Hybrid method integrating machine learning and particle swarm optimization for smart chemical process operations

Modeling and optimization is crucial to smart chemical process operations. However, a large number of nonlinearities must be considered in a typical chemical process according to complex unit operations, chemical reactions and separations. This leads to a great challenge of implementing mechanistic...

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Bibliographic Details
Published in:Frontiers of chemical science and engineering 2022-02, Vol.16 (2), p.274-287
Main Authors: Fang, Haoqin, Zhou, Jianzhao, Wang, Zhenyu, Qiu, Ziqi, Sun, Yihua, Lin, Yue, Chen, Ke, Zhou, Xiantai, Pan, Ming
Format: Article
Language:English
Subjects:
Artificial neural networks
Chemical reactions
Chemistry
Chemistry and Materials Science
Complexity
Decision trees
Dehydrogenation
Industrial Chemistry/Chemical Engineering
Machine learning
Nanotechnology
Optimization
Particle swarm optimization
Prediction models
Research Article
Support vector machines
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Summary:Modeling and optimization is crucial to smart chemical process operations. However, a large number of nonlinearities must be considered in a typical chemical process according to complex unit operations, chemical reactions and separations. This leads to a great challenge of implementing mechanistic models into industrial-scale problems due to the resulting computational complexity. Thus, this paper presents an efficient hybrid framework of integrating machine learning and particle swarm optimization to overcome the aforementioned difficulties. An industrial propane dehydrogenation process was carried out to demonstrate the validity and efficiency of our method. Firstly, a data set was generated based on process mechanistic simulation validated by industrial data, which provides sufficient and reasonable samples for model training and testing. Secondly, four well-known machine learning methods, namely, K-nearest neighbors, decision tree, support vector machine, and artificial neural network, were compared and used to obtain the prediction models of the processes operation. All of these methods achieved highly accurate model by adjusting model parameters on the basis of high-coverage data and properly features. Finally, optimal process operations were obtained by using the particle swarm optimization approach.
ISSN:2095-0179
2095-0187
DOI:10.1007/s11705-021-2043-0