An ensemble deep learning model for exhaust emissions prediction of heavy oil-fired boiler combustion
نویسندگان
چکیده
Accurate and reliable prediction of exhaust emissions is crucial for combustion optimization control environmental protection. This study proposes a novel ensemble deep learning model (NOx CO2) prediction. In this model, the stacked denoising autoencoder established to extract features flame images. Four forecasting engines include artificial neural network, extreme machine, support vector machine least squares are employed preliminary NOx CO2 based on extracted image features. After that, these predictions combined by Gaussian process regression in nonlinear manner achieve final emissions. The effectiveness proposed evaluated through 4.2 MW heavy oil-fired boiler Experimental results suggest that achieved from four inconsistent, however, an accurate accuracy has been model. not only provides point but also generates satisfactory confidence interval.
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ژورنال
عنوان ژورنال: Fuel
سال: 2022
ISSN: ['0016-2361', '1873-7153']
DOI: https://doi.org/10.1016/j.fuel.2021.121975