Physical and chemical indexes of synthetic base oils based on a wavelet neural network and genetic algorithm. Issue 1 (14th September 2019)

• Record Type: Journal Article
• Authors: Wang, Guomin; Wu, Yuanyuan; Jiang, Haifu; Zhang, Yanjie; Quan, Jiarong; Huang, Fuchuan
• Is Part Of: Industrial lubrication and tribology. Volume 72:Issue 1(2020)
• Page Start: 116
• Page End: 121
• Publication Date: 2019-09-14
• Subjects: Genetic algorithm -- Wavelet neural network -- Lubricating oil physical and chemical index; Lubrication and lubricants -- Periodicals; Tribology -- Periodicals; 621.89
• DOI: 10.1108/ILT-03-2019-0101
• Languages: English
• ISSNs: 0036-8792

Abstract

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.