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2025 Vol.37, Issue 3 Preview Page

Research Article

Development of Calorific Value Prediction Model Using Torrefied Biomass Elemental, Proximate and Chemical Composition
Sunyong Park1†
,
Kwang Cheol Oh2†
,
Seok Jun Kim3
,
Sunhwa Ryu4
,
Dae Hyun Kim5*
1Postdorctoral Researcher, Forest Industrial Materials Division, National Institute of Forest Science, Seoul 02455, Korea
2Researcher, Korea Research Institute on Climate Change, Chuncheon 24239, Korea
3Researcher, Agriculture and Life Science Research Institute, Kangwon National University, Chuncheon 24341, Korea
4Senior Researcher, Forest Industrial Materials Division, National Institute of Forest Science, Seoul 02455, Korea
5Professor, Department of Biosystems Engineering, Kangwon National University, Chuncheon 24341, Korea
*Corresponding Author
†These authors contributed equally to this work.
30 September 2025. pp. 235-248
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Abstract

With increasing environmental degradation, biofuels such as biochar have emerged as viable alternatives to fossil fuels. The heating value of biofuels is a key parameter for assessing their efficiency, yet it is often lower than that of traditional fossil fuels. Direct measurement of heating value requires multiple analyses, consuming both time and resources. To address this, numerous predictive models based on various analytical approaches have been developed. This study constructed heating value prediction models using elemental analysis, proximate analysis, and chemical composition analysis, applying them to thermally treated herbaceous agricultural biomass (soybean pods, corncobs), woody agricultural biomass (pepper stems, grape pruning branches), and forestry biomass. A dataset of 66 samples was analyzed. Model performance was evaluated using the coefficient of determination (R2) and root mean square error (RMSE), with elemental analysis models achieving the highest accuracy (R2 = 0.9302 - 0.9650), followed by chemical composition (R2 = 0.8920 - 0.9082) and proximate analysis (R2 = 0.7882 - 0.9882). The optimal models were selected based on both statistical performance and agreement with previous literature. These equations can support rapid estimation of heating values, reducing the need for direct calorimetry. The findings have potential applications in industrial bioenergy production, agricultural residue management, and biomass-based energy policy planning.

Keywords
Calorific value prediction
Chemical composition analysis
Elemental analysis
Proximate analysis
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Information
  • Publisher :Agriculture and Life Sciences Research Institute, Kangwon National University
  • Publisher(Ko) :None
  • Journal Title :Journal of Agricultural, Life and Environmental Sciences
  • Volume : 37
  • No :3
  • Pages :235-248
  • Received Date : 2025-05-14
  • Revised Date : 2025-08-12
  • Accepted Date : 2025-08-28
  • DOI :https://doi.org/10.22698/jales.20250019
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