Affiliation: | 1. Department of Chemical and Biomolecular Engineering, National University of Singapore, Singapore, Singapore
Contribution: Conceptualization (equal), Data curation (equal), Formal analysis (equal), Methodology (equal), Software (equal), Writing - original draft (equal);2. Department of Chemical and Biomolecular Engineering, National University of Singapore, Singapore, Singapore
Joint School of National University of Singapore and Tianjin University, International Campus of Tianjin University, Binhai New City, Fuzhou, China
Contribution: Formal analysis (supporting), Methodology (equal), Software (supporting), Writing - original draft (supporting);3. Department of Chemical and Biomolecular Engineering, National University of Singapore, Singapore, Singapore |
Abstract: | This work considers a seeded fesoterodine fumarate (FF) cooling crystallization and presents the methodology and implementation of a real-time machine learning modeling-based predictive controller to handle batch-to-batch (B2B) parametric drift. Specifically, an autoencoder recurrent neural network-based model predictive controller (AERNN-MPC) is developed to optimize product yield, crystal size, and energy consumption while accounting for the physical constraints on cooling jacket temperature. Deviations in the kinetic parameters are considered in the closed-loop simulations to account for the B2B parametric drift, and two error-triggered online update mechanisms are proposed to address issues pertaining to the availability of real-time crystal property measurements and are incorporated into the AERNN-MPC to improve the model prediction accuracy. Closed-loop simulation results demonstrate that the proposed AERNN-MPC with online update, irrespective of the accessibility to real-time crystal property data, achieves a desired closed-loop performance in terms of maximizing product yield and minimizing energy consumption. |