Physical and chemical indexes of synthetic base oils based on a wavelet neural network and genetic algorithm
Industrial Lubrication and Tribology
ISSN: 0036-8792
Article publication date: 19 September 2019
Issue publication date: 14 January 2020
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