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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Veröffentlicht in:	Applied energy Jg. 310; S. 118502
Hauptverfasser:	Roy, Rishi, Gupta, Ashwani K.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Ltd 15.03.2022
Schlagworte:	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
Online-Zugang:	Volltext
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