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

Department of Chemical & Biomolecular Engineering, Faculty of Engineering, National University of Singapore, 4 Engineering Drive 4, Singapore 117585, Singapore
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Mathematics 2018, 6(11), 242; https://doi.org/10.3390/math6110242
Received: 5 August 2018 / Revised: 21 October 2018 / Accepted: 26 October 2018 / Published: 7 November 2018
(This article belongs to the Special Issue New Directions on Model Predictive Control)
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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. View Full-Text
Keywords: pharmaceuticals; critical quality attributes (CQAs); recurrent neural networks; model predictive control (MPC); system identification pharmaceuticals; critical quality attributes (CQAs); recurrent neural networks; model predictive control (MPC); system identification
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Wong, W.C.; Chee, E.; Li, J.; Wang, X. Recurrent Neural Network-Based Model Predictive Control for Continuous Pharmaceutical Manufacturing. Mathematics 2018, 6, 242.

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