Deep Learning-Based Model Predictive Control for Continuous Stirred-Tank Reactor System

A continuous stirred-tank reactor (CSTR) system is widely applied in wastewater treatment processes. Its control is a challenging industrial-process-control problem due to great difficulty to achieve accurate system identification. This work proposes a deep learning-based model predictive control (D...

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Bibliographic Details
Published in:IEEE transaction on neural networks and learning systems 2021-08, Vol.32 (8), p.3643-3652
Main Authors: Wang, Gongming, Jia, Qing-Shan, Qiao, Junfei, Bi, Jing, Zhou, MengChu
Format: Article
Language:English
Subjects:
Belief networks
Chemical reactors
Computational modeling
Continuous stirred-tank reactor (CSTR) system
Continuously stirred tank reactors
Control stability
Control systems
Controllers
Deep learning
Feature extraction
growing deep belief network (GDBN) model
model predictive control
optimal controller
Optimization
Prediction algorithms
Prediction models
Predictive control
Predictive models
Reactors
Stability analysis
System identification
Training
Transfer learning
Wastewater treatment
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Summary:A continuous stirred-tank reactor (CSTR) system is widely applied in wastewater treatment processes. Its control is a challenging industrial-process-control problem due to great difficulty to achieve accurate system identification. This work proposes a deep learning-based model predictive control (DeepMPC) to model and control the CSTR system. The proposed DeepMPC consists of a growing deep belief network (GDBN) and an optimal controller. First, GDBN can automatically determine its size with transfer learning to achieve high performance in system identification, and it serves just as a predictive model of a controlled system. The model can accurately approximate the dynamics of the controlled system with a uniformly ultimately bounded error. Second, quadratic optimization is conducted to obtain an optimal controller. This work analyzes the convergence and stability of DeepMPC. Finally, the DeepMPC is used to model and control a second-order CSTR system. In the experiments, DeepMPC shows a better performance in modeling, tracking, and antidisturbance than the other state-of-the-art methods.
ISSN:2162-237X
2162-2388
DOI:10.1109/TNNLS.2020.3015869