Calorific Value Prediction Model Using Structure Composition of Heat-Treated Lignocellulosic Biomass

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

Energies, 2023, vol. 16, issue 23, 1-15

Abstract: This study aims to identify an equation for predicting the calorific value for heat-treated biomass using structural analysis. Different models were constructed using 129 samples of cellulose, hemicellulose, and lignin, and calorific values obtained from previous studies. These models were validated using 41 additional datasets, and an optimal model was identified using its results and following performance metrics: the coefficient of determination (R 2 ), mean absolute error (MAE), root-mean-squared error (RMSE), average absolute error (AAE), and average bias error (ABE). Finally, the model was verified using 25 additional data points. For the overall dataset, R 2 was ~0.52, and the RMSE range was 1.46–1.77. For woody biomass, the R 2 range was 0.78–0.83, and the RMSE range was 0.9626–1.2810. For herbaceous biomass, the R 2 range was 0.5251–0.6001, and the RMSE range was 1.1822–1.3957. The validation results showed similar or slightly poorer performances. The optimal model was then tested using the test data. For overall biomass and woody biomass, the performance metrics of the obtained model were superior to those in previous studies, whereas for herbaceous biomass, lower performance metrics were observed. The identified model demonstrated equal or superior performance compared to linear models. Further improvements are required based on a wider range of structural biomass data.

Keywords: woody biomass; herbaceous biomass; prediction model; calorific value (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 (1)

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