文献类型：期刊文献

英文题名：Toward prediction of kinematic viscosity of biodiesel using a robust approach

作者：Zhou, Liang

第一作者：周亮

通信作者：Zhou, L[1]

机构：[1]Guizhou Inst Technol, Sch Chem Engn, Guiyang 550003, Guizhou, Peoples R China

第一机构：贵州理工学院化学工程学院

通信机构：corresponding author), Guizhou Inst Technol, Sch Chem Engn, Guiyang 550003, Guizhou, Peoples R China.|贵州理工学院化学工程学院;贵州理工学院;

年份：2018

卷号：40

期号：23

起止页码：2895-2902

外文期刊名：ENERGY SOURCES PART A-RECOVERY UTILIZATION AND ENVIRONMENTAL EFFECTS

收录：;EI(收录号：20183805831421);Scopus(收录号：2-s2.0-85053353107);WOS:【SCI-EXPANDED(收录号:WOS:000445248700013)】；

基金：The author is pleased to acknowledge the financial support of this startup project for high-level talents of Guizhou Institute of Technology (XJGC20150102). The author also acknowledges the financial support of this research by the Natural Science Research Project of Guizhou Provincial Education Office in 2015 ([2015]416) and support from Guizhou Province Science and Technology Fund in 2016 ([2016]1059).

语种：英文

外文关键词：ANN; artificial intelligence; biodiesel; kinematic viscosity; predictive modeling

摘要：Especially by using a renewable source of fuels such as biodiesel, a large number of high-quality researches have been performed on the reduction of pollution released from fossil fuels. Transesterification process is a common way for the production of biodiesel from vegetable oil, animal fat, and algae oil in the presence of alcohol and catalyst. Viscosity is one of the important physical fuel properties used in the selection of biodiesel. Experimental measurement of viscosity is a time-consuming task. Hence, in this contribution, applicability and performance of two artificial neural network-based models named least square support vector machine (LSSVM) and genetic algorithm-radial basis function (GA-RBF) for the prediction of kinematic viscosity of biodiesel were investigated. Root-meansquare error, coefficient of determination (R-2), and average absolute relative deviation of each modeling were reported for each LSSVM and GA-RBF models. Modeling results show that the proposed LSSVM model is more accurate and robust than GA-RBF model.