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The pharmaceutical industry has witnessed exponential growth in transformingoperations towards continuous manufacturing to effectively achieve increasedprofitability, reduced waste, and extended product range. Model PredictiveControl (MPC) can be applied for enabling this vision, in providing superiorregulation of critical quality attributes. For MPC, obtaining a workable modelis of fundamental importance, especially in the presence of complex reactionkinetics and process dynamics. Whilst physics-based models are desirable, it isnot always practical to obtain one effective and fit-for-purpose model.Instead, within industry, data-driven system-identification approaches havebeen found to be useful and widely deployed in MPC solutions. In this work, wedemonstrated the applicability of Recurrent Neural Networks (RNNs) for MPCapplications in continuous pharmaceutical manufacturing. We have shown thatRNNs are especially well-suited for modeling dynamical systems due to theirmathematical structure and satisfactory closed-loop control performance can beyielded for MPC in continuous pharmaceutical manufacturing.

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