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

Kexin Bi  Tong Qiu  
【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.

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中国期刊全文数据库 前2条
1 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年08期
2 Teng Houxing;Zhang Jinjun;;Modeling the viscoelasto-plastic behavior of waxy crude[J];Petroleum Science;2013年03期
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