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Online Learning-Based Predictive Control of Crystallization Processes under Batch-to-Batch Parametric Drift
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  • Yingzhe Zheng,
  • Tianyi Zhao,
  • Xiaonan Wang,
  • Zhe Wu
Yingzhe Zheng
National University of Singapore

Corresponding Author:yzzheng@nus.edu.sg

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Tianyi Zhao
National University of Singapore
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Xiaonan Wang
National university of singapore
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Zhe Wu
National University of Singapore
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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.
30 Mar 2022Submitted to AIChE Journal
31 Mar 2022Submission Checks Completed
31 Mar 2022Assigned to Editor
02 Apr 2022Reviewer(s) Assigned
10 May 2022Editorial Decision: Revise Major
13 May 20221st Revision Received
16 May 2022Submission Checks Completed
16 May 2022Assigned to Editor
18 May 2022Reviewer(s) Assigned
15 Jun 2022Editorial Decision: Accept
Jul 2022Published in AIChE Journal. 10.1002/aic.17815