IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v111y2013icp153-160.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261913003310
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2013.04.036?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. 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).
    3. 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.
    4. 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.
    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhou, Dongdong & Cheng, Shusen, 2019. "Measurement study of the PCI process on the temperature distribution in raceway zone of blast furnace by using digital imaging techniques," Energy, Elsevier, vol. 174(C), pages 814-822.
    2. 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.
    3. 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.
    4. 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.
    5. Peña, B. & Pallarés, J. & Bartolomé, C. & Herce, C., 2018. "Experimental study on the effects of co-firing coal mine waste residues with coal in PF swirl burners," Energy, Elsevier, vol. 157(C), pages 45-53.
    6. Lu, Hantao & Gong, Yan & Guo, Qinghua & Wang, Yue & Song, Xudong & Yu, Guangsuo, 2024. "In-situ study on flow and rotation behaviors of coal particles near the burner plane in an impinging entrained-flow gasifier," Applied Energy, Elsevier, vol. 359(C).
    7. Ren, Tao & Modest, Michael F. & Fateev, Alexander & Sutton, Gavin & Zhao, Weijie & Rusu, Florin, 2019. "Machine learning applied to retrieval of temperature and concentration distributions from infrared emission measurements," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    8. Pallarés, Javier & Herce, Carlos & Bartolomé, Carmen & Peña, Begoña, 2017. "Investigation on co-firing of coal mine waste residues in pulverized coal combustion systems," Energy, Elsevier, vol. 140(P1), pages 58-68.
    9. Pourhoseini, S.H., 2017. "A novel configuration of natural gas diffusion burners to enhance optical, thermal and radiative characteristics of flame and reduce NOx emission," Energy, Elsevier, vol. 132(C), pages 41-48.
    10. Weijie Yan & Dongmei Chen & Zuomei Yang & Enyu Yan & Peitao Zhao, 2017. "Measurement of Soot Volume Fraction and Temperature for Oxygen-Enriched Ethylene Combustion Based on Flame Image Processing," Energies, MDPI, vol. 10(6), pages 1-16, May.
    11. Zhongya Xi & Zhongguang Fu & Syed Waqas Sabir & Xiaotian Hu & Yibo Jiang & Tao Zhang, 2018. "Experimental Analysis on Flame Flickering of a Swirl Partially Premixed Combustion," Energies, MDPI, vol. 11(9), pages 1-14, September.
    12. Zhu, Shujun & Hui, Jicheng & Lyu, Qinggang & Ouyang, Ziqu & Zeng, Xiongwei & Zhu, Jianguo & Liu, Jingzhang & Cao, Xiaoyang & Zhang, Xiaoyu & Ding, Hongliang & Liu, Yuhua, 2023. "Experimental study on pulverized coal swirl-opposed combustion preheated by a circulating fluidized bed. Part A. Wide-load operation and low-NOx emission characteristics," Energy, Elsevier, vol. 284(C).
    13. Gong, Yan & Zhang, Qing & Zhu, Huiwen & Guo, Qinghua & Yu, Guangsuo, 2017. "Refractory failure in entrained-flow gasifier: Vision-based macrostructure investigation in a bench-scale OMB gasifier," Applied Energy, Elsevier, vol. 205(C), pages 1091-1099.
    14. Pourhoseini, S.H., 2020. "Enhancement of radiation characteristics and reduction of NOx emission in natural gas flame through silver-water nanofluid injection," Energy, Elsevier, vol. 194(C).
    15. Gong, Yan & Zhang, Qing & Guo, Qinghua & Xue, Zhicun & Wang, Fuchen & Yu, Guangsuo, 2017. "Vision-based investigation on the ash/slag particle deposition characteristics in an impinging entrained-flow gasifier," Applied Energy, Elsevier, vol. 206(C), pages 1184-1193.
    16. Weijie Yan & Yunqi Ya & Feng Du & Hao Shao & Peitao Zhao, 2017. "Spectrometer-Based Line-of-Sight Temperature Measurements during Alkali-Pulverized Coal Combustion in a Power Station Boiler," Energies, MDPI, vol. 10(9), pages 1-14, September.
    17. Zhang, Jing-hao & Bi, Ming-shu & Du, Dan & Hao, Qiang-qiang & Yu, Di & Wang, Yuan & Ren, Jing-jie, 2024. "Composite combustion behaviors of tubular flame and central jet flame in a reduced-diameter vortex combustor," Energy, Elsevier, vol. 302(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:appene:v:111:y:2013:i:c:p:153-160. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.