Model predictive control

Control (management)

Process (computing)

Engineering

Biology

Identification (biology)

Control engineering

Operating system

Economics

Data mining

Computer science

Botany

Chemical engineering

Bioinformatics

Pharmaceutical industry

Profitability index

System identification

Process engineering

Pharmacology

Artificial intelligence

Continuous stirred-tank reactor

Measure (data warehouse)

Pharmaceutical manufacturing

Medicine

Finance

Artificial neural network

Recurrent neural network

Mathematics

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.

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