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作者:

Pan, Xiaotong (Pan, Xiaotong.) | Tang, Jian (Tang, Jian.) | Xia, Heng (Xia, Heng.) | Li, Weitao (Li, Weitao.) | Guo, Haitao (Guo, Haitao.)

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EI Scopus

摘要:

It is important to accurately identify the combustion state of the municipal solid waste incineration (MSWI) processes. Stable state not only can greatly improve the combustion efficiency, but also can ensure safety of the MSWI processes. What’s more, the pollution emission concentration would be greatly reduced. Aiming at the situation that domain experts identify the combustion state in terms of self-experience in the actual MSWI processes, this study proposes an efficient method based on improved deep forest (IDF). First, the image preprocessing methods such as defogging and denoising, were used to preprocess the combustion flame image to obtain a clear one. Then, the multi-source features (brightness, flame and color) were extracted. Finally, the multi-source features were used as the input of cascade forest module in terms of substituting multi-grained scanning module. Therefore, a combustion state recognition model of MSWI processes based on IDF was established. Based on actual flame images of industrial processes, many experiments has been done. The results showed that the constructed model can reach a recognition accuracy of 95.28%. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词:

Waste incineration Forestry Municipal solid waste State estimation Image processing

作者机构:

  • [ 1 ] [Pan, Xiaotong]Faculty of Information Technology, Beijing University of Technology, Beijing; 100124, China
  • [ 2 ] [Pan, Xiaotong]Beijing Laboratory of Smart Environmental Protection, Beijing; 100124, China
  • [ 3 ] [Tang, Jian]Faculty of Information Technology, Beijing University of Technology, Beijing; 100124, China
  • [ 4 ] [Tang, Jian]Beijing Laboratory of Smart Environmental Protection, Beijing; 100124, China
  • [ 5 ] [Xia, Heng]Faculty of Information Technology, Beijing University of Technology, Beijing; 100124, China
  • [ 6 ] [Xia, Heng]Beijing Laboratory of Smart Environmental Protection, Beijing; 100124, China
  • [ 7 ] [Li, Weitao]School of Electrical Engineering and Automation, Hefei University of Technology, Anhui Province; 230009, China
  • [ 8 ] [Guo, Haitao]Faculty of Information Technology, Beijing University of Technology, Beijing; 100124, China
  • [ 9 ] [Guo, Haitao]Beijing Laboratory of Smart Environmental Protection, Beijing; 100124, China

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ISSN: 1865-0929

年份: 2022

卷: 1637 CCIS

页码: 71-84

语种: 英文

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SCOPUS被引频次: 5

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