(575f) Machine Learning-Based Modeling and Predictive Control of Crystallization Processes Under Batch-to-Batch Parametric Drift
AIChE Annual Meeting
2022
2022 Annual Meeting
Topical Conference: Next-Gen Manufacturing
Next-Gen Manufacturing in Pharma, Food, and Bioprocessing II
Wednesday, November 16, 2022 - 5:15pm to 5:36pm
Motivated by the above considerations, this work proposes a general framework for constructing computationally efficient machine learning models as substitutions for the mechanistic models in MPC and developing machine learning (ML)-based MPC schemes equipped with error-triggered online model update mechanisms for implementation to batch crystallization processes under the influence of B2B parameter drift. Specifically, we consider a seeded fesoterodine fumarate cooling crystallization and dissolution in a batch crystallizer ([6]) and present the methodology for designing a ML-based MPC controller to optimize product yield, crystal size, and energy consumption while accounting for the physical constraints on cooling jacket temperature. The ML model is constructed via the integration of recurrent neural network (RNN) with autoencoder (AE), where the RNN is developed to model the process dynamics and the AE is incorporated as a dimensionality reduction technique to further enhance the computational efficiency [4, 5]. Deviations in the crystallization kinetic parameters are considered in the closed-loop simulations to account for B2B parametric drift, and error-triggered online update mechanisms are incorporated into the MPC to improve its control performance by taking the availability of real-time system measurements into account. The results demonstrate that the proposed AERNN-MPC with online update, irrespective of the accessibility to real-time measurement data, achieves a desired closed-loop performance as compared with the MPC without online update.
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