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Improved coal combustion optimization model based on load balance and coal qualities

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  • Li, Qingwei
  • Yao, Guihuan

Abstract

Combustion optimization by fine tuning combustion parameters of boilers can cut down NOx emissions effectively with little cost. Coal qualities, which change from time to time, have great influences on NOx emissions. However, the current NOx reduction optimization model cannot handle with this problem well. What is more, the output load would deviate from the demand load as the boiler efficiency is also affected by the optimized manipulated variables (MVs). In this paper, an on-line method to calculate coal qualities based on reverse balance thermal efficiency model was integrated into the optimization model. Furthermore, a new constraint was added to the optimization model to meet the demand load. NOx emission characteristics of some 600 MW capacity utility boiler were investigated. Fine selected MVs were taken as the inputs of support vector machines (SVM) and NOx emission was taken as the output, respectively. Parameters of SVM were fine tuned by particle swarm optimization algorithm (PSO). Combustion optimization for the studied boiler was undertaken based on the proposed optimization model. Results showed that the new model can provide lower NOx emissions and meet demand loads at the same time.

Suggested Citation

  • Li, Qingwei & Yao, Guihuan, 2017. "Improved coal combustion optimization model based on load balance and coal qualities," Energy, Elsevier, vol. 132(C), pages 204-212.
    • Handle: RePEc:eee:energy:v:132:y:2017:i:c:p:204-212
    DOI: 10.1016/j.energy.2017.05.068
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    References listed on IDEAS

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    1. Wei, Zhongbao & Li, Xiaolu & Xu, Lijun & Cheng, Yanting, 2013. "Comparative study of computational intelligence approaches for NOx reduction of coal-fired boiler," Energy, Elsevier, vol. 55(C), pages 683-692.
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    Cited by:

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    5. 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.
    6. Zhu, Hengyi & Tan, Peng & He, Ziqian & Ma, Lun & Zhang, Cheng & Fang, Qingyan & Chen, Gang, 2023. "Revealing steam temperature characteristics for a double-reheat unit under coal calorific value variation," Energy, Elsevier, vol. 283(C).
    7. Mollo, Malebo & Kolesnikov, Andrei & Makgato, Seshibe, 2022. "Simultaneous reduction of NOx emission and SOx emission aided by improved efficiency of a Once-Through Benson Type Coal Boiler," Energy, Elsevier, vol. 248(C).
    8. Yu, Haoyang & Gao, Mingming & Zhang, Hongfu & Yue, Guangxi & Zhang, Zhen, 2023. "Data-driven optimization of pollutant emission and operational efficiency for circulating fluidized bed unit," Energy, Elsevier, vol. 281(C).
    9. Yang, Guotian & Wang, Yingnan & Li, Xinli, 2020. "Prediction of the NOx emissions from thermal power plant using long-short term memory neural network," Energy, Elsevier, vol. 192(C).
    10. 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).
    11. Bartnicki, Grzegorz & Klimczak, Marcin & Ziembicki, Piotr, 2023. "Evaluation of the effects of optimization of gas boiler burner control by means of an innovative method of Fuel Input Factor," Energy, Elsevier, vol. 263(PD).

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