IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v182y2019icp37-47.html
   My bibliography  Save this article

The multi-objective optimization of combustion system operations based on deep data-driven models

Author

Listed:
  • Tang, Zhenhao
  • Zhang, Zijun

Abstract

Advancing methods for modeling combustion systems and optimizing their operations is beneficial to improve the combustion performance. This paper develops a deep data-driven framework for the optimization of combustion system operations. First, a deep belief network based method is developed to model both of the combustion efficiency and the NOx emission. Next, a multi-objective optimization model is developed by integrating the deep belief network based models, the considered operational constraints, and the control variable constraints. Two objectives, maximizing the combustion efficiency and minimizing the NOx emission, are considered in the optimization. Due to the nonlinearity and complexity of the optimization model, traditional exact solution methods are not applicable to solve it. A recently presented swarm intelligence method, the JAYA algorithm, is applied to obtain the optimal solutions of the developed optimization model. Advantages of using JAYA are proved by benchmarking against well-known computational intelligence methods. The feasibility and effectiveness of the developed framework for optimizing the combustion process using industrial data is validated by computational experiments. Results demonstrate the potential of further improving both of the combustion efficiency and NOx emission by optimizing control settings of the combustion system.

Suggested Citation

  • Tang, Zhenhao & Zhang, Zijun, 2019. "The multi-objective optimization of combustion system operations based on deep data-driven models," Energy, Elsevier, vol. 182(C), pages 37-47.
    • Handle: RePEc:eee:energy:v:182:y:2019:i:c:p:37-47
    DOI: 10.1016/j.energy.2019.06.051
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544219311740
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2019.06.051?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.

    Citations

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


    Cited by:

    1. Li, Shicheng & Ma, Suxia & Wang, Fang, 2023. "A combined NOx emission prediction model based on semi-empirical model and black box models," Energy, Elsevier, vol. 264(C).
    2. Tang, Zhenhao & Zhao, Gengnan & Ouyang, Tinghui, 2021. "Two-phase deep learning model for short-term wind direction forecasting," Renewable Energy, Elsevier, vol. 173(C), pages 1005-1016.
    3. Yin, Linfei & Wang, Tao & Zheng, Baomin, 2021. "Analytical adaptive distributed multi-objective optimization algorithm for optimal power flow problems," Energy, Elsevier, vol. 216(C).
    4. Tuttle, Jacob F. & Blackburn, Landen D. & Andersson, Klas & Powell, Kody M., 2021. "A systematic comparison of machine learning methods for modeling of dynamic processes applied to combustion emission rate modeling," Applied Energy, Elsevier, vol. 292(C).
    5. Zhou, Dengji & Jia, Xingyun & Ma, Shixi & Shao, Tiemin & Huang, Dawen & Hao, Jiarui & Li, Taotao, 2022. "Dynamic simulation of natural gas pipeline network based on interpretable machine learning model," Energy, Elsevier, vol. 253(C).
    6. Li, Xinli & Wang, Yingnan & Zhu, Yun & Yang, Guotian & Liu, He, 2021. "Temperature prediction of combustion level of ultra-supercritical unit through data mining and modelling," Energy, Elsevier, vol. 231(C).
    7. Xiao, Guolin & Gao, Xiaori & Lu, Wei & Liu, Xiaodong & Asghar, Aamer Bilal & Jiang, Liu & Jing, Wenlin, 2023. "A physically based air proportioning methodology for optimized combustion in gas-fired boilers considering both heat release and NOx emissions," Applied Energy, Elsevier, vol. 350(C).
    8. Xu, Wentao & Huang, Yaji & Song, Siheng & Yue, Junfeng & Chen, Bo & Liu, Yuqing & Zou, Yiran, 2023. "A new on-line combustion optimization approach for ultra-supercritical coal-fired boiler to improve boiler efficiency, reduce NOx emission and enhance operating safety," Energy, Elsevier, vol. 282(C).
    9. Tang, Zhenhao & Wang, Shikui & Chai, Xiangying & Cao, Shengxian & Ouyang, Tinghui & Li, Yang, 2022. "Auto-encoder-extreme learning machine model for boiler NOx emission concentration prediction," Energy, Elsevier, vol. 256(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:energy:v:182:y:2019:i:c:p:37-47. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.journals.elsevier.com/energy .

    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.