IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v281y2023ics0360544223017322.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544223017322
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.energy.2023.128338?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.

    References listed on IDEAS

    as
    1. 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.
    2. 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).
    3. 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.
    4. 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.
    5. 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).
    6. 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).
    7. 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.
    8. 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.
    9. 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).
    10. Tomasz Czakiert & Jaroslaw Krzywanski & Anna Zylka & Wojciech Nowak, 2022. "Chemical Looping Combustion: A Brief Overview," Energies, MDPI, vol. 15(4), pages 1-19, February.
    11. 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).
    12. 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).
    13. 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).
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    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. Jia, Xiongjie & Sang, Yichen & Li, Yanjun & Du, Wei & Zhang, Guolei, 2022. "Short-term forecasting for supercharged boiler safety performance based on advanced data-driven modelling framework," Energy, Elsevier, vol. 239(PE).
    3. 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).
    4. Ma, Liyang & Zhang, Lan & Wang, Deming & Xin, Haihui & Ma, Qiulin, 2023. "Effect of oxygen-supply on the reburning reactivity of pyrolyzed residual from sub-bituminous coal: A reactive force field molecular dynamics simulation," Energy, Elsevier, vol. 283(C).
    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. Güleç, Fatih & Okolie, Jude A. & Erdogan, Ahmet, 2023. "Techno-economic feasibility of fluid catalytic cracking unit integrated chemical looping combustion – A novel approach for CO2 capture," Energy, Elsevier, vol. 284(C).
    7. Muhammad Usman & Muhammad Ali Ijaz Malik & Rehmat Bashir & Fahid Riaz & Muhammad Juniad Raza & Khubaib Suleman & Abd-ul Rehman & Waqar Muhammad Ashraf & Jaroslaw Krzywanski, 2022. "Enviro-Economic Assessment of HHO–CNG Mixture Utilization in Spark Ignition Engine for Performance and Environmental Sustainability," Energies, MDPI, vol. 15(21), pages 1-15, November.
    8. Kijo-Kleczkowska, Agnieszka & Gnatowski, Adam & Krzywanski, Jaroslaw & Gajek, Marcin & Szumera, Magdalena & Tora, Barbara & Kogut, Krzysztof & Knaś, Krzysztof, 2024. "Experimental research and prediction of heat generation during plastics, coal and biomass waste combustion using thermal analysis methods," Energy, Elsevier, vol. 290(C).
    9. Lv, You & Lv, Xuguang & Fang, Fang & Yang, Tingting & Romero, Carlos E., 2020. "Adaptive selective catalytic reduction model development using typical operating data in coal-fired power plants," Energy, Elsevier, vol. 192(C).
    10. Feng, Xiangdong & Liu, Shanjian & Yue, Kang & Wei, Heng & Bi, Dongmei & Zhao, Wenjing, 2023. "Insight into the promotional effect of Mn-modified nitrogenous biochar on the NH3-SCR denitrification activity at low temperatures," Energy, Elsevier, vol. 285(C).
    11. Xiaoliang Yu & Jin Yan & Rongyue Sun & Lin Mei & Yanmin Li & Shuyuan Wang & Fan Wang & Yicheng Gu, 2023. "An Experimental Study on SO 2 Emission and Ash Deposition Characteristics of High Alkali Red Mud under Large Proportional Co-Combustion Conditions in Fluidized Bed," Energies, MDPI, vol. 16(6), pages 1-17, March.
    12. 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).
    13. 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.
    14. Yang, Jie & Dong, Senlin & Xie, Longgui & Cen, Qihong & Zheng, Dalong & Ma, Liping & Dai, Quxiu, 2023. "Analysis of hydrogen-rich syngas generation in chemical looping gasification of lignite: Application of carbide slag as the oxygen carrier, hydrogen carrier, and in-situ carbon capture agent," Energy, Elsevier, vol. 283(C).
    15. Meng, Anbo & Zhang, Haitao & Yin, Hao & Xian, Zikang & Chen, Shu & Zhu, Zibin & Zhang, Zheng & Rong, Jiayu & Li, Chen & Wang, Chenen & Wu, Zhenbo & Deng, Weisi & Luo, Jianqiang & Wang, Xiaolin, 2023. "A novel multi-gradient evolutionary deep learning approach for few-shot wind power prediction using time-series GAN," Energy, Elsevier, vol. 283(C).
    16. 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).
    17. Xie, Xinyu & Wang, Xiaofang & Zhao, Pu & Hao, Yichen & Xie, Rong & Liu, Haitao, 2023. "Learning time-aware multi-phase flow fields in coal-supercritical water fluidized bed reactor with deep learning," Energy, Elsevier, vol. 263(PD).
    18. Lv, Sheng-Xiang & Wang, Lin, 2023. "Multivariate wind speed forecasting based on multi-objective feature selection approach and hybrid deep learning model," Energy, Elsevier, vol. 263(PE).
    19. Zhang, Zhiqing & Zhong, Weihuang & Mao, Chengfang & Xu, Yuejiang & Lu, Kai & Ye, Yanshuai & Guan, Wei & Pan, Mingzhang & Tan, Dongli, 2024. "Multi-objective optimization of Fe-based SCR catalyst on the NOx conversion efficiency for a diesel engine based on FGRA-ANN/RF," Energy, Elsevier, vol. 294(C).
    20. Wang, Yun & Chen, Tuo & Zou, Runmin & Song, Dongran & Zhang, Fan & Zhang, Lingjun, 2022. "Ensemble probabilistic wind power forecasting with multi-scale features," Renewable Energy, Elsevier, vol. 201(P1), pages 734-751.

    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:281:y:2023:i:c:s0360544223017322. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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.