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Burning condition recognition of rotary kiln based on spatiotemporal features of flame video

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  • Chen, Hua
  • Yan, Tingting
  • Zhang, Xiaogang

Abstract

In the coal-fire industry, recognition of burning condition is vital for combustion control and optimization as it can provide early warning of abnormal conditions in the combustion system. We propose an effective model based on spatiotemporal features extracted from flame video for burning condition recognition especially for unsteady state condition. This model extracts features from both the spatial and temporal dimensions based on multiple adjacent frames to capture the appearance and motion information of the flame. Two new three-dimensional (3D) descriptors are developed to characterize the dynamic characteristics of the flame video stream. A computationally simple and fast dynamic texture descriptor, 3DBLBP, is designed to extract flame dynamic texture and motion from three adjacent frames of a video block, and a dynamic structure descriptor, HOPC-TOP is developed by extracting 3D phase congruency information of video clip from three orthogonal planes to capture structure and motion characterizes of flame. The two descriptors are combined to extract spatiotemporal features from flame clip for burning condition recognition. Experiment results show the proposed framework can achieve a high recognition accuracy in the real-world data, thus verifying the effectiveness of our proposed framework for the recognition of burning conditions in a rotary kiln.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:energy:v:211:y:2020:i:c:s0360544220317643
    DOI: 10.1016/j.energy.2020.118656
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    References listed on IDEAS

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    1. 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.
    2. Chen, Junghui & Hsu, Tong-Yang & Chen, Chih-Chien & Cheng, Yi-Cheng, 2010. "Monitoring combustion systems using HMM probabilistic reasoning in dynamic flame images," Applied Energy, Elsevier, vol. 87(7), pages 2169-2179, July.
    3. 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.
    4. Tóth, Pál & Garami, Attila & Csordás, Bernadett, 2017. "Image-based deep neural network prediction of the heat output of a step-grate biomass boiler," Applied Energy, Elsevier, vol. 200(C), pages 155-169.
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