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

Sina Faizollahzadeh Ardabili, Bahman Najafi, Meysam Alizamir, Amir Mosavi, Shahaboddin Shamshirband and Timon Rabczuk
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Sina Faizollahzadeh Ardabili: Biosystem Engineering Department, University of Mohaghegh Ardabili, Ardabil 5619911367, Iran
Bahman Najafi: Biosystem Engineering Department, University of Mohaghegh Ardabili, Ardabil 5619911367, Iran
Meysam Alizamir: Department of the Civil Engineering, Hamedan Branch, Islamic Azad University, Hamedan, Iran
Shahaboddin Shamshirband: Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Vietnam
Timon Rabczuk: Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia
Authors registered in the RePEc Author Service: Shahab S Band

Energies, 2018, vol. 11, issue 11, 1-19

Abstract: 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.

Keywords: biodiesel; optimization; extreme learning machine (ELM); hybrid methods; response surface methodology (RSM); support vector machine (SVM) (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

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