IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v262y2023ipas0360544222023805.html
   My bibliography  Save this article

Attention mechanism-aided data- and knowledge-driven soft sensors for predicting blast furnace gas generation

Author

Listed:
  • Liu, Shuhan
  • Sun, Wenqiang

Abstract

Blast furnace gas (BFG) is an important energy-carrying byproduct of the iron and steel industry. High-accuracy prediction of BFG generation is the basis of the dynamic balance of gas supply–demand and energy scheduling. However, due to instrument faults, measurements of BFG are discontinuous or inaccurate, making it difficult to accurately predict future BFG generation by using historical data, which seriously restricts the development of intelligent management and coordination between various gas sources and users. To solve this problem, an attention mechanism-aided data- and knowledge-driven soft sensor is proposed to predict BFG generation. To reduce the complexity of the samples, the proposed method selects key features to simplify the model input by using attention mechanism. Genetic algorithm (GA) is used to optimize hyperparameters to improve the stability of the model. In addition, combined with the knowledge of the blast furnace process, the prediction results are reasonably constrained. The results show that the prediction accuracy of the A-DK-GA-XGBoost model is higher than that of the other prediction models, with a mean absolute error of 68.2 m3/min, a symmetric mean absolute percentage error of 0.83%, a root mean square error of 68.71 m3/min, and an R squared of 99.06%. It is proven that the A-DK-GA-XGBoost model has superior performance.

