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A Fast Screening Method of Key Parameters from Coal for Carbon Emission Enterprises

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  • Weiye Lu

    (School of Electric Power, South China University of Technology, Guangzhou 510640, China
    Guangdong Institute of Special Equipment Inspection and Research Shunde Branch, Foshan 528300, China)

  • Xiaoxuan Chen

    (Guangdong Institute of Special Equipment Inspection and Research Shunde Branch, Foshan 528300, China)

  • Zhuorui Song

    (Guangdong Institute of Special Equipment Inspection and Research Shunde Branch, Foshan 528300, China)

  • Yuesheng Li

    (Guangdong Institute of Special Equipment Inspection and Research Shunde Branch, Foshan 528300, China)

  • Jidong Lu

    (School of Electric Power, South China University of Technology, Guangzhou 510640, China)

Abstract

During the process of determining carbon emissions from coal using the emission factor method, third-party organizations in China are responsible for verifying the accuracy of the carbon emission data. However, these verifiers face challenges in efficiently handling large quantities of data. Therefore, this study proposed a fast screening method that utilizes multiple linear regression (MLR), in combination with the stepwise backward regression method, to identify problematic carbon emission data for the lower calorific value (LCV) and carbon content (C) of coal. The results demonstrated the effectiveness of the proposed method. The regression models for LCV and C exhibited high R-squared (R 2 ) values of 0.9784 and 0.9762, respectively, and the root mean square error (RMSE) values of the validation set were 0.32 MJ/kg and 0.80% for LCV and C, respectively, indicating strong predictive capabilities. By analyzing the obtained results, the study established the optional error threshold interval for the LCV and C of coal as 2RMSE–3RMSE. This interval can be utilized as a reliable criterion for judging the quality and reliability of carbon emission data during the verification process. Overall, the proposed screening method can serve as a valuable tool for verifiers in assessing the quality and reliability of carbon emission data in various regions.

Suggested Citation

  • Weiye Lu & Xiaoxuan Chen & Zhuorui Song & Yuesheng Li & Jidong Lu, 2023. "A Fast Screening Method of Key Parameters from Coal for Carbon Emission Enterprises," Energies, MDPI, vol. 16(22), pages 1-14, November.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:22:p:7592-:d:1280819
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    References listed on IDEAS

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    1. Hong Chang & Wei Sun & Xingsheng Gu, 2013. "Forecasting Energy CO 2 Emissions Using a Quantum Harmony Search Algorithm-Based DMSFE Combination Model," Energies, MDPI, vol. 6(3), pages 1-22, March.
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