleinert T, Weickgennant M, Judat B, Hagenmeyer V. On control of particle size distribution in granulation using high shear mixers. J Process Control. 2010;20:29–44.CrossRefGoogle Scholar
  27. Sanders CFW, Hounslow MJ, III FJD. Identification of models for control of wet granulation. Powder Technol. 2009;188:255–263.Google Scholar
  28. Gatzke EP, III FJD. Model predictive control of a granulation system using soft output constraints and prioritized control objectives. Powder Technol. 2001;121:149–158.Google Scholar
  29. Long CE, Polisetty PK, Gatzke EP. Deterministic global optimization for non-linear model predictive control of hybrid dynamic systems. Int J Robust Nonlinear Control. 2007;17:1232–1250.CrossRefGoogle Scholar
  30. Pottmann M, Ogunnaike BA, Adetayo AA, Ennis BJ. Model-based control of a granulation process. Powder Technol. 2000;108:192–201.CrossRefGoogle Scholar
  31. Ramachandran R, Arjunan J, Chaudhury A, Ierapetritou MG. Model-based control loop performance assessment of a continuous direct compaction pharmaceutical processes. J Pharm Innov. 2012;6:249–263.CrossRefGoogle Scholar
  32. Singh R, Ierapetritou M, Ramachandran R. System-wide hybrid MPC-PID control of a continuous pharmaceutical tablet manufacturing process via direct compaction. Eur J Pharm Biopharm. 2013;85:1164–1182.PubMedCrossRefGoogle Scholar
  33. Singh R, Sahay A, Oka S, Liu X, Ramachandran R, Ierapetritou M, Muzzio F. Online monitoring, advanced control and operation of robust continuous pharmaceutical tablet manufacturing process. BioPharma Mag Asia. 2013;2:18–23.Google Scholar
  34. Sen M, Singh R, Vanarase A, John J, Ramachandran R. Multi-dimensional population balance modeling and experimental validation of continuous powder mixing processes. Chem Eng Sci. 2012;80:349–360.CrossRefGoogle Scholar
  35. Robles A, Ruano MV, Ribes J, Ferrer J. Advanced control system for optimal filtration in submerged anaerobic mbrs (SAnMBRs). J Membr Sci. 2013;430:330–340.CrossRefGoogle Scholar
  36. Peiris RH, Budman H, Moresoli C, Legge RL. Fouling control and optimization of a drinking water membrane filtration process with real-time model parameter adaptation using fluorescence and permeate flux measurements. J Process Control. 2013;23:70–77.CrossRefGoogle Scholar
  37. Singh R, Gernaey KV, Gani R. An ontological knowledge-based system for the selection of process monitoring and analysis tools. Comput Chem Eng. 2010;34:1137–1154.CrossRefGoogle Scholar
  38. gPROMS model builder, gPROMS 3.4.0 documentation, Process system enterprise (PSE) http://www.psenterprise.com/gproms.html.
  39. Stephanopoulos G. Chemical process control. USA: Prentice-Hall,Inc.; 2006.Google Scholar
  40. Blevins T, Wojsznis WK, Nixon M. Advanced control foundation: tools, techniques and applications. USA: International Society of Automation; 2013.Google Scholar
  41. Vanarase AU, Alaca M, Rozo J, Muzzio FJ, Romonach RJ. Real time monitoring of drug concentration in a continuous powder mixing process using NIR spectroscopy. Chem Eng Sci. 2010;65:5728–5733.CrossRefGoogle Scholar
  42. Miki H, Terashima T, Asakuma Y, Maeda K, Fukui K. Inclusion of mother liquor inside KDP crystals in a continuous MSMPR crystallizer. Sep Purif Technol. 2005;43:71– 76.CrossRefGoogle Scholar
  43. Gunawan R, Fusman I, Braatz RD. High resolution algorithms for multidimensional population balance equations. AIChE J. 2004;50:2738–2749.CrossRefGoogle Scholar
  44. Mccabe WL, Smith JC, Harriott P. Unit operations of chemical engineering. NY: McGraw-Hill; 2001.Google Scholar
  45. Mezhericher A, Levy A, Borde I. Modelling of particle breakage during drying. Chem Eng Process. 2008; 47:1404–1411.CrossRefGoogle Scholar
  46. Sen M, Ramachandran R. A multi-dimensional population balance model approach to continuous powder mixing processes. Adv Powder Technol. 2013;24:51–59.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Maitraye Sen
    • 1
  • Ravendra Singh
    • 1
  • Rohit Ramachandran
    • 1
    Email author
  1. 1.Department of Chemical and Biochemical Engineering, RutgersThe State University of New JerseyPiscatawayUSA

Personalised recommendations