Suggested Citation

  • Liu, Shuhan & Sun, Wenqiang, 2023. "Attention mechanism-aided data- and knowledge-driven soft sensors for predicting blast furnace gas generation," Energy, Elsevier, vol. 262(PA).
    • Handle: RePEc:eee:energy:v:262:y:2023:i:pa:s0360544222023805
    DOI: 10.1016/j.energy.2022.125498
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544222023805
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2022.125498?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. Wang, Xiaoyang & Sun, Yunlin & Luo, Duo & Peng, Jinqing, 2022. "Comparative study of machine learning approaches for predicting short-term photovoltaic power output based on weather type classification," Energy, Elsevier, vol. 240(C).
    2. Ding, Lin & Bai, Yulong & Liu, Ming-De & Fan, Man-Hong & Yang, Jie, 2022. "Predicting short wind speed with a hybrid model based on a piecewise error correction method and Elman neural network," Energy, Elsevier, vol. 244(PA).
    3. Ali, Aliyuda, 2021. "Data-driven based machine learning models for predicting the deliverability of underground natural gas storage in salt caverns," Energy, Elsevier, vol. 229(C).
    4. Sun, Wenqiang & Wang, Qiang & Zhou, Yue & Wu, Jianzhong, 2020. "Material and energy flows of the iron and steel industry: Status quo, challenges and perspectives," Applied Energy, Elsevier, vol. 268(C).
    5. Na, Hongming & Sun, Jingchao & Qiu, Ziyang & He, Jianfei & Yuan, Yuxing & Yan, Tianyi & Du, Tao, 2021. "A novel evaluation method for energy efficiency of process industry — A case study of typical iron and steel manufacturing process," Energy, Elsevier, vol. 233(C).
    6. Zhong, Wei & Huang, Wei & Lin, Xiaojie & Li, Zhongbo & Zhou, Yi, 2020. "Research on data-driven identification and prediction of heat response time of urban centralized heating system," Energy, Elsevier, vol. 212(C).
    7. Xueying Sun & Zhuo Wang & Jingtao Hu, 2017. "Prediction Interval Construction for Byproduct Gas Flow Forecasting Using Optimized Twin Extreme Learning Machine," Mathematical Problems in Engineering, Hindawi, vol. 2017, pages 1-12, August.
    8. Ma, Shuaiyin & Zhang, Yingfeng & Lv, Jingxiang & Ge, Yuntian & Yang, Haidong & Li, Lin, 2020. "Big data driven predictive production planning for energy-intensive manufacturing industries," Energy, Elsevier, vol. 211(C).
    9. Díaz, Guzmán & Coto, José & Gómez-Aleixandre, Javier, 2019. "Optimal operation value of combined wind power and energy storage in multi-stage electricity markets," Applied Energy, Elsevier, vol. 235(C), pages 1153-1168.
    10. Sun, Wenqiang & Wang, Zihao & Wang, Qiang, 2020. "Hybrid event-, mechanism- and data-driven prediction of blast furnace gas generation," Energy, Elsevier, vol. 199(C).
    11. Gorus, Muhammed Sehid & Aydin, Mucahit, 2019. "The relationship between energy consumption, economic growth, and CO2 emission in MENA countries: Causality analysis in the frequency domain," Energy, Elsevier, vol. 168(C), pages 815-822.
    12. Zhang, Hanxin & Sun, Wenqiang & Li, Weidong & Ma, Guangyu, 2022. "A carbon flow tracing and carbon accounting method for exploring CO2 emissions of the iron and steel industry: An integrated material–energy–carbon hub," Applied Energy, Elsevier, vol. 309(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. Ma, Shuaiyin & Huang, Yuming & Liu, Yang & Kong, Xianguang & Yin, Lei & Chen, Gaige, 2023. "Edge-cloud cooperation-driven smart and sustainable production for energy-intensive manufacturing industries," Applied Energy, Elsevier, vol. 337(C).
    2. Wen, Shizhao & Wang, Hongzeng & Qian, Jinhua & Men, Xuanyu, 2023. "A novel combined model based on echo state network optimized by whale optimization algorithm for blast furnace gas prediction," Energy, Elsevier, vol. 279(C).
    3. Ma, Shuaiyin & Ding, Wei & Liu, Yang & Ren, Shan & Yang, Haidong, 2022. "Digital twin and big data-driven sustainable smart manufacturing based on information management systems for energy-intensive industries," Applied Energy, Elsevier, vol. 326(C).
    4. Cui, Zhipeng & Xu, Jing & Liu, Wenhao & Zhao, Guanjia & Ma, Suxia, 2023. "Data-driven modeling-based digital twin of supercritical coal-fired boiler for metal temperature anomaly detection," Energy, Elsevier, vol. 278(PA).
    5. Wang, Jiayang & Wang, Qiang & Sun, Wenqiang, 2023. "Quantifying flexibility provisions of the ladle furnace refining process as cuttable loads in the iron and steel industry," Applied Energy, Elsevier, vol. 342(C).
    6. Ma, Shuaiyin & Huang, Yuming & Liu, Yang & Liu, Haizhou & Chen, Yanping & Wang, Jin & Xu, Jun, 2023. "Big data-driven correlation analysis based on clustering for energy-intensive manufacturing industries," Applied Energy, Elsevier, vol. 349(C).

