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.
Highlights
The pharmaceutical industry has shown a growing interest in adopting the concept of continuous manufacturing [1,2] to reduce waste, cost, environmental footprints and lead-times
The methods for generating the control-oriented recurrent neural networks (RNNs) and integrating their use into Model predictive control (MPC) controllers were articulated, and the performance of the RNN-based MPC (RNN-MPC) controllers was benchmarked against non-linear MPC (NMPC) controllers that used the true plant model directly as its internal model
Two control scenarios were simulated, one involving reactor start-up and the other involving upset-recovery, and the results showed that even if the RNNs do not perfectly describe the underlying plant dynamics, RNN-MPC controllers can give stable closed-loop control performance for these scenarios as long as the global dynamics of the system are captured
Summary
The pharmaceutical industry has shown a growing interest in adopting the concept of continuous manufacturing [1,2] to reduce waste, cost, environmental footprints and lead-times. Coupled with developments in continuous manufacturing technologies and quality by design (QbD) paradigms, there has recently been an increasing demand for more advanced system identification and process control strategies for continuous pharmaceutical manufacturing [1]. Continuous manufacturing has the potential of extending the palette of permissible reaction conditions and enabling reaction outcomes that are quite challenging when performed under batch conditions [3,4,5], and its application to the production of complex Active Pharmaceutical Ingredients (APIs) is an emerging research domain. The complexity of the system, as well as its potential impact on reaction yields, downstream processing requirements and critical quality attributes (CQAs) of final products, render model identification and control of continuous API reactions challenging
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