Artificial neural networks used for the prediction of the cetane number of biodiesel
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DOI: 10.1016/j.renene.2006.01.009
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- 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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- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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).
- 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.
- 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.
- 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.
- 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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Keywords
Cetane number; Biodiesel; Artificial neural networks;All these keywords.
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