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Physical and chemical indexes of synthetic base oils based on a wavelet neural network and genetic algorithm

Guomin Wang (Key Laboratory of Guangxi Petrochemical Resource Processing and Process Intensification Technology, College of Mechanical Engineering, Guangxi University, Nanning, China)
Yuanyuan Wu (Key Laboratory of Guangxi Petrochemical Resource Processing and Process Intensification Technology, College of Mechanical Engineering, Guangxi University, Nanning, China)
Haifu Jiang (College of Mechanical Engineering, Guangxi University, Nanning, China)
Yanjie Zhang (College of Mechanical Engineering, Guangxi University, Nanning, China)
Jiarong Quan (College of Mechanical Engineering, Guangxi University, Nanning, China)
Fuchuan Huang (Key Laboratory of Guangxi Petrochemical Resource Processing and Process Intensification Technology, College of Mechanical Engineering, Guangxi University, Nanning, China)

Industrial Lubrication and Tribology

ISSN: 0036-8792

Article publication date: 19 September 2019

Issue publication date: 14 January 2020

131

Abstract

Purpose

The purpose of this paper is to use the wavelet neural network and genetic algorithm to study the effects of polyalphaolefin, TMP108 and OCP0016 on the kinematic viscosity, viscosity index and pour point of lubricating oil.

Design/methodology/approach

Wavelet neural network is used to train the known samples, test the unknown samples and compare the obtained results with those obtained with a traditional empirical formula.

Findings

It is found that the wavelet neural network prediction value is closer to the experimental value than the traditional empirical formula calculation value.

Originality/value

The results show that the wavelet neural network can be used to study the physical and chemical indexes of lubricating oil.

Keywords

Acknowledgements

This research was supported financially by the Dean Project of Guangxi Key Laboratory of Petrochemical Resource Processing and Process Intensification Technology, China [2016Z005], Guangxi Special Research Project for Technology Innovation Guidance, China [2017AC05020].

Citation

Wang, G., Wu, Y., Jiang, H., Zhang, Y., Quan, J. and Huang, F. (2020), "Physical and chemical indexes of synthetic base oils based on a wavelet neural network and genetic algorithm", Industrial Lubrication and Tribology, Vol. 72 No. 1, pp. 116-121. https://doi.org/10.1108/ILT-03-2019-0101

Publisher

:

Emerald Publishing Limited

Copyright © 2019, Emerald Publishing Limited

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