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...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	Fuel (Guildford) Ročník 316; s. 123348
Hlavní autoři:	Huang, Yundi, Li, Fashe, Bao, Guirong, Xiao, Qingtai, Wang, Hua
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Kidlington Elsevier Ltd 15.05.2022 Elsevier BV
Témata:	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
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Buďte první, kdo okomentuje tento záznam!