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

Abstract

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.

Graphical abstract

Keywords

smart chemical process operations / data generation / hybrid method / machine learning / particle swarm optimization

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Acknowledgements

This work was supported by the “Zhujiang Talent Program” High Talent Project of Guangdong Province (Grant No. 2017GC010614); and the National Natural Science Foundation of China (Grant No. 22078372).

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