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Developing a Proximate Component Prediction Model of Biomass Based on Element Analysis

Author

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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
    These authors contributed equally to this work.)

  • Seok Jun Kim

    (Department of Interdisciplinary Program in Smart Agriculture, Kangwon National University, Hyoja 2 Dong 192-1, Chuncheon-si 24341, Republic of Korea
    These authors contributed equally to this work.)

  • Kwang Cheol Oh

    (Agriculture and Life Science Research Institute, 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)

  • DaeHyun Kim

    (Department of Interdisciplinary Program in Smart Agriculture, Kangwon National University, Hyoja 2 Dong 192-1, Chuncheon-si 24341, Republic of Korea
    Department of Biosystems Engineering, Kangwon National University, Hyoja 2 Dong 192-1, Chuncheon-si 24341, Republic of Korea)

Abstract

Interest in biomass has increased due to current environmental issues, and biomass analysis is usually performed using element and proximate analyses to ascertain its fuel characteristics. Mainly, element component prediction models have been developed based on proximate analysis, yet few studies have predicted proximate components based on element analysis. Hence, this study developed a proximate component prediction model following the calorific value calculation. Analysis of Pearson’s correlation coefficient showed that volatile matter (VM) and fixed carbon (FC) were positively correlated with hydrogen and oxygen, and with carbon, respectively. Thus, the model correlation was developed using a combination of the “stepwise” and “enter” methods along with linear or nonlinear regressions. The optimal models were developed for VM and ash content (Ash). The VM optimal model values were: R 2 = 0.9402, root-mean-square error (RMSE) = 7.0063, average absolute error (AAE) = 14.8170%, and average bias error (ABE) = −11.7862%. For Ash, the values were: R 2 = 0.9249, RMSE = 2.9614, AAE = 168.9028%, and ABE = 167.2849%, and for FC, the values were: R 2 = 9505, RMSE = 6.3214, AAE = 18.3199%, and ABE = 15.0094%. This study provides a model to predict the proximate component by element analysis. Contrary to existing method, proximate analysis can be predicted based on elemental analysis, and shows that consume samples can be performed at once.

Suggested Citation

  • Sunyong Park & Seok Jun Kim & Kwang Cheol Oh & La Hoon Cho & DaeHyun Kim, 2023. "Developing a Proximate Component Prediction Model of Biomass Based on Element Analysis," Energies, MDPI, vol. 16(1), pages 1-14, January.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:1:p:509-:d:1022961
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    References listed on IDEAS

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    3. Oh, Kwang Cheol & Kim, Junghwan & Park, Sun Yong & Kim, Seok Jun & Cho, La Hoon & Lee, Chung Geon & Roh, Jiwon & Kim, Dae