    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. Ma, Shuaiyin & Ding, Wei & Liu, Yang & Ren, Shan & Yang, Haidong, 2022. "Digital twin and big data-driven sustainable smart manufacturing based on information management systems for energy-intensive industries," Applied Energy, Elsevier, vol. 326(C).
    2. Ma, Shuaiyin & Huang, Yuming & Liu, Yang & Kong, Xianguang & Yin, Lei & Chen, Gaige, 2023. "Edge-cloud cooperation-driven smart and sustainable production for energy-intensive manufacturing industries," Applied Energy, Elsevier, vol. 337(C).
    3. Sun, Jingchao & Na, Hongming & Yan, Tianyi & Che, Zichang & Qiu, Ziyang & Yuan, Yuxing & Li, Yingnan & Du, Tao & Song, Yanli & Fang, Xin, 2022. "Cost-benefit assessment of manufacturing system using comprehensive value flow analysis," Applied Energy, Elsevier, vol. 310(C).
    4. Ma, Shuaiyin & Huang, Yuming & Liu, Yang & Liu, Haizhou & Chen, Yanping & Wang, Jin & Xu, Jun, 2023. "Big data-driven correlation analysis based on clustering for energy-intensive manufacturing industries," Applied Energy, Elsevier, vol. 349(C).
    5. Yuan, Yuxing & Na, Hongming & Du, Tao & Qiu, Ziyang & Sun, Jingchao & Yan, Tianyi & Che, Zichang, 2023. "Multi-objective optimization and analysis of material and energy flows in a typical steel plant," Energy, Elsevier, vol. 263(PD).
    6. Jiang, Sheng-Long & Wang, Meihong & Bogle, I. David L., 2023. "Plant-wide byproduct gas distribution under uncertainty in iron and steel industry via quantile forecasting and robust optimization," Applied Energy, Elsevier, vol. 350(C).
    7. Sun, Wenqiang & Wang, Zihao & Wang, Qiang, 2020. "Hybrid event-, mechanism- and data-driven prediction of blast furnace gas generation," Energy, Elsevier, vol. 199(C).
    8. Yang, Jiaojiao & Sun, Zeyi & Hu, Wenqing & Steinmeister, Louis, 2022. "Joint control of manufacturing and onsite microgrid system via novel neural-network integrated reinforcement learning algorithms," Applied Energy, Elsevier, vol. 315(C).
    9. Zhang, Hanxin & Sun, Wenqiang & Li, Weidong & Ma, Guangyu, 2022. "A carbon flow tracing and carbon accounting method for exploring CO2 emissions of the iron and steel industry: An integrated material–energy–carbon hub," Applied Energy, Elsevier, vol. 309(C).
    10. Ma, Shuaiyin & Zhang, Yingfeng & Lv, Jingxiang & Ge, Yuntian & Yang, Haidong & Li, Lin, 2020. "Big data driven predictive production planning for energy-intensive manufacturing industries," Energy, Elsevier, vol. 211(C).
    11. Shuangping Wu & Anjun Xu, 2021. "Calculation Method of Energy Saving in Process Engineering: A Case Study of Iron and Steel Production Process," Energies, MDPI, vol. 14(18), pages 1-15, September.
    12. Na, Hongming & Sun, Jingchao & Qiu, Ziyang & Yuan, Yuxing & Du, Tao, 2022. "Optimization of energy efficiency, energy consumption and CO2 emission in typical iron and steel manufacturing process," Energy, Elsevier, vol. 257(C).
    13. Na, Hongming & Sun, Jingchao & Qiu, Ziyang & He, Jianfei & Yuan, Yuxing & Yan, Tianyi & Du, Tao, 2021. "A novel evaluation method for energy efficiency of process industry — A case study of typical iron and steel manufacturing process," Energy, Elsevier, vol. 233(C).
    14. Wang, Jiayang & Wang, Qiang & Sun, Wenqiang, 2023. "Quantifying flexibility provisions of the ladle furnace refining process as cuttable loads in the iron and steel industry," Applied Energy, Elsevier, vol. 342(C).
    15. Yang, Honghua & Ma, Linwei & Li, Zheng, 2023. "Tracing China's steel use from steel flows in the production system to steel footprints in the consumption system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 172(C).
    16. Che, Gelegen & Zhang, Yanyan & Tang, Lixin & Zhao, Shengnan, 2023. "A deep reinforcement learning based multi-objective optimization for the scheduling of oxygen production system in integrated iron and steel plants," Applied Energy, Elsevier, vol. 345(C).
    17. 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.
    18. Shengxiang Lv & Lin Wang & Sirui Wang, 2023. "A Hybrid Neural Network Model for Short-Term Wind Speed Forecasting," Energies, MDPI, vol. 16(4), pages 1-18, February.
    19. Zheng, Lingwei & Su, Ran & Sun, Xinyu & Guo, Siqi, 2023. "Historical PV-output characteristic extraction based weather-type classification strategy and its forecasting method for the day-ahead prediction of PV output," Energy, Elsevier, vol. 271(C).
    20. Xiaohang Ren & Cheng Cheng & Zhen Wang & Cheng Yan, 2021. "Spillover and dynamic effects of energy transition and economic growth on carbon dioxide emissions for the European Union: A dynamic spatial panel model," Sustainable Development, John Wiley & Sons, Ltd., vol. 29(1), pages 228-242, January.

    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:262:y:2023:i:pa:s0360544222023805. 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.