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

Author

Listed:
  • 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)

  • Amir Mosavi

    (Institute of Structural Mechanics, Bauhaus University Weimar, D-99423 Weimar, Germany
    Institute of Automation, Obuda University, 1431 Budapest, Hungary
    Institute of Advanced Studies Koszeg (IASK), 9730 Koszeg, Hungary)

  • Shahaboddin Shamshirband

    (Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Vietnam
    Faculty of Information Technology, 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)

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.

Suggested Citation

  • Sina Faizollahzadeh Ardabili & Bahman Najafi & Meysam Alizamir & Amir Mosavi & Shahaboddin Shamshirband & Timon Rabczuk, 2018. "Using SVM-RSM and ELM-RSM Approaches for Optimizing the Production Process of Methyl and Ethyl Esters," Energies, MDPI, vol. 11(11), pages 1-19, October.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:11:p:2889-:d:178004
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    3. Shahaboddin Shamshirband & Masoud Hadipoor & Alireza Baghban & Amir Mosavi & Jozsef Bukor & Annamária R. Várkonyi-Kóczy, 2019. "Developing an ANFIS-PSO Model to Predict Mercury Emissions in Combustion Flue Gases," Mathematics, MDPI, vol. 7(10), pages 1-16, October.
    4. Nosratabadi, Saeed & Mosavi, Amir & Shamshirband, Shahaboddin & Zavadskas, Edmundas Kazimieras & Rakotonirainy, Andry & Chau, Kwok Wing, 2020. "Sustainable Business Models: A Review," OSF Preprints ts54m, Center for Open Science.
    5. Chen, Yu & Yu, Hui & Liu, Chengjie & Xie, Jin & Han, Jun & Dai, Houde, 2024. "Synergistic fusion of physical modeling and data-driven approaches for parameter inference to enzymatic biodiesel production system," Applied Energy, Elsevier, vol. 373(C).
    6. Haris Mahmood Khan & Tanveer Iqbal & M. A. Mujtaba & Manzoore Elahi M. Soudagar & Ibham Veza & I. M. Rizwanul Fattah, 2021. "Microwave Assisted Biodiesel Production Using Heterogeneous Catalysts," Energies, MDPI, vol. 14(23), pages 1-16, December.
    7. Han, Yongming & Liu, Shuang & Cong, Di & Geng, Zhiqiang & Fan, Jinzhen & Gao, Jingyang & Pan, Tingrui, 2021. "Resource optimization model using novel extreme learning machine with t-distributed stochastic neighbor embedding: Application to complex industrial processes," Energy, Elsevier, vol. 225(C).
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