Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/4682
Title: Experimental and artificial neural network modeling study on soot formation in premixed hydrocarbon flames
Authors: İnal, Fikret
Tayfur, Gökmen
Melton, Tyler R.
Senkan, Selim M.
İnal, Fikret
Tayfur, Gökmen
Izmir Institute of Technology. Chemical Engineering
Izmir Institute of Technology. Civil Engineering
Keywords: Soot
Hydrocarbon flames
Combustion
Light scattering
Artificial neural networks
Issue Date: Aug-2003
Publisher: Elsevier Ltd.
Source: İnal, F., Tayfur, G., Melton, T. R., and Senkan, S. M. (2003). Experimental and artificial neural network modeling study on soot formation in premixed hydrocarbon flames. Fuel, 82(12), 1477-1490. doi:10.1016/S0016-2361(03)00060-7
Abstract: The formation of soot in premixed flames of methane, ethane, propane, and butane was studied at three different equivalence ratios. Soot particle sizes, number densities, and volume fractions were determined using classical light scattering measurement techniques. The experimental data revealed that the soot properties were sensitive to the fuel type and combustion parameter equivalence ratio. Increase in equivalence ratio increased the amount of soot formed for each fuel. In addition, methane flames showed larger particle diameters at higher distances above the burner surface and propane, ethane, and butane flames came after the methane flames, respectively. Three-layer, feed-forward type artificial neural networks having seven input neurons, one output neuron, and five hidden neurons for soot particle diameter predictions and seven hidden neurons for volume fraction predictions were used to model the soot properties. The network could not be trained and tested with sufficient accuracy to predict the number density due to a large data range and greater uncertainty in determination of this parameter. The number of complete data set used in the model was 156. There was a good agreement between the experimental and predicted values, and neural networks performed better when predicting output parameters (i.e. soot particle diameters and volume fractions) within the limits of the training data.
URI: http://doi.org/10.1016/S0016-2361(03)00060-7
http://hdl.handle.net/11147/4682
ISSN: 0016-2361
0016-2361
Appears in Collections:Chemical Engineering / Kimya Mühendisliği
Civil Engineering / İnşaat Mühendisliği
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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