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

Xin Wang, Dahu Li (), Youxiang Jiao, Yibin Yang and Zhao Cao
Additional contact information
Xin Wang: School of Mining and Coal, Inner Mongolia University of Science and Technology, Baotou 014010, China
Dahu Li: School of Mining and Coal, Inner Mongolia University of Science and Technology, Baotou 014010, China
Youxiang Jiao: School of Mining and Coal, Inner Mongolia University of Science and Technology, Baotou 014010, China
Yibin Yang: School of Mining and Coal, Inner Mongolia University of Science and Technology, Baotou 014010, China
Zhao Cao: School of Mining and Coal, Inner Mongolia University of Science and Technology, Baotou 014010, China

Energies, 2025, vol. 18, issue 21, 1-21

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–FC ad as characteristic variables, the established SVR model achieved a mean absolute percentage error (MAPE), root mean square error (RMSE), and coefficient of determination (R 2 ) 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 ( R 2 > 0.900) can serve as an alternative for rapid estimation. This method effectively enhances the accuracy and robustness of coal calorific value prediction.

Keywords: calorific value; prediction; derived indicators; support vector regression; characteristic variables (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/18/21/5600/pdf (application/pdf)
https://www.mdpi.com/1996-1073/18/21/5600/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:18:y:2025:i:21:p:5600-:d:1779187

Access Statistics for this article

Energies is currently edited by Ms. Cassie Shen

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().