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Multi-objective combustion optimization based on data-driven hybrid strategy

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Listed:
  • Zheng, Wei
  • Wang, Chao
  • Yang, Yajun
  • Zhang, Yongfei

Abstract

In order to reduce pollutant discharge and improve boiler efficiency, data-driven hybrid strategy is proposed to solve the problem of multi-objective combustion optimization. First, massive historical operation data of a coal-fired power station are preprocessed (including resampling, steady-state detection, data cleaning and cluster analysis), so as to divide the whole boiler working condition into different partitions. Next, combustion association rules based multi-objective optimal strategy is applied to extract a combustion optimal rule from every partition, and a combustion optimal rule-base is built up by merging all the rules, so that the preliminary combustion optimization can be quickly finished based on the combustion optimal rule-base. Meanwhile, combustion mathematical model based multi-objective optimal strategy is applied to develop the LSSVR (least square support vector regression) model of boiler combustion process for every partition, and a combustion optimal model-base, which contains all the LSSVR models, is built up. After that, an improved multi-objective particle swarm optimization algorithm is presented to calculate Pareto optimal solutions depend on the corresponding LSSVR model with the constraint of real-time boiler working condition. To achieve further combustion optimization, the method of multiple attribute decision making is used to determine the unique solution from all the Pareto optimal solutions. Data-driven hybrid strategy is to combine the above two strategies. Simulation experiments verified the validity and feasibility of data-driven hybrid strategy on multi-objective test function ZDT1. Taking advantage of data-driven hybrid strategy, the comprehensive average of NOx emissions dropped by 29.63% and the comprehensive average of boiler efficiency increased by 0.69% in the application experiments with historical operation data under some boiler working conditions. Data-driven hybrid strategy based multi-objective combustion optimization makes the integration of instantaneity and effectiveness, so that it is suitable for online application.

Suggested Citation

  • Zheng, Wei & Wang, Chao & Yang, Yajun & Zhang, Yongfei, 2020. "Multi-objective combustion optimization based on data-driven hybrid strategy," Energy, Elsevier, vol. 191(C).
    • Handle: RePEc:eee:energy:v:191:y:2020:i:c:s0360544219321735
    DOI: 10.1016/j.energy.2019.116478
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    References listed on IDEAS

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    1. Lv, You & Liu, Jizhen & Yang, Tingting & Zeng, Deliang, 2013. "A novel least squares support vector machine ensemble model for NOx emission prediction of a coal-fired boiler," Energy, Elsevier, vol. 55(C), pages 319-329.
    2. Smrekar, J. & Potočnik, P. & Senegačnik, A., 2013. "Multi-step-ahead prediction of NOx emissions for a coal-based boiler," Applied Energy, Elsevier, vol. 106(C), pages 89-99.
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    Citations

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    Cited by:

    1. Wang, Zhi & Peng, Xianyong & Zhou, Huaichun & Cao, Shengxian & Huang, Wenbo & Yan, Weijie & Li, Kuangyu & Fan, Siyuan, 2024. "A dynamic modeling method using channel-selection convolutional neural network: A case study of NOx emission," Energy, Elsevier, vol. 290(C).
    2. Tang, Zhenhao & Wang, Shikui & Li, Yue, 2024. "Dynamic NOX emission concentration prediction based on the combined feature selection algorithm and deep neural network," Energy, Elsevier, vol. 292(C).
    3. Chuanpeng Zhu & Pu Huang & Yiguo Li, 2022. "Closed-Loop Combustion Optimization Based on Dynamic and Adaptive Models with Application to a Coal-Fired Boiler," Energies, MDPI, vol. 15(14), pages 1-16, July.
    4. Wu, Yixi & Wang, Ziqi & Shi, Chenli & Jin, Xiaohang & Xu, Zhengguo, 2024. "A novel data-driven approach for coal-fired boiler under deep peak shaving to predict and optimize NOx emission and heat exchange performance," Energy, Elsevier, vol. 304(C).
    5. Zhu, Yukun & Yu, Cong & Fan, Wei & Yu, Haiquan & Jin, Wei & Chen, Shuo & Liu, Xia, 2023. "A novel NOx emission prediction model for multimodal operational utility boilers considering local features and prior knowledge," Energy, Elsevier, vol. 280(C).

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