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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. Zhang, Hongfu & Gao, Mingming & Fan, Haohao & Zhang, Kaiping & Zhang, Jiahui, 2022. "A dynamic model for supercritical once-through circulating fluidized bed boiler-turbine units," Energy, Elsevier, vol. 241(C).
    2. Wang, Yun & Xu, Houhua & Zou, Runmin & Zhang, Lingjun & Zhang, Fan, 2022. "A deep asymmetric Laplace neural network for deterministic and probabilistic wind power forecasting," Renewable Energy, Elsevier, vol. 196(C), pages 497-517.
    3. Xie, Peiran & Gao, Mingming & Zhang, Hongfu & Niu, Yuguang & Wang, Xiaowen, 2020. "Dynamic modeling for NOx emission sequence prediction of SCR system outlet based on sequence to sequence long short-term memory network," Energy, Elsevier, vol. 190(C).
    4. 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.
    5. Jaroslaw Krzywanski & Tomasz Czakiert & Anna Zylka & Wojciech Nowak & Marcin Sosnowski & Karolina Grabowska & Dorian Skrobek & Karol Sztekler & Anna Kulakowska & Waqar Muhammad Ashraf & Yunfei Gao, 2022. "Modelling of SO 2 and NO x Emissions from Coal and Biomass Combustion in Air-Firing, Oxyfuel, iG-CLC, and CLOU Conditions by Fuzzy Logic Approach," Energies, MDPI, vol. 15(21), pages 1-17, October.
    6. Lv, You & Hong, Feng & Yang, Tingting & Fang, Fang & Liu, Jizhen, 2017. "A dynamic model for the bed temperature prediction of circulating fluidized bed boilers based on least squares support vector machine with real operational data," Energy, Elsevier, vol. 124(C), pages 284-294.
    7. Zhang, Chu & Ma, Huixin & Hua, Lei & Sun, Wei & Nazir, Muhammad Shahzad & Peng, Tian, 2022. "An evolutionary deep learning model based on TVFEMD, improved sine cosine algorithm, CNN and BiLSTM for wind speed prediction," Energy, Elsevier, vol. 254(PA).
    8. Tomasz Czakiert & Jaroslaw Krzywanski & Anna Zylka & Wojciech Nowak, 2022. "Chemical Looping Combustion: A Brief Overview," Energies, MDPI, vol. 15(4), pages 1-19, February.
    9. Kang, Ligai & Yuan, Xiaoxue & Sun, Kangjie & Zhang, Xu & Zhao, Jun & Deng, Shuai & Liu, Wei & Wang, Yongzhen, 2022. "Feed-forward active operation optimization for CCHP system considering thermal load forecasting," Energy, Elsevier, vol. 254(PB).
    10. Shijun Wang & Chun Liu & Kui Liang & Ziyun Cheng & Xue Kong & Shuang Gao, 2022. "Wind Speed Prediction Model Based on Improved VMD and Sudden Change of Wind Speed," Sustainability, MDPI, vol. 14(14), pages 1-15, July.
    11. Krzywanski, J. & Czakiert, T. & Nowak, W. & Shimizu, T. & Zylka, A. & Idziak, K. & Sosnowski, M. & Grabowska, K., 2022. "Gaseous emissions from advanced CLC and oxyfuel fluidized bed combustion of coal and biomass in a complex geometry facility:A comprehensive model," Energy, Elsevier, vol. 251(C).
    12. Yuansheng, Huang & Mengshu, Shi, 2021. "What are the environmental advantages of circulating fluidized bed technology? ——A case study in China," Energy, Elsevier, vol. 220(C).
    13. Hong, Feng & Long, Dongteng & Chen, Jiyu & Gao, Mingming, 2020. "Modeling for the bed temperature 2D-interval prediction of CFB boilers based on long-short term memory network," Energy, Elsevier, vol. 194(C).
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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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