LAPSE:2023.0906v1
Published Article
LAPSE:2023.0906v1
Neural-Network-Based Nonlinear Model Predictive Control of Multiscale Crystallization Process
Liangyong Wang, Yaolong Zhu
February 21, 2023
The purpose of this study was to develop an integrated control strategy for multiscale crystallization processes. An image analysis method using a deep learning neural network is used to measure the fine-scale information of the crystallization process, and the mathematical statistical method is adopted to obtain the mean size of the crystal population. A feedforward neural network is subsequently trained and employed in a nonlinear model predictive control formulation to obtain the optimal profile of the manipulated variable. The effectiveness of the proposed nonlinear model predictive control method is evaluated using alum cooling crystallization experiments. Experimental results demonstrate benefits of the proposed combination of feedforward neural network and nonlinear model predictive control method for the multiscale crystallization process.
Keywords
deep learning, feedforward neural network, image analysis, multiscale crystallization process, Nonlinear Model Predictive Control
Suggested Citation
Wang L, Zhu Y. Neural-Network-Based Nonlinear Model Predictive Control of Multiscale Crystallization Process. (2023). LAPSE:2023.0906v1
Author Affiliations
Wang L: State Key Laboratory of Integrated Automation for Process Industry, Northeastern University, Shenyang 110819, China [ORCID]
Zhu Y: State Key Laboratory of Integrated Automation for Process Industry, Northeastern University, Shenyang 110819, China
Journal Name
Processes
Volume
10
Issue
11
First Page
2374
Year
2022
Publication Date
2022-11-12
ISSN
2227-9717
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Original Submission
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PII: pr10112374, Publication Type: Journal Article
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LAPSE:2023.0906v1
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https://doi.org/10.3390/pr10112374
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Feb 21, 2023
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