A tutorial overview of model predictive control for continuous crystallization: current possibilities and future perspectives

TL;DR

This paper reviews advanced model predictive control techniques for continuous crystallization, emphasizing surrogate models for real-time optimization and demonstrating their effectiveness through case studies.

Contribution

It introduces a systematic methodology integrating population balance models with data-driven surrogates for real-time control of crystallization processes.

Findings

Surrogate models enable efficient real-time control of complex crystallization models.

The approach maintains high accuracy in controlling particle size distributions.

Case studies validate the effectiveness of the proposed control strategy.

Abstract

This paper presents a systematic approach to the advanced control of continuous crystallization processes using model predictive control. We provide a tutorial introduction to controlling complex particle size distributions by integrating population balance equations with detailed models of various continuous crystallizers. Since these high-fidelity models are often too complex for online optimization, we propose the use of data-driven surrogate models that enable efficient optimization-based control. Through two case studies, one with a low-complexity system allowing direct comparison with traditional methods and another involving a spatially distributed crystallizer, we demonstrate how our approach enables real-time model predictive control while maintaining accuracy. The presented methodology facilitates the use of complex models in a model-based control framework, allowing precise control of key particle size distribution characteristics, such as the median particle size $d_{50}$ and the width $d_{90} - d_{10}$. This addresses a critical challenge in pharmaceutical and fine chemical manufacturing, where product quality depends on tight control of particle characteristics.