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Article

Using SVM-RSM and ELM-RSM Approaches for Optimizing the Production Process of Methyl and Ethyl Esters

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Biosystem Engineering Department, University of Mohaghegh Ardabili, Ardabil 5619911367, Iran
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Department of the Civil Engineering, Hamedan Branch, Islamic Azad University, Hamedan, Iran
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Institute of Structural Mechanics, Bauhaus University Weimar, D-99423 Weimar, Germany
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Institute of Automation, Obuda University, 1431 Budapest, Hungary
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Institute of Advanced Studies Koszeg (IASK), 9730 Koszeg, Hungary
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Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Vietnam
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Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City, Vietnam
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Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia
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Author to whom correspondence should be addressed.
Energies 2018, 11(11), 2889; https://doi.org/10.3390/en11112889
Received: 15 July 2018 / Revised: 18 September 2018 / Accepted: 8 October 2018 / Published: 24 October 2018
The production of a desired product needs an effective use of the experimental model. The present study proposes an extreme learning machine (ELM) and a support vector machine (SVM) integrated with the response surface methodology (RSM) to solve the complexity in optimization and prediction of the ethyl ester and methyl ester production process. The novel hybrid models of ELM-RSM and ELM-SVM are further used as a case study to estimate the yield of methyl and ethyl esters through a trans-esterification process from waste cooking oil (WCO) based on American Society for Testing and Materials (ASTM) standards. The results of the prediction phase were also compared with artificial neural networks (ANNs) and adaptive neuro-fuzzy inference system (ANFIS), which were recently developed by the second author of this study. Based on the results, an ELM with a correlation coefficient of 0.9815 and 0.9863 for methyl and ethyl esters, respectively, had a high estimation capability compared with that for SVM, ANNs, and ANFIS. Accordingly, the maximum production yield was obtained in the case of using ELM-RSM of 96.86% for ethyl ester at a temperature of 68.48 °C, a catalyst value of 1.15 wt. %, mixing intensity of 650.07 rpm, and an alcohol to oil molar ratio (A/O) of 5.77; for methyl ester, the production yield was 98.46% at a temperature of 67.62 °C, a catalyst value of 1.1 wt. %, mixing intensity of 709.42 rpm, and an A/O of 6.09. Therefore, ELM-RSM increased the production yield by 3.6% for ethyl ester and 3.1% for methyl ester, compared with those for the experimental data. View Full-Text
Keywords: biodiesel; optimization; extreme learning machine (ELM); hybrid methods; response surface methodology (RSM); support vector machine (SVM) biodiesel; optimization; extreme learning machine (ELM); hybrid methods; response surface methodology (RSM); support vector machine (SVM)
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MDPI and ACS Style

Faizollahzadeh Ardabili, S.; Najafi, B.; Alizamir, M.; Mosavi, A.; Shamshirband, S.; Rabczuk, T. Using SVM-RSM and ELM-RSM Approaches for Optimizing the Production Process of Methyl and Ethyl Esters. Energies 2018, 11, 2889. https://doi.org/10.3390/en11112889

AMA Style

Faizollahzadeh Ardabili S, Najafi B, Alizamir M, Mosavi A, Shamshirband S, Rabczuk T. Using SVM-RSM and ELM-RSM Approaches for Optimizing the Production Process of Methyl and Ethyl Esters. Energies. 2018; 11(11):2889. https://doi.org/10.3390/en11112889

Chicago/Turabian Style

Faizollahzadeh Ardabili, Sina, Bahman Najafi, Meysam Alizamir, Amir Mosavi, Shahaboddin Shamshirband, and Timon Rabczuk. 2018. "Using SVM-RSM and ELM-RSM Approaches for Optimizing the Production Process of Methyl and Ethyl Esters" Energies 11, no. 11: 2889. https://doi.org/10.3390/en11112889

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