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

Han, Zhezhe, Li, Jian, Hossain, Md. Moinul, Qi, Qi, Zhang, Biao, Xu, Chuanlong (2021) An ensemble deep learning model for exhaust emissions prediction of heavy oil-fired boiler combustion. Fuel, 308 . Article Number 121975. ISSN 0016-2361. (In press) (doi:10.1016/j.fuel.2021.121975) (Access to this publication is currently restricted. You may be able to access a copy if URLs are provided) (KAR id:90352)

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https://doi.org/10.1016/j.fuel.2021.121975

Abstract

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

Item Type: Article
DOI/Identification number: 10.1016/j.fuel.2021.121975
Uncontrolled keywords: Emission prediction, Flame image, Ensemble method, Stacked denoising autoencoder, Gaussian process regression
Subjects: Q Science
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Engineering and Digital Arts
Depositing User: Moinul Hossain
Date Deposited: 25 Sep 2021 08:10 UTC
Last Modified: 04 Oct 2021 13:24 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/90352 (The current URI for this page, for reference purposes)
Hossain, Md. Moinul: https://orcid.org/0000-0003-4184-2397
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