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

doi:10.1007/s11705-021-2043-0

Front. Chem. Sci. Eng.

2022, Vol. 16

Issue (2) : 274-287 https://doi.org/10.1007/s11705-021-2043-0

RESEARCH ARTICLE

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

Haoqin Fang¹, Jianzhao Zhou¹, Zhenyu Wang¹, Ziqi Qiu¹, Yihua Sun², Yue Lin¹, Ke Chen¹, Xiantai Zhou¹(

), Ming Pan¹(

)

¹. School of Chemical Engineering and Technology, Sun Yat-Sen University, Zhuhai 519082, China
². School of Mathematics, Sun Yat-Sen University, Zhuhai 519082, China

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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.

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

Corresponding Author(s): Xiantai Zhou,Ming Pan

Just Accepted Date: 16 March 2021 Online First Date: 28 April 2021 Issue Date: 10 January 2022

Cite this article:

Haoqin Fang,Jianzhao Zhou,Zhenyu Wang, et al. Hybrid method integrating machine learning and particle swarm optimization for smart chemical process operations[J]. Front. Chem. Sci. Eng., 2022, 16(2): 274-287.

URL:

https://academic.hep.com.cn/fcse/EN/10.1007/s11705-021-2043-0
https://academic.hep.com.cn/fcse/EN/Y2022/V16/I2/274

Fig.1 Flowsheet of the PDH process built in Aspen Plus.

Tab.1 The flowrate of propane and propylene in moving bed reactors

Tab.2 The results of previous model [31] and Aspen Plus simulation in reaction process

Tab.3 Operating ranges and sampling points of inputs for data-driven modeling

Fig.2 Sampling for model training and testing of the two outputs in the PDH process: total annual profit (y₁) and propylene yield (y₂).

Fig.3 Illustration of a CRT structure with two parent nodes and three child nodes.

Fig.4 R² of test data with different combinations of k, distance calculations, and predictive equations based on (a) average KNN and (b) distanced-weighted KNN.

Tab.4 The best KNN models under different combinations of k, distance calculations, and predictive equations

Fig.5 R² of test data under different combinations of kernel functions and parameters c in SVR models.

Tab.5 The best SVR models under different combinations of Kernel functions and parameter c

Fig.6 R² of test data under different parameter N in DT models.

Fig.7 R² of test data with different optimization algorithms based on different activation functions: (a) ReLU, (b) softplus, (c) sigmoid, and (d) Tanh.

Tab.6 The best ANN models under different combinations of nodes in L₂ (N), activation functions, and optimization algorithms

Fig.8 The impact of key operating parameters (c₁, c₂) on controlling PSO procedure: (a) ω = 0.05, (b) ω = 0.45, (c) ω = 3.0, and (d) ω = 10.

Tab.7 The optimal solutions obtained by PSO ^a)

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