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

TL;DR

This paper demonstrates that Recurrent Neural Networks can effectively model complex dynamics for Model Predictive Control in continuous pharmaceutical manufacturing, enabling improved process regulation and control.

Contribution

It introduces the application of RNNs for MPC in continuous pharmaceutical manufacturing, highlighting their suitability for modeling complex process dynamics.

Findings

RNNs provide accurate dynamic models for pharmaceutical processes.

RNN-based MPC achieves satisfactory closed-loop control performance.

The approach supports industry shift towards data-driven process control.

Abstract

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.