Structure–activity relationship study of anti-wear additives in rapeseed oil based on machine learning and logistic regression

Liu, Jianfang; Yi, Chenglingzi; Zhang, Yaoyun; Yang, Sicheng; Liu, Ting; Zhang, Rongrong; Jia, Dan; Peng, Shuai; Yang, Qing

doi:10.1039/d3ra08871e

Cited by 1 publication

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“…Weinebeck et al developed a QSPR model for the tribological properties and structure of fuels using the elastic net regression method, distinguishing between effective and ineffective lubricants and offering a rapid screening tool for selecting potential biofuel molecules [16]. Liu et al utilized rapeseed oil as the base oil, employing machine learning (ML) and logistic regression (LR) to analyze the correlation between the wear scar diameter (WSD) and the molecular structure of anti-wear additives, resulting in a well-fitted QSPR model with predictive capabilities [17]. Wan et al used the least squares support vector regression (LS-SVR) method to establish an efficient QSPR model, selecting key molecular descriptors as inputs to characterize lubricants and identify ester-based compounds suitable for use as lubricants [18].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Lubrication Performances of Vegetable Oils by Genetic Functional Approximation Algorithm

Liu,

Zhang,

Yang

et al. 2024

Lubricants

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Vegetable oils, which are considered potential lubricants, are composed of different types and proportions of fatty acids. Because of their diverse types and varying compositions, they exhibit different lubrication performances. The genetic function approximation algorithm was used to model the quantitative structure–property relationship between fatty acid structure and the wear scar diameter and friction coefficients measured by four-ball friction and wear tests. Based on the models with adjusted R2 greater than 0.9 and fatty acid compositions of vegetable oils, the wear scar diameter and friction coefficients of Xanthoceras sorbifolia bunge oil and Soybean oil as validation oil samples were predicted. The difference between the predicted and experimental values was small, indicating that the models could accurately predict the lubrication performances of vegetable oils. The lubrication performances of 14 kinds of vegetable oils were predicted by GFA-QSPR models, and the primary factors influencing their lubrication properties were studied by cluster analysis. The results show that the content of C18:1 has a positive effect on the lubrication performances of vegetable oils, while the content of C18:3 has a negative effect, and the length of the carbon chain of fatty acids significantly affects their lubrication properties.

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Section: Introductionmentioning

confidence: 99%

Prediction of Lubrication Performances of Vegetable Oils by Genetic Functional Approximation Algorithm

Liu,

Zhang,

Yang

et al. 2024

Lubricants

Self Cite

View full text Add to dashboard Cite

show abstract

Structure–activity relationship study of anti-wear additives in rapeseed oil based on machine learning and logistic regression

Abstract: Anti-wear performance is a crucial quality of lubricants, and it is important to conduct research into the structure–activity relationship of anti-wear additives in bio-based lubricants.

Cited by 1 publication

References 45 publications

Prediction of Lubrication Performances of Vegetable Oils by Genetic Functional Approximation Algorithm

Prediction of Lubrication Performances of Vegetable Oils by Genetic Functional Approximation Algorithm

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