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Prediction of Coal Calorific Value Based on Coal Quality-Derived Indicators and Support Vector Regression Method.

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Title: Prediction of Coal Calorific Value Based on Coal Quality-Derived Indicators and Support Vector Regression Method.
Authors: Wang, Xin1 (AUTHOR), Li, Dahu1,2,3,4 (AUTHOR) lidahu649@126.com, Jiao, Youxiang1,3 (AUTHOR), Yang, Yibin1,4 (AUTHOR), Cao, Zhao1,2,3,4 (AUTHOR)
Source: Energies (19961073). Nov2025, Vol. 18 Issue 21, p5600. 21p.
Subjects: Nonlinear statistical models, Feature selection, Thermal coal, Predictive validity, Support vector machines, Elemental analysis, Statistical significance, Indicators & test-papers
Abstract: This study addresses the limitations of traditional coal calorific value prediction models, which primarily rely on linear regression and single-source proximate analysis data. Based on 465 Chinese coal samples and integrating proximate analysis, ultimate analysis, and constructed derived indicators (combustible content—CC, carbon–hydrogen index—CHI, carbon in combustibles—CIC), a nonlinear modeling method combining mean impact value (MIV) feature selection and support vector regression (SVR) is proposed. The results show that the Pearson correlation coefficients between the derived indicators and net calorific value (NCV) all exceed 0.93, outperforming the original items. Using CC–CHI–CIC–FCad as characteristic variables, the established SVR model achieved a mean absolute percentage error (MAPE), root mean square error (RMSE), and coefficient of determination (R2) of 1.838%, 0.544 MJ/kg, and 0.962, respectively, with exceptionally high statistical significance (F = 1485.96, p < 0.001). The predictive accuracy of this model is significantly superior to traditional linear models, while the proposed linear model based on the derived indicators (R2 > 0.900) can serve as an alternative for rapid estimation. This method effectively enhances the accuracy and robustness of coal calorific value prediction. [ABSTRACT FROM AUTHOR]
Copyright of Energies (19961073) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Prediction of Coal Calorific Value Based on Coal Quality-Derived Indicators and Support Vector Regression Method.
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  Data: &lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Wang%2C+Xin%22&quot;&gt;Wang, Xin&lt;/searchLink&gt;&lt;relatesTo&gt;1&lt;/relatesTo&gt; (AUTHOR)&lt;br /&gt;&lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Li%2C+Dahu%22&quot;&gt;Li, Dahu&lt;/searchLink&gt;&lt;relatesTo&gt;1,2,3,4&lt;/relatesTo&gt; (AUTHOR)&lt;i&gt; lidahu649@126.com&lt;/i&gt;&lt;br /&gt;&lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Jiao%2C+Youxiang%22&quot;&gt;Jiao, Youxiang&lt;/searchLink&gt;&lt;relatesTo&gt;1,3&lt;/relatesTo&gt; (AUTHOR)&lt;br /&gt;&lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Yang%2C+Yibin%22&quot;&gt;Yang, Yibin&lt;/searchLink&gt;&lt;relatesTo&gt;1,4&lt;/relatesTo&gt; (AUTHOR)&lt;br /&gt;&lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Cao%2C+Zhao%22&quot;&gt;Cao, Zhao&lt;/searchLink&gt;&lt;relatesTo&gt;1,2,3,4&lt;/relatesTo&gt; (AUTHOR)
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  Data: &lt;searchLink fieldCode=&quot;JN&quot; term=&quot;%22Energies+%2819961073%29%22&quot;&gt;Energies (19961073)&lt;/searchLink&gt;. Nov2025, Vol. 18 Issue 21, p5600. 21p.
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– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: This study addresses the limitations of traditional coal calorific value prediction models, which primarily rely on linear regression and single-source proximate analysis data. Based on 465 Chinese coal samples and integrating proximate analysis, ultimate analysis, and constructed derived indicators (combustible content—CC, carbon–hydrogen index—CHI, carbon in combustibles—CIC), a nonlinear modeling method combining mean impact value (MIV) feature selection and support vector regression (SVR) is proposed. The results show that the Pearson correlation coefficients between the derived indicators and net calorific value (NCV) all exceed 0.93, outperforming the original items. Using CC–CHI–CIC–FCad as characteristic variables, the established SVR model achieved a mean absolute percentage error (MAPE), root mean square error (RMSE), and coefficient of determination (R2) of 1.838%, 0.544 MJ/kg, and 0.962, respectively, with exceptionally high statistical significance (F = 1485.96, p &lt; 0.001). The predictive accuracy of this model is significantly superior to traditional linear models, while the proposed linear model based on the derived indicators (R2 &gt; 0.900) can serve as an alternative for rapid estimation. This method effectively enhances the accuracy and robustness of coal calorific value prediction. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
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  Data: &lt;i&gt;Copyright of Energies (19961073) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder&#39;s express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.&lt;/i&gt; (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.3390/en18215600
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 21
        StartPage: 5600
    Subjects:
      – SubjectFull: Nonlinear statistical models
        Type: general
      – SubjectFull: Feature selection
        Type: general
      – SubjectFull: Thermal coal
        Type: general
      – SubjectFull: Predictive validity
        Type: general
      – SubjectFull: Support vector machines
        Type: general
      – SubjectFull: Elemental analysis
        Type: general
      – SubjectFull: Statistical significance
        Type: general
      – SubjectFull: Indicators & test-papers
        Type: general
    Titles:
      – TitleFull: Prediction of Coal Calorific Value Based on Coal Quality-Derived Indicators and Support Vector Regression Method.
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            NameFull: Wang, Xin
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            NameFull: Li, Dahu
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            NameFull: Jiao, Youxiang
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            NameFull: Yang, Yibin
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            NameFull: Cao, Zhao
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            – D: 01
              M: 11
              Text: Nov2025
              Type: published
              Y: 2025
          Identifiers:
            – Type: issn-print
              Value: 19961073
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            – TitleFull: Energies (19961073)
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