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Data-driven optimization of pollutant emission and operational efficiency for circulating fluidized bed unit

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  • Yu, Haoyang
  • Gao, Mingming
  • Zhang, Hongfu
  • Yue, Guangxi
  • Zhang, Zhen

Abstract

To achieve the economic and environmentally friendly operation of circulating fluidized bed (CFB) units, it is imperative to conduct optimization to obtain an economical mode of pollutant removal. This article focuses on the multi-objective optimization between SO2-NOx emissions and thermal efficiency for CFB units. According to the operation data and production mechanism of pollutants, models of SO2-NOx emission concentration, bed temperature, and oxygen content based on a convolutional neural network–bidirectional long short-term memory–attention mechanism (CNN-BiLSTM-Attention) were established. Then, an improved quantum genetic algorithm was used to find the optimal input variables of the SO2-NOx emission model. The proposed modeling method was evaluated, and it more accurately simulated the trends of actual operation data than other models under different operating conditions. Combining these models with economic calculations, the operating costs under typical conditions were reduced by 3.20% and 1.82% respectively, and the thermal efficiency increased by 0.72% and 1.07%, which contributes to economical and intelligent operation of the unit.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:energy:v:281:y:2023:i:c:s0360544223017322
    DOI: 10.1016/j.energy.2023.128338
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    References listed on IDEAS

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    1. Lei Han & Lingmei Wang & Hairui Yang & Chengzhen Jia & Enlong Meng & Yushan Liu & Shaoping Yin, 2023. "Optimization of Circulating Fluidized Bed Boiler Combustion Key Control Parameters Based on Machine Learning," Energies, MDPI, vol. 16(15), pages 1-23, July.

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