An ensemble deep learning model for exhaust emissions prediction of heavy oil-fired boiler combustion

•An ensemble deep learning model is established for predicting NOx and CO2 emissions.•Effects of image features and feature forecasters on the prediction performance are investigated.•Different machine learning engines are combined in a nonlinear way.•The model is evaluated through heavy oil-fired b...

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Vydáno v:	Fuel (Guildford) Ročník 308; s. 121975
Hlavní autoři:	Han, Zhezhe, Li, Jian, Hossain, Md. Moinul, Qi, Qi, Zhang, Biao, Xu, Chuanlong
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Kidlington Elsevier Ltd 15.01.2022 Elsevier BV
Témata:	Artificial neural networks Boilers Carbon dioxide Carbon dioxide emissions Combustion Confidence intervals Deep learning Emission prediction Emissions Engines Ensemble method Environmental protection Exhaust emissions Feature extraction Flame image Forecasting Gaussian process Gaussian process regression Learning algorithms Machine learning Mathematical models Neural networks Nitrogen oxides Optimization Predictions Stacked denoising autoencoder Statistical analysis Support vector machines Gaussian process regression Ensemble method Flame image Stacked denoising autoencoder Emission prediction
ISSN:	0016-2361, 1873-7153
On-line přístup:	Získat plný text
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