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Artificial neural networks used for the prediction of the cetane number of biodiesel

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  • Ramadhas, A.S.
  • Jayaraj, S.
  • Muraleedharan, C.
  • Padmakumari, K.

Abstract

Cetane number (CN) is one of the most significant properties to specify the ignition quality of any fuel for internal combustion engines. The CN of biodiesel varies widely in the range of 48–67 depending upon various parameters including the oil processing technology and climatic conditions where the feedstock (vegetable oil) is collected. Determination of the CN of a fuel by an experimental procedure is a tedious job for the upcoming biodiesel production industry. The fatty acid composition of base oil predominantly affects the CN of the biodiesel produced from it. This paper discusses the currently available CN estimation techniques and the necessity of accurate prediction of CN of biodiesel. Artificial Neural Network (ANN) models are developed to predict the CN of any biodiesel. The present paper deals with the application of multi-layer feed forward, radial base, generalized regression and recurrent network models for the prediction of CN. The fatty acid compositions of biodiesel and the experimental CNs are used to train the networks. The parameters that affect the development of the model are also discussed. ANN predicted CNs are found to be in agreement with the experimental CNs. Hence, the ANN models developed can be used reliably for the prediction of CN of biodiesel.

Suggested Citation

  • Ramadhas, A.S. & Jayaraj, S. & Muraleedharan, C. & Padmakumari, K., 2006. "Artificial neural networks used for the prediction of the cetane number of biodiesel," Renewable Energy, Elsevier, vol. 31(15), pages 2524-2533.
    • Handle: RePEc:eee:renene:v:31:y:2006:i:15:p:2524-2533
    DOI: 10.1016/j.renene.2006.01.009
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    References listed on IDEAS

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    1. Kalogirou, Soteris A., 2001. "Artificial neural networks in renewable energy systems applications: a review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 5(4), pages 373-401, December.
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    1. Kaya, Canan & Hamamci, Candan & Baysal, Akin & Akba, Osman & Erdogan, Sait & Saydut, Abdurrahman, 2009. "Methyl ester of peanut (Arachis hypogea L.) seed oil as a potential feedstock for biodiesel production," Renewable Energy, Elsevier, vol. 34(5), pages 1257-1260.
    2. Cherng-Yuan Lin & Yi-Wei Lin, 2012. "Fuel Characteristics of Biodiesel Produced from a High-Acid Oil from Soybean Soapstock by Supercritical-Methanol Transesterification," Energies, MDPI, vol. 5(7), pages 1-11, July.
    3. Evangelos G. Giakoumis & Christos K. Sarakatsanis, 2019. "A Comparative Assessment of Biodiesel Cetane Number Predictive Correlations Based on Fatty Acid Composition," Energies, MDPI, vol. 12(3), pages 1-30, January.
    4. Olalekan Alade & Dhafer Al Shehri & Mohamed Mahmoud & Kyuro Sasaki, 2019. "Viscosity–Temperature–Pressure Relationship of Extra-Heavy Oil (Bitumen): Empirical Modelling versus Artificial Neural Network (ANN)," Energies, MDPI, vol. 12(12), pages 1-13, June.
    5. Noushabadi, Abolfazl Sajadi & Dashti, Amir & Raji, Mojtaba & Zarei, Alireza & Mohammadi, Amir H., 2020. "Estimation of cetane numbers of biodiesel and diesel oils using regression and PSO-ANFIS models," Renewable Energy, Elsevier, vol. 158(C), pages 465-473.
    6. Kumar, Niraj & Varun, & Chauhan, Sant Ram, 2013. "Performance and emission characteristics of biodiesel from different origins: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 21(C), pages 633-658.
    7. K. E. K. Vimal & S. Vinodh & A. Raja, 2017. "Optimization of process parameters of SMAW process using NN-FGRA from the sustainability view point," Journal of Intelligent Manufacturing, Springer, vol. 28(6), pages 1459-1480, August.
    8. Jha, Sunil Kr. & Bilalovic, Jasmin & Jha, Anju & Patel, Nilesh & Zhang, Han, 2017. "Renewable energy: Present research and future scope of Artificial Intelligence," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 297-317.
    9. Bukkarapu, Kiran Raj & Krishnasamy, Anand, 2022. "A critical review on available models to predict engine fuel properties of biodiesel," Renewable and Sustainable Energy Reviews, Elsevier, vol. 155(C).
    10. Kuk Yeol Bae & Han Seung Jang & Bang Chul Jung & Dan Keun Sung, 2019. "Effect of Prediction Error of Machine Learning Schemes on Photovoltaic Power Trading Based on Energy Storage Systems," Energies, MDPI, vol. 12(7), pages 1-20, April.
    11. Javed, Syed & Baig, Rahmath Ulla & Murthy, Y.V.V. Satyanarayana, 2018. "Study on noise in a hydrogen dual-fuelled zinc-oxide nanoparticle blended biodiesel engine and the development of an artificial neural network model," Energy, Elsevier, vol. 160(C), pages 774-782.
    12. 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.
    13. Mohammed I. Jahirul & Richard J. Brown & Wijitha Senadeera & Ian M. O'Hara & Zoran D. Ristovski, 2013. "The Use of Artificial Neural Networks for Identifying Sustainable Biodiesel Feedstocks," Energies, MDPI, vol. 6(8), pages 1-43, July.

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