Development of a Biomass Component Prediction Model Based on Elemental and Proximate Analyses

Sun Yong Park, Kwang Cheol Oh, Seok Jun Kim, La Hoon Cho, Young Kwang Jeon and DaeHyun Kim ()
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Sun Yong Park: Department of Interdisciplinary Program in Smart Agriculture, Kangwon National University, Hyoja 2 Dong 192-1, Chuncheon 24341, Republic of Korea
Kwang Cheol Oh: Agriculture and Life Science Research Institute, Kangwon National University, Hyoja 2 Dong 192-1, Chuncheon 24341, Republic of Korea
Seok Jun Kim: Department of Interdisciplinary Program in Smart Agriculture, Kangwon National University, Hyoja 2 Dong 192-1, Chuncheon 24341, Republic of Korea
La Hoon Cho: Department of Interdisciplinary Program in Smart Agriculture, Kangwon National University, Hyoja 2 Dong 192-1, Chuncheon 24341, Republic of Korea
Young Kwang Jeon: Department of Interdisciplinary Program in Smart Agriculture, Kangwon National University, Hyoja 2 Dong 192-1, Chuncheon 24341, Republic of Korea
DaeHyun Kim: Department of Interdisciplinary Program in Smart Agriculture, Kangwon National University, Hyoja 2 Dong 192-1, Chuncheon 24341, Republic of Korea

Energies, 2023, vol. 16, issue 14, 1-17

Abstract: Emerging global environmental pollution issues have caused a reduction in coal utilization, leading to an increased research focus on biomass use as an alternative. However, due to the low heat values of biomass, studies in this field are still in progress. Biomass primarily comprises cellulose, hemicellulose, and lignin. To determine the composition of these three components, the measurement methods recommended by TAPPI (Technical Association of the Pulp and Paper Industry) and NREL (National Renewable Energy Laboratory) are typically employed involving equipment such as HPLC. However, these methods are time consuming. In this study, we proposed a model for predicting cellulose, hemicellulose, and lignin contents based on elemental and industrial analyses. A dataset comprising 174 samples was used to develop this model. This was validated using 25 additional samples. The R 2 P values for cellulose, hemicellulose, and lignin were 0.6104–0.6362, 0.4803–0.5112, and 0.7247–0.7914, respectively; however, the R 2 CV values obtained from the validation results were 0.7387–0.7837, 0.3280–0.4004, and 0.7427–0.7757, respectively. The optimal models selected for cellulose, lignin, and hemicellulose were C1, L2, and 100-(C1-L2) or H2, respectively. Our predictions for woody and herbaceous biomass, including torrefied samples, should be applied with caution to other biomass types due to the potential accuracy limitations. To enhance the prediction accuracy, future research should broaden the range of biomass types considered and gather more data specifically related to woody and herbaceous biomass.

Keywords: pollution; coal; energy; biomass (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: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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