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, Timon Rabczuk

Research output: Contribution to journalArticle

7 Citations (Scopus)

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.

Original languageEnglish
Article number2889
JournalEnergies
Volume11
Issue number11
DOIs
Publication statusPublished - 2018 Nov 1
Externally publishedYes

Fingerprint

Extreme Learning Machine
Response Surface Methodology
Support vector machines
Learning systems
Support Vector Machine
Esters
Adaptive Neuro-fuzzy Inference System
Catalyst
Artificial Neural Network
Fuzzy inference
Prediction
Hybrid Model
Alcohol
Neural networks
Correlation coefficient
Catalysts
Transesterification
Cooking
Experimental Data
Testing

Keywords

  • Biodiesel
  • Extreme learning machine (ELM)
  • Hybrid methods
  • Optimization
  • Response surface methodology (RSM)
  • Support vector machine (SVM)

ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment
  • Energy Engineering and Power Technology
  • Energy (miscellaneous)
  • Control and Optimization
  • Electrical and Electronic Engineering

Cite this

Ardabili, S. F., Najafi, B., Alizamir, M., Mosavi, A., Shamshirband, S., & Rabczuk, T. (2018). Using SVM-RSM and ELM-RSM approaches for optimizing the production process of methyl and ethyl esters. Energies, 11(11), [2889]. https://doi.org/10.3390/en11112889

Using SVM-RSM and ELM-RSM approaches for optimizing the production process of methyl and ethyl esters. / Ardabili, Sina Faizollahzadeh; Najafi, Bahman; Alizamir, Meysam; Mosavi, Amir; Shamshirband, Shahaboddin; Rabczuk, Timon.

In: Energies, Vol. 11, No. 11, 2889, 01.11.2018.

Research output: Contribution to journalArticle

Ardabili, SF, Najafi, B, Alizamir, M, Mosavi, A, Shamshirband, S & Rabczuk, T 2018, 'Using SVM-RSM and ELM-RSM approaches for optimizing the production process of methyl and ethyl esters', Energies, vol. 11, no. 11, 2889. https://doi.org/10.3390/en11112889
Ardabili SF, 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 Nov 1;11(11). 2889. https://doi.org/10.3390/en11112889
Ardabili, Sina Faizollahzadeh ; Najafi, Bahman ; Alizamir, Meysam ; Mosavi, Amir ; Shamshirband, Shahaboddin ; Rabczuk, Timon. / Using SVM-RSM and ELM-RSM approaches for optimizing the production process of methyl and ethyl esters. In: Energies. 2018 ; Vol. 11, No. 11.
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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.",
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AU - Rabczuk, Timon

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