期刊
COMBUSTION SCIENCE AND TECHNOLOGY
卷 188, 期 2, 页码 233-246出版社
TAYLOR & FRANCIS INC
DOI: 10.1080/00102202.2015.1102905
关键词
Biomass; deep learning; de-noising auto-encoder; flame radical imaging; image processing; NOx emission
资金
- Chinese Ministry of Science and Technology (MOST)
- Chinese Ministry of Education, 973 Project [2012CB215203]
- 111 Talent Introduction Projects at North China Electric Power University [B13009]
- EPSRC [EP/G063214/1] Funding Source: UKRI
- Engineering and Physical Sciences Research Council [EP/G063214/1] Funding Source: researchfish
This article presents a methodology for predicting NOx emissions from a biomass combustion process through flame radical imaging and deep learning (DL). The dataset was established experimentally from flame radical images captured on a biomass-gas fired test rig. Morphological component analysis is undertaken to improve the quality of the dataset, and the region-of-interest extraction is introduced to extract the flame radical part and rescale the image size. The developed DL-based prediction model contains three successive stages for implementing the feature extraction, feature fusion, and emission prediction. The fine-tuning based on the prediction is introduced to adjust the process of the feature fusion. The effects of the feature fusion and fine-tuning are discussed in detail. A comparison between various image- and machine-learning-based prediction models show that the proposed DL prediction model outperforms other models in terms of root mean square error criteria. The predicted NOx emissions are in good agreement with the measurement results.
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