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Optimization of heterogeneous Catalyst-assisted fatty acid methyl esters biodiesel production from Soybean oil with different Machine learning methods
journal contribution
posted on 2022-11-22, 04:07 authored by W Kamal Abdelbasset, SM Alrawaili, SH Elsayed, T Diana, S Ghazali, BF Felemban, M Zwawi, M Algarni, CH Su, Hoang Chinh NguyenHoang Chinh Nguyen, O MahmoudThere is a growing attention to the bio and renewable energies due to fast depletion of fossil fuels as well as the global warming problem. Here, we developed a modeling and simulation method by means of artificial intelligence (AI) for prediction of the bioenergy production from vegetable bean oil. AI methods are well known for prediction of complex and nonlinear process. Three distinct Adaptive Boosted models including Huber regression, LASSO, and Support Vector Regression (SVR) as well as artificial neural network (ANN) were applied in this study to predict actual yield of Fatty acid methyl esters (FAME) production. All boosted utilizing the Adaptive boosting algorithm. The important influencing parameters on the biodiesel production such as the catalyst loading (CAO/Ag, wt%) and methanol to oil (Soybean oil) molar ratio were selected as the input variables of models while the yield of FAME production was selected as output. Model hyper-parameters were tuned to maintain generality while improving prediction accuracy. The models were evaluated using three distinct metrics Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R2. Error rates of 8.16780E-01, 4.43895E-01, 2.06692E + 00, and 3.92713 E-01 were obtained with the MAE metric for boosted Huber, SVR, LASSO and ANN models. On the other hand, the RMSE error of these models were about 1.092E-02, 1.015E-02, 2.669E-02, and 1.01174E-02, respectively. Finally, the R-square score were calculated for boosted Huber, boosted SVR, and boosted LASSO as 0.976, 0.990, 0.872, and 0.99702, respectively. Therefore, it can be concluded that although the boosted SVR and ANN models were better models for prediction of process efficiency in terms of error, but all algorithms had high accuracy. The optimum yield of 83.77% and 81.60% for biodiesel production were observed at optimum operating values from boosted SVR and ANN models, respectively.
History
Journal
Arabian Journal of ChemistryVolume
15Article number
103915Pagination
1-10Location
Amsterdam, The NetherlandsPublisher DOI
ISSN
1878-5352eISSN
1878-5379Language
EnglishPublication classification
C1 Refereed article in a scholarly journalIssue
7Publisher
ElsevierUsage metrics
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No categories selectedKeywords
Bioenergy productionChemistryChemistry, MultidisciplinaryFatty acid methyl ester (FAME)LASSOMachine learning methodNEURAL-NETWORKSOptimization and analysisPhysical SciencesREGRESSIONROBUST ESTIMATIONScience & TechnologySEEDSUPPORT VECTOR MACHINETRANSESTERIFICATIONTransesterification process7 Affordable and Clean Energy13 Climate ActionSchool of Life and Environmental SciencesFaculty of Science Engineering and Built EnvironmentChemical Sciences
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