  The pharmaceutical industry has witnessed exponential growth in transforming
operations towards continuous manufacturing to effectively achieve increased
profitability, reduced waste, and extended product range. Model Predictive
Control (MPC) can be applied for enabling this vision, in providing superior
regulation of critical quality attributes. For MPC, obtaining a workable model
is of fundamental importance, especially in the presence of complex reaction
kinetics and process dynamics. Whilst physics-based models are desirable, it is
not always practical to obtain one effective and fit-for-purpose model.
Instead, within industry, data-driven system-identification approaches have
been found to be useful and widely deployed in MPC solutions. In this work, we
demonstrated the applicability of Recurrent Neural Networks (RNNs) for MPC
applications in continuous pharmaceutical manufacturing. We have shown that
RNNs are especially well-suited for modeling dynamical systems due to their
mathematical structure and satisfactory closed-loop control performance can be
yielded for MPC in continuous pharmaceutical manufacturing.
