润滑性
植物油
生化工程
主成分分析
质量(理念)
数量结构-活动关系
化学
工艺工程
制浆造纸工业
数学
计算机科学
环境科学
有机化学
机器学习
工程类
人工智能
哲学
认识论
作者
Rongrong Zhang,Sicheng Yang,Ting Liu,Yaoyun Zhang,Chenglingzi Yi,Dechang Jia,Jianfang Liu
出处
期刊:Fuel
[Elsevier]
日期:2024-02-01
卷期号:358: 130120-130120
标识
DOI:10.1016/j.fuel.2023.130120
摘要
With the increasing awareness of human environmental protection, it is urgent to find potential bio-lubricants. Vegetable oil has attracted the interest of scientists because of its good lubricity and biodegradability. The external properties of vegetable oils are closely related to their internal chemical composition. Fatty acids are the main chemical components in vegetable oils. The industrial capacity of fatty acids is accurately predicted and evaluated by theory, which is very helpful for the auxiliary design of green lubricants. Combining 9 meaningful molecular descriptors and principal component regression, a calculation scheme for predicting the anti-wear properties of fatty acids and vegetable oils was proposed. The training set and validation set of the model proved that the model had excellent predictive performance. Then, on this basis, through literature retrieval and factor score study, the research group successfully screened out a class of vegetable oils with the broadest industrial application potential, including yuzu, sesame, olive and so on. This project aims to provide new ideas about the design of green bio-based lubricants and to promote the development of vegetable oils in the industry.
科研通智能强力驱动
Strongly Powered by AbleSci AI