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 e...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2018-10-01
|
| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/11/11/2889 |
| _version_ | 1828396097982169088 |
|---|---|
| author | Sina Faizollahzadeh Ardabili Bahman Najafi Meysam Alizamir Amir Mosavi Shahaboddin Shamshirband Timon Rabczuk |
| author_facet | Sina Faizollahzadeh Ardabili Bahman Najafi Meysam Alizamir Amir Mosavi Shahaboddin Shamshirband Timon Rabczuk |
| author_sort | Sina Faizollahzadeh Ardabili |
| collection | DOAJ |
| description | 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 (<i>A</i>/<i>O</i>) 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 <i>A</i>/<i>O</i> 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. |
| first_indexed | 2024-12-10T08:24:26Z |
| format | Article |
| id | doaj.art-3048c97b4c8a473081e5e6bdf4020582 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-12-10T08:24:26Z |
| publishDate | 2018-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-3048c97b4c8a473081e5e6bdf40205822022-12-22T01:56:16ZengMDPI AGEnergies1996-10732018-10-011111288910.3390/en11112889en11112889Using SVM-RSM and ELM-RSM Approaches for Optimizing the Production Process of Methyl and Ethyl EstersSina Faizollahzadeh Ardabili0Bahman Najafi1Meysam Alizamir2Amir Mosavi3Shahaboddin Shamshirband4Timon Rabczuk5Biosystem Engineering Department, University of Mohaghegh Ardabili, Ardabil 5619911367, IranBiosystem Engineering Department, University of Mohaghegh Ardabili, Ardabil 5619911367, IranDepartment of the Civil Engineering, Hamedan Branch, Islamic Azad University, Hamedan, IranInstitute of Structural Mechanics, Bauhaus University Weimar, D-99423 Weimar, GermanyDepartment for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, VietnamDepartment of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi ArabiaThe 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 (<i>A</i>/<i>O</i>) 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 <i>A</i>/<i>O</i> 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.https://www.mdpi.com/1996-1073/11/11/2889biodieseloptimizationextreme learning machine (ELM)hybrid methodsresponse surface methodology (RSM)support vector machine (SVM) |
| spellingShingle | Sina Faizollahzadeh Ardabili Bahman Najafi Meysam Alizamir Amir Mosavi Shahaboddin Shamshirband Timon Rabczuk Using SVM-RSM and ELM-RSM Approaches for Optimizing the Production Process of Methyl and Ethyl Esters Energies biodiesel optimization extreme learning machine (ELM) hybrid methods response surface methodology (RSM) support vector machine (SVM) |
| title | Using SVM-RSM and ELM-RSM Approaches for Optimizing the Production Process of Methyl and Ethyl Esters |
| title_full | Using SVM-RSM and ELM-RSM Approaches for Optimizing the Production Process of Methyl and Ethyl Esters |
| title_fullStr | Using SVM-RSM and ELM-RSM Approaches for Optimizing the Production Process of Methyl and Ethyl Esters |
| title_full_unstemmed | Using SVM-RSM and ELM-RSM Approaches for Optimizing the Production Process of Methyl and Ethyl Esters |
| title_short | Using SVM-RSM and ELM-RSM Approaches for Optimizing the Production Process of Methyl and Ethyl Esters |
| title_sort | using svm rsm and elm rsm approaches for optimizing the production process of methyl and ethyl esters |
| topic | biodiesel optimization extreme learning machine (ELM) hybrid methods response surface methodology (RSM) support vector machine (SVM) |
| url | https://www.mdpi.com/1996-1073/11/11/2889 |
| work_keys_str_mv | AT sinafaizollahzadehardabili usingsvmrsmandelmrsmapproachesforoptimizingtheproductionprocessofmethylandethylesters AT bahmannajafi usingsvmrsmandelmrsmapproachesforoptimizingtheproductionprocessofmethylandethylesters AT meysamalizamir usingsvmrsmandelmrsmapproachesforoptimizingtheproductionprocessofmethylandethylesters AT amirmosavi usingsvmrsmandelmrsmapproachesforoptimizingtheproductionprocessofmethylandethylesters AT shahaboddinshamshirband usingsvmrsmandelmrsmapproachesforoptimizingtheproductionprocessofmethylandethylesters AT timonrabczuk usingsvmrsmandelmrsmapproachesforoptimizingtheproductionprocessofmethylandethylesters |