Journal
FUEL
Volume 308, Issue -, Pages -Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.fuel.2021.121975
Keywords
Emission prediction; Flame image; Ensemble method; Stacked denoising autoencoder; Gaussian process regression
Categories
Funding
- National Natural Science Foundation of China [51976038]
- Natural Science Foundation of Jiangsu Province, China for Young Scholars [BK20190366]
- China Scholarship Council [202006090164]
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The study introduces a novel ensemble deep learning model for exhaust emissions prediction, which extracts deep features from flame images and combines multiple forecasting engines to achieve accurate final predictions.
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 4.2 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.
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