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The optimized operational conditions for biodiesel production from soybean oil and application of artificial neural networks for estimation of the biodiesel yield

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  • Moradi, G.R.
  • Dehghani, S.
  • Khosravian, F.
  • Arjmandzadeh, A.

Abstract

In this study, transesterification of soybean oil to biodiesel using KOH in different process conditions were studied. The investigated conditions were the molar ratio of methanol/oil, catalyst amount and reaction temperature. Optimal conditions were found to be methanol/oil molar ratio, 9:1; catalyst amount, 1 wt%; reaction temperature, 60 °C. Biodiesel yield for these conditions was obtained 93.2% in 1 h. In addition, the artificial neural network has been applied to estimate the biodiesel yield. The multilayer feed forward neural network with three inputs and one output has been trained with different algorithms and different numbers of neurons in the hidden layer. The accuracy of the proposed model was found to agree nearly with the experimental results over a wide range of experimental conditions. The results clearly depict that the neural network is a powerful tool to estimate the reaction rate and the designed neural network can be used instead of approximate and complex analytical equations.

Suggested Citation

  • Moradi, G.R. & Dehghani, S. & Khosravian, F. & Arjmandzadeh, A., 2013. "The optimized operational conditions for biodiesel production from soybean oil and application of artificial neural networks for estimation of the biodiesel yield," Renewable Energy, Elsevier, vol. 50(C), pages 915-920.
    • Handle: RePEc:eee:renene:v:50:y:2013:i:c:p:915-920
    DOI: 10.1016/j.renene.2012.08.070
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    1. Kalogirou, Soteris A. & Bojic, Milorad, 2000. "Artificial neural networks for the prediction of the energy consumption of a passive solar building," Energy, Elsevier, vol. 25(5), pages 479-491.
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    1. Sakthivel, G. & Sivaraja, C.M. & Ikua, Bernard W., 2019. "Prediction OF CI engine performance, emission and combustion parameters using fish oil as a biodiesel by fuzzy-GA," Energy, Elsevier, vol. 166(C), pages 287-306.
    2. Vardast, Neda & Haghighi, Mohammad & Dehghani, Sahar, 2019. "Sono-dispersion of calcium over Al-MCM-41used as a nanocatalyst for biodiesel production from sunflower oil: Influence of ultrasound irradiation and calcium content on catalytic properties and perform," Renewable Energy, Elsevier, vol. 132(C), pages 979-988.
    3. Abhirup Khanna & Bhawna Yadav Lamba & Sapna Jain & Vadim Bolshev & Dmitry Budnikov & Vladimir Panchenko & Alexandr Smirnov, 2023. "Biodiesel Production from Jatropha: A Computational Approach by Means of Artificial Intelligence and Genetic Algorithm," Sustainability, MDPI, vol. 15(12), pages 1-33, June.
    4. Silitonga, A.S. & Shamsuddin, A.H. & Mahlia, T.M.I. & Milano, Jassinne & Kusumo, F. & Siswantoro, Joko & Dharma, S. & Sebayang, A.H. & Masjuki, H.H. & Ong, Hwai Chyuan, 2020. "Biodiesel synthesis from Ceiba pentandra oil by microwave irradiation-assisted transesterification: ELM modeling and optimization," Renewable Energy, Elsevier, vol. 146(C), pages 1278-1291.
    5. Sakthivel, G. & Sivakumar, R. & Saravanan, N. & Ikua, Bernard W., 2017. "A decision support system to evaluate the optimum fuel blend in an IC engine to enhance the energy efficiency and energy management," Energy, Elsevier, vol. 140(P1), pages 566-583.
    6. Iftikhar Ahmad & Adil Sana & Manabu Kano & Izzat Iqbal Cheema & Brenno C. Menezes & Junaid Shahzad & Zahid Ullah & Muzammil Khan & Asad Habib, 2021. "Machine Learning Applications in Biofuels’ Life Cycle: Soil, Feedstock, Production, Consumption, and Emissions," Energies, MDPI, vol. 14(16), pages 1-27, August.
    7. Marina Corral Bobadilla & Roberto Fernández Martínez & Rubén Lostado Lorza & Fátima Somovilla Gómez & Eliseo P. Vergara González, 2018. "Optimizing Biodiesel Production from Waste Cooking Oil Using Genetic Algorithm-Based Support Vector Machines," Energies, MDPI, vol. 11(11), pages 1-19, November.
    8. Can, Özer & Baklacioglu, Tolga & Özturk, Erkan & Turan, Onder, 2022. "Artificial neural networks modeling of combustion parameters for a diesel engine fueled with biodiesel fuel," Energy, Elsevier, vol. 247(C).
    9. Shelare, Sagar D. & Belkhode, Pramod N. & Nikam, Keval Chandrakant & Jathar, Laxmikant D. & Shahapurkar, Kiran & Soudagar, Manzoore Elahi M. & Veza, Ibham & Khan, T.M. Yunus & Kalam, M.A. & Nizami, Ab, 2023. "Biofuels for a sustainable future: Examining the role of nano-additives, economics, policy, internet of things, artificial intelligence and machine learning technology in biodiesel production," Energy, Elsevier, vol. 282(C).
    10. Vellaiyan, Suresh & Partheeban, C.M. Anand, 2020. "Combined effect of water emulsion and ZnO nanoparticle on emissions pattern of soybean biodiesel fuelled diesel engine," Renewable Energy, Elsevier, vol. 149(C), pages 1157-1166.
    11. Manieniyan, V. & Vinodhini, G. & Senthilkumar, R. & Sivaprakasam, S., 2016. "Wear element analysis using neural networks of a DI diesel engine using biodiesel with exhaust gas recirculation," Energy, Elsevier, vol. 114(C), pages 603-612.
    12. 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.
    13. Aghbashlo, Mortaza & Hosseinpour, Soleiman & Tabatabaei, Meisam & Dadak, Ali, 2017. "Fuzzy modeling and optimization of the synthesis of biodiesel from waste cooking oil (WCO) by a low power, high frequency piezo-ultrasonic reactor," Energy, Elsevier, vol. 132(C), pages 65-78.
    14. Ricardo Gava & Dthenifer Cordeiro Santana & Mayara Favero Cotrim & Fernando Saragosa Rossi & Larissa Pereira Ribeiro Teodoro & Carlos Antonio da Silva Junior & Paulo Eduardo Teodoro, 2022. "Soybean Cultivars Identification Using Remotely Sensed Image and Machine Learning Models," Sustainability, MDPI, vol. 14(12), pages 1-12, June.
    15. Soltani, Soroush & Roodbar Shojaei, Taha & Khanian, Nasrin & Shean Yaw Choong, Thomas & Asim, Nilofar & Zhao, Yue, 2022. "Artificial neural network method modeling of microwave-assisted esterification of PFAD over mesoporous TiO2‒ZnO catalyst," Renewable Energy, Elsevier, vol. 187(C), pages 760-773.
    16. Tamilselvan, P. & Nallusamy, N. & Rajkumar, S., 2017. "A comprehensive review on performance, combustion and emission characteristics of biodiesel fuelled diesel engines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 79(C), pages 1134-1159.
    17. Dehghani, Sahar & Haghighi, Mohammad, 2020. "Sono-enhanced dispersion of CaO over Zr-Doped MCM-41 bifunctional nanocatalyst with various Si/Zr ratios for conversion of waste cooking oil to biodiesel," Renewable Energy, Elsevier, vol. 153(C), pages 801-812.

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