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Deep learning based monitoring of furnace combustion state and measurement of heat release rate

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  • Wang, Zhenyu
  • Song, Chunfeng
  • Chen, Tao

Abstract

Effective and efficient monitoring of furnace combustion state and measurement of heat release rate are important and pressing problems in the power industry. However, traditional methods including image segmentation based methods, feature based methods and shallow classifier based methods cannot meet the requirements of highly accurate. These methods are composed with several separating steps, i.e. feature selection and recognition. This paper proposes a novel deep learning based method to identify furnace combustion state and measure heat release rate. With an end-to-end network, feature extraction and classification are integrated into one framework. The deep learning model takes flame images into a multi-layer DNN (Deep Neural Network) or CNN (Convolutional Neural Network) to predict combustion state and heat release rate simultaneously. We also implement smooth and adjustment techniques which can get a trade-off between stability and sensitivity, ensuring both accurate prediction of burner state and fast detection of unstable combustion. The proposed system achieved state-of-the-art 99.9% accuracy in predicting combustion state with a speed of 1 ms per image. Experimental results show that this method has great potential for practical applications on power plants.

Suggested Citation

  • Wang, Zhenyu & Song, Chunfeng & Chen, Tao, 2017. "Deep learning based monitoring of furnace combustion state and measurement of heat release rate," Energy, Elsevier, vol. 131(C), pages 106-112.
    • Handle: RePEc:eee:energy:v:131:y:2017:i:c:p:106-112
    DOI: 10.1016/j.energy.2017.05.012
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    Citations

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    Cited by:

    1. Chen, Hua & Yan, Tingting & Zhang, Xiaogang, 2020. "Burning condition recognition of rotary kiln based on spatiotemporal features of flame video," Energy, Elsevier, vol. 211(C).
    2. Dong, Zhe & Liu, Miao & Guo, Zhiwu & Huang, Xiaojin & Zhang, Yajun & Zhang, Zuoyi, 2019. "Adaptive state-observer for monitoring flexible nuclear reactors," Energy, Elsevier, vol. 171(C), pages 893-909.
    3. Han, Zhezhe & Hossain, Md. Moinul & Wang, Yuwei & Li, Jian & Xu, Chuanlong, 2020. "Combustion stability monitoring through flame imaging and stacked sparse autoencoder based deep neural network," Applied Energy, Elsevier, vol. 259(C).
    4. Tian Qiu & Minjian Liu & Guiping Zhou & Li Wang & Kai Gao, 2019. "An Unsupervised Classification Method for Flame Image of Pulverized Coal Combustion Based on Convolutional Auto-Encoder and Hidden Markov Model," Energies, MDPI, vol. 12(13), pages 1-17, July.
    5. Yuteng Xiao & Jihang Yin & Yifan Hu & Junzhe Wang & Hongsheng Yin & Honggang Qi, 2019. "Monitoring and Control in Underground Coal Gasification: Current Research Status and Future Perspective," Sustainability, MDPI, vol. 11(1), pages 1-14, January.
    6. Jiang, Feifeng & Ma, Jun & Li, Zheng & Ding, Yuexiong, 2022. "Prediction of energy use intensity of urban buildings using the semi-supervised deep learning model," Energy, Elsevier, vol. 249(C).

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