Machine Learning Modeling and Predictive Control of the Batch Crystallization Process

Abstract

This work develops a framework for building machine learning models and machine-learning-based predictive control schemes for batch crystallization processes. We consider a seeded fesoterodine fumarate cooling crystallization and dissolution process in a batch reactor and present the methodology and implementation of simulation, modeling, and controller design. Specifically, to address the experimental data scarcity problem, we first develop a one-dimensional population balance model based on published kinetic parameters that were obtained empirically to describe the formation of crystals via nucleation, growth, and agglomeration. Then, recurrent neural network (RNN) and autoencoder–RNN (AERNN) models are developed using data from extensive open-loop simulations of the semi-empirical population balance model under various operating conditions to capture the process dynamic behavior. Two model predictive control (MPC) schemes using the respective RNN and AERNN models are developed to optimize the crystallization process with respect to product yield, crystal size, number of fines in the final product, and energy consumption, while accounting for the constraints on manipulated inputs. Through open- and closed-loop simulations, it is demonstrated that the RNN and AERNN models capture the process dynamics well, and the RNN- and AERNN-based MPCs achieved the desired product yield and crystal size with significantly improved computational efficiency.