Modeling the effects of biodiesel chemical composition on iodine value using novel machine learning algorithm

•The feature selection methods were used to identify the important fatty acid methyl ester combination.•LSSVM model is most appropriate as the predictive model compared to the ANFIS, MLPNN, and DT models.•RF-WOA-LSSVM model was developed for predicting the IV of biodiesel as a function of fatty acid...

Full description

Saved in:

Bibliographic Details
Published in:	Fuel (Guildford) Vol. 316; p. 123348
Main Authors:	Huang, Yundi, Li, Fashe, Bao, Guirong, Xiao, Qingtai, Wang, Hua
Format:	Journal Article
Language:	English
Published:	Kidlington Elsevier Ltd 15.05.2022 Elsevier BV
Subjects:	Adaptive systems Algorithms Artificial neural networks Biodiesel fuels Biodiesel property Biofuels Carbon Chemical composition Correlation coefficient Correlation coefficients Decision trees Diesel Fatty acids Feature selection Fuzzy logic Gas chromatography Iodine Learning algorithms Least squares support vector machine Machine learning Mass spectrometry Mass spectroscopy Meta-heuristic optimization algorithm Modeling Modelling Multilayer perceptrons Multilayers Neural networks Optimization Principal components analysis Renewable energy Support vector machines Meta-heuristic optimization algorithm SBME Feature selection SVM Modeling DT Least squares support vector machine CSME OSME CME GA Chemical composition ANFIS MSE SSME ML HME JME OOB MME COP OME WOA PME COSME MAPE RME RMSE IV PSO PCA GC-MS MAE MLPNN PCC FAME PNME RBFNN RF LSSVM Biodiesel property RL RSME
ISSN:	0016-2361, 1873-7153
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Be the first to leave a comment!