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- aggregation classification "A1".
- aggregation creator B510901.
- aggregation creator B510902.
- aggregation creator B510903.
- aggregation creator B510904.
- aggregation creator person.
- aggregation date "2013".
- aggregation format "application/pdf".
- aggregation hasFormat 3167882.bibtex.
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- aggregation hasFormat 3167882.doc.
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- aggregation isPartOf urn:issn:0196-8904.
- aggregation language "eng".
- aggregation rights "I have transferred the copyright for this publication to the publisher".
- aggregation subject "Technology and Engineering".
- aggregation title "Prediction of the cetane number of biodiesel using artificial neural networks and multiple linear regression".
- aggregation 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.".
- aggregation authorList BK856644.
- aggregation endPage "261".
- aggregation startPage "255".
- aggregation volume "65".
- aggregation aggregates 3167886.
- aggregation isDescribedBy 3167882.
- aggregation similarTo j.enconman.2012.07.023.
- aggregation similarTo LU-3167882.