An intelligent SVM modeling process for crude oil properties prediction based on a hybrid GA-PSO method

doi:10.1016/j.cjche.2018.12.015

Chinese Journal of Chemical Engineering ›› 2019, Vol. 27 ›› Issue (8): 1888-1894.DOI: 10.1016/j.cjche.2018.12.015

• Process Systems Engineering and Process Safety • Previous Articles Next Articles

An intelligent SVM modeling process for crude oil properties prediction based on a hybrid GA-PSO method

Kexin Bi¹, Tong Qiu²

1 Department of Chemical Engineering, Tsinghua University, Beijing 100084, China;
2 Beijing Key Laboratory of Industrial Big Data System and Application, Beijing 100084, China

Received:2018-09-13 Revised:2018-12-08 Online:2019-11-16 Published:2019-08-28
Contact: Tong Qiu
Supported by:
Supported by the National Natural Science Foundation of China (U1462206).

An intelligent SVM modeling process for crude oil properties prediction based on a hybrid GA-PSO method

Kexin Bi¹, Tong Qiu²

1 Department of Chemical Engineering, Tsinghua University, Beijing 100084, China;
2 Beijing Key Laboratory of Industrial Big Data System and Application, Beijing 100084, China

通讯作者: Tong Qiu
基金资助:
Supported by the National Natural Science Foundation of China (U1462206).

Abstract

Abstract: Properties prediction of crude oil remains an essential issue for refineries. In this communication, an exhaustive and extendable support vector machine (SVM) intelligent prediction process has been proposed to solve this problem. A novel hybrid genetic algorithm-particle swarm optimization (GA-PSO) method was applied to optimize the SVM model. The optimization process and result demonstrated that the newly proposed GA-PSO-SVM method was more accurate and time-saving than the classical GA or PSO method. Compared with the classical Grid-search SVM, the combined GA-PSO-SVM model appeared to be more applicable for the properties prediction task. The TBP distillation curve fitting was exampled to evaluate the performance of the developed model. The regression result demonstrated the high accuracy and efficiency of the proposed process. The model can be applied in the Industrial Internet as a plugin, and the adaptability and flexibility is demonstrated by the implement of crude oil molecular reconstruction employing the intelligent prediction process.

Key words: Intelligent properties prediction, Support vector machine, Hybrid GA-PSO, TBP distillation curve fitting

摘要： Properties prediction of crude oil remains an essential issue for refineries. In this communication, an exhaustive and extendable support vector machine (SVM) intelligent prediction process has been proposed to solve this problem. A novel hybrid genetic algorithm-particle swarm optimization (GA-PSO) method was applied to optimize the SVM model. The optimization process and result demonstrated that the newly proposed GA-PSO-SVM method was more accurate and time-saving than the classical GA or PSO method. Compared with the classical Grid-search SVM, the combined GA-PSO-SVM model appeared to be more applicable for the properties prediction task. The TBP distillation curve fitting was exampled to evaluate the performance of the developed model. The regression result demonstrated the high accuracy and efficiency of the proposed process. The model can be applied in the Industrial Internet as a plugin, and the adaptability and flexibility is demonstrated by the implement of crude oil molecular reconstruction employing the intelligent prediction process.

关键词: Intelligent properties prediction, Support vector machine, Hybrid GA-PSO, TBP distillation curve fitting

Kexin Bi, Tong Qiu. An intelligent SVM modeling process for crude oil properties prediction based on a hybrid GA-PSO method[J]. Chinese Journal of Chemical Engineering, 2019, 27(8): 1888-1894.

Kexin Bi, Tong Qiu. An intelligent SVM modeling process for crude oil properties prediction based on a hybrid GA-PSO method[J]. 中国化学工程学报, 2019, 27(8): 1888-1894.

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An intelligent SVM modeling process for crude oil properties prediction based on a hybrid GA-PSO method

An intelligent SVM modeling process for crude oil properties prediction based on a hybrid GA-PSO method

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