Data-driven prediction of flame temperature and pollutant emission in distributed combustion

•Machine learning modeling was investigated in lean distributed combustion regime.•Flame temperature and pollutants were predicted using artificial neural network.•Effect of different learning rate on model training was studied.•Current model showed very well prediction in swirl combustion and distr...

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Vydáno v:	Applied energy Ročník 310; s. 118502
Hlavní autoři:	Roy, Rishi, Gupta, Ashwani K.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier Ltd 15.03.2022
Témata:	air flow algorithms Artificial neural network Bayesian theory carbon dioxide combustion data collection Distributed combustion Emission and flame temperature prediction energy Learning rate methane neural networks pollutants prediction Swirl burner temperature Artificial neural network Emission and flame temperature prediction Swirl burner Learning rate Distributed combustion
ISSN:	0306-2619, 1872-9118
On-line přístup:	Získat plný text
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