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

  • 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.

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    Gefördert aus Mitteln des Open-Access-Publikationsfonds' der Bauhaus-Universität Weimar und vom Thüringer Ministerium für Wirtschaft, Wissenschaft und Digitale Gesellschaft (TMWWDG).

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Author:Postdoc Sina Faizollahzadeh ArdabiliORCiD, Prof. Bahman NajafORCiDGND, Prof Meysam Alizamir, Postdoc Amir MosaviORCiD, Shahaboddin ShamshirbandORCiD, Timon RabczukORCiDGND
DOI (Cite-Link):https://doi.org/10.3390/en11112889Cite-Link
URN (Cite-Link):https://nbn-resolving.org/urn:nbn:de:gbv:wim2-20181025-38170Cite-Link
URL:https://www.mdpi.com/1996-1073/11/11/2889
Parent Title (English):Energies
Publisher:MDPI
Place of publication:Basel
Document Type:Article
Language:English
Date of Publication (online):2018/10/24
Date of first Publication:2018/10/24
Release Date:2018/10/25
Publishing Institution:Bauhaus-Universität Weimar
Institutes:Fakultät Bauingenieurwesen / Institut für Strukturmechanik
Issue:11, 2889
First Page:1
Last Page:20
Tag:extreme learning machine; machine learning; response surface methodology; support vector machine
GND Keyword:Biodiesel; Optimierung
Dewey Decimal Classification:000 Informatik, Informationswissenschaft, allgemeine Werke
500 Naturwissenschaften und Mathematik
600 Technik, Medizin, angewandte Wissenschaften
BKL-Classification:05 Kommunikationswissenschaft
31 Mathematik
33 Physik
35 Chemie
54 Informatik
Open Access Publikationsfonds:Open-Access-Publikationsfonds 2018
Licence (German):License Logo Creative Commons 4.0 - Namensnennung (CC BY 4.0)