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

Kexin Bi; Tong Qiu

doi:10.1016/j.cjche.2018.12.015

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

Process Systems Engineering and Process Safety | Updated：2026-01-06

- 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
- 中国化学工程学报（英文版） 2019年27卷第8期页码：1888-1894
- Affiliations：
  
  1. Department of Chemical Engineering, Tsinghua University,Beijing,China,100084
  2. Beijing Key Laboratory of Industrial Big Data System and Application,Beijing,China,100084
- Author bio：
- Funds：
- DOI：10.1016/j.cjche.2018.12.015
  中图分类号：
- 纸质出版：2019
- Accepted：
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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（英文版）, 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]. 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（英文版）, 2019,27(8):1888-1894. DOI： 10.1016/j.cjche.2018.12.015.

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. DOI： 10.1016/j.cjche.2018.12.015.

摘要

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.

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