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...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	Fuel (Guildford) Jg. 316; S. 123348
Hauptverfasser:	Huang, Yundi, Li, Fashe, Bao, Guirong, Xiao, Qingtai, Wang, Hua
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Kidlington Elsevier Ltd 15.05.2022 Elsevier BV
Schlagworte:	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-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!