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Gaussian process regression based optimal design of combustion systems using flame images

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  • Chen, Junghui
  • Chan, Lester Lik Teck
  • Cheng, Yi-Cheng

Abstract

With the advanced methods of digital image processing and optical sensing, it is possible to have continuous imaging carried out on-line in combustion processes. In this paper, a method that extracts characteristics from the flame images is presented to immediately predict the outlet content of the flue gas. First, from the large number of flame image data, principal component analysis is used to discover the principal components or combinational variables, which describe the important trends and variations in the operation data. Then stochastic modeling of the combustion process is done by a Gaussian process with the aim to capture the stochastic nature of the flame associated with the oxygen content. The designed oxygen combustion content considers the uncertainty presented in the combustion. A reference image can be designed for the actual combustion process to provide an easy and straightforward maintenance of the combustion process.

Suggested Citation

  • Chen, Junghui & Chan, Lester Lik Teck & Cheng, Yi-Cheng, 2013. "Gaussian process regression based optimal design of combustion systems using flame images," Applied Energy, Elsevier, vol. 111(C), pages 153-160.
    • Handle: RePEc:eee:appene:v:111:y:2013:i:c:p:153-160
    DOI: 10.1016/j.apenergy.2013.04.036
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    References listed on IDEAS

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    1. Chen, Junghui & Chang, Yu-Hsiang & Cheng, Yi-Cheng & Hsu, Chen-Kai, 2012. "Design of image-based control loops for industrial combustion processes," Applied Energy, Elsevier, vol. 94(C), pages 13-21.
    2. Yan, Zhuoyong & Liang, Qinfeng & Guo, Qinghua & Yu, Guangsuo & Yu, Zunhong, 2009. "Experimental investigations on temperature distributions of flame sections in a bench-scale opposed multi-burner gasifier," Applied Energy, Elsevier, vol. 86(7-8), pages 1359-1364, July.
    3. González-Cencerrado, A. & Peña, B. & Gil, A., 2012. "Coal flame characterization by means of digital image processing in a semi-industrial scale PF swirl burner," Applied Energy, Elsevier, vol. 94(C), pages 375-384.
    4. Draper, Teri Snow & Zeltner, Darrel & Tree, Dale R. & Xue, Yuan & Tsiava, Remi, 2012. "Two-dimensional flame temperature and emissivity measurements of pulverized oxy-coal flames," Applied Energy, Elsevier, vol. 95(C), pages 38-44.
    5. Hwang, Cheol-Hong & Lee, Seungro & Kim, Jong-Hyun & Lee, Chang-Eon, 2009. "An experimental study on flame stability and pollutant emission in a cyclone jet hybrid combustor," Applied Energy, Elsevier, vol. 86(7-8), pages 1154-1161, July.
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    Cited by:

    1. 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).
    2. Ruiyuan Kang & Panos Liatsis & Dimitrios C. Kyritsis, 2022. "Emission Quantification via Passive Infrared Optical Gas Imaging: A Review," Energies, MDPI, vol. 15(9), pages 1-32, April.
    3. Han, Zhezhe & Tang, Xiaoyu & Xie, Yue & Liang, Ruiyu & Bao, Yongqiang, 2024. "Prediction of heavy-oil combustion emissions with a semi-supervised learning model considering variable operation conditions," Energy, Elsevier, vol. 288(C).
    4. Yuansheng Huang & Lei Yang & Chong Gao & Yuqing Jiang & Yulin Dong, 2019. "A Novel Prediction Approach for Short-Term Renewable Energy Consumption in China Based on Improved Gaussian Process Regression," Energies, MDPI, vol. 12(21), pages 1-17, November.
    5. Ögren, Yngve & Tóth, Pál & Garami, Attila & Sepman, Alexey & Wiinikka, Henrik, 2018. "Development of a vision-based soft sensor for estimating equivalence ratio and major species concentration in entrained flow biomass gasification reactors," Applied Energy, Elsevier, vol. 226(C), pages 450-460.

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