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Recurrent Neural Network-Based Model Predictive Control for Continuous Pharmaceutical Manufacturing

Author

Listed:
  • Wee Chin Wong

    (Department of Chemical & Biomolecular Engineering, Faculty of Engineering, National University of Singapore, 4 Engineering Drive 4, Singapore 117585, Singapore)

  • Ewan Chee

    (Department of Chemical & Biomolecular Engineering, Faculty of Engineering, National University of Singapore, 4 Engineering Drive 4, Singapore 117585, Singapore)

  • Jiali Li

    (Department of Chemical & Biomolecular Engineering, Faculty of Engineering, National University of Singapore, 4 Engineering Drive 4, Singapore 117585, Singapore)

  • Xiaonan Wang

    (Department of Chemical & Biomolecular Engineering, Faculty of Engineering, National University of Singapore, 4 Engineering Drive 4, Singapore 117585, Singapore)

Abstract

The pharmaceutical industry has witnessed exponential growth in transforming operations towards continuous manufacturing to increase profitability, reduce waste and extend product ranges. Model predictive control (MPC) can be applied to enable this vision by providing superior regulation of critical quality attributes (CQAs). For MPC, obtaining a workable system model is of fundamental importance, especially if complex process dynamics and reaction kinetics are present. Whilst physics-based models are desirable, obtaining models that are effective and fit-for-purpose may not always be practical, and industries have often relied on data-driven approaches for system identification instead. In this work, we demonstrate the applicability of recurrent neural networks (RNNs) in MPC applications in continuous pharmaceutical manufacturing. RNNs were shown to be especially well-suited for modelling dynamical systems due to their mathematical structure, and their use in system identification has enabled satisfactory closed-loop performance for MPC of a complex reaction in a single continuous-stirred tank reactor (CSTR) for pharmaceutical manufacturing.

Suggested Citation

  • Wee Chin Wong & Ewan Chee & Jiali Li & Xiaonan Wang, 2018. "Recurrent Neural Network-Based Model Predictive Control for Continuous Pharmaceutical Manufacturing," Mathematics, MDPI, vol. 6(11), pages 1-20, November.
    • Handle: RePEc:gam:jmathe:v:6:y:2018:i:11:p:242-:d:181249
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    Citations

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    Cited by:

    1. Hui Wang & Ashutosh Sharma & Mohammad Shabaz, 2022. "Research on digital media animation control technology based on recurrent neural network using speech technology," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(1), pages 564-575, March.
    2. Aleksey I. Shinkevich & Irina G. Ershova & Farida F. Galimulina, 2022. "Forecasting the Efficiency of Innovative Industrial Systems Based on Neural Networks," Mathematics, MDPI, vol. 11(1), pages 1-25, December.

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