Prediction of the cetane number of biodiesel using artificial neural networks and multiple linear regression
- Author
- Ramon Piloto-Rodriguez, Yisel Sanchez-Borroto, Magin Lapuerta, Leonardo Goyos-Perez and Sebastian Verhelst (UGent)
- Organization
- Abstract
- Models for estimation of cetane number of biodiesel from their fatty acid methyl ester composition using multiple linear regression and artificial neural networks were obtained in this work. For the obtaining of models to predict the cetane number, an experimental data from literature reports that covers 48 and 15 biodiesels in the modeling-training step and validation step respectively were taken. Twenty-four neural networks using two topologies and different algorithms for the second training step were evaluated. The model obtained using multiple regression was compared with two other models from literature and it was able to predict cetane number with 89% of accuracy, observing one outlier. A model to predict cetane number using artificial neural network was obtained with better accuracy than 92% except one outlier. The best neural network to predict the cetane number was a backpropagation network (11:5:1) using the Levenberg-Marquardt algorithm for the second step of the networks training and showing R = 0.9544 for the validation data.
- Keywords
- Fatty acid, Biodiesel, Cetane number, Ester composition, Neural network
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Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-3167882
- MLA
- Piloto-Rodriguez, Ramon, et al. “Prediction of the Cetane Number of Biodiesel Using Artificial Neural Networks and Multiple Linear Regression.” ENERGY CONVERSION AND MANAGEMENT, vol. 65, 2013, pp. 255–61, doi:10.1016/j.enconman.2012.07.023.
- APA
- Piloto-Rodriguez, R., Sanchez-Borroto, Y., Lapuerta, M., Goyos-Perez, L., & Verhelst, S. (2013). Prediction of the cetane number of biodiesel using artificial neural networks and multiple linear regression. ENERGY CONVERSION AND MANAGEMENT, 65, 255–261. https://doi.org/10.1016/j.enconman.2012.07.023
- Chicago author-date
- Piloto-Rodriguez, Ramon, Yisel Sanchez-Borroto, Magin Lapuerta, Leonardo Goyos-Perez, and Sebastian Verhelst. 2013. “Prediction of the Cetane Number of Biodiesel Using Artificial Neural Networks and Multiple Linear Regression.” ENERGY CONVERSION AND MANAGEMENT 65: 255–61. https://doi.org/10.1016/j.enconman.2012.07.023.
- Chicago author-date (all authors)
- Piloto-Rodriguez, Ramon, Yisel Sanchez-Borroto, Magin Lapuerta, Leonardo Goyos-Perez, and Sebastian Verhelst. 2013. “Prediction of the Cetane Number of Biodiesel Using Artificial Neural Networks and Multiple Linear Regression.” ENERGY CONVERSION AND MANAGEMENT 65: 255–261. doi:10.1016/j.enconman.2012.07.023.
- Vancouver
- 1.Piloto-Rodriguez R, Sanchez-Borroto Y, Lapuerta M, Goyos-Perez L, Verhelst S. Prediction of the cetane number of biodiesel using artificial neural networks and multiple linear regression. ENERGY CONVERSION AND MANAGEMENT. 2013;65:255–61.
- IEEE
- [1]R. Piloto-Rodriguez, Y. Sanchez-Borroto, M. Lapuerta, L. Goyos-Perez, and S. Verhelst, “Prediction of the cetane number of biodiesel using artificial neural networks and multiple linear regression,” ENERGY CONVERSION AND MANAGEMENT, vol. 65, pp. 255–261, 2013.
@article{3167882,
abstract = {{Models for estimation of cetane number of biodiesel from their fatty acid methyl ester composition using multiple linear regression and artificial neural networks were obtained in this work. For the obtaining of models to predict the cetane number, an experimental data from literature reports that covers 48 and 15 biodiesels in the modeling-training step and validation step respectively were taken. Twenty-four neural networks using two topologies and different algorithms for the second training step were evaluated. The model obtained using multiple regression was compared with two other models from literature and it was able to predict cetane number with 89% of accuracy, observing one outlier. A model to predict cetane number using artificial neural network was obtained with better accuracy than 92% except one outlier. The best neural network to predict the cetane number was a backpropagation network (11:5:1) using the Levenberg-Marquardt algorithm for the second step of the networks training and showing R = 0.9544 for the validation data.}},
author = {{Piloto-Rodriguez, Ramon and Sanchez-Borroto, Yisel and Lapuerta, Magin and Goyos-Perez, Leonardo and Verhelst, Sebastian}},
issn = {{0196-8904}},
journal = {{ENERGY CONVERSION AND MANAGEMENT}},
keywords = {{Fatty acid,Biodiesel,Cetane number,Ester composition,Neural network}},
language = {{eng}},
pages = {{255--261}},
title = {{Prediction of the cetane number of biodiesel using artificial neural networks and multiple linear regression}},
url = {{http://doi.org/10.1016/j.enconman.2012.07.023}},
volume = {{65}},
year = {{2013}},
}
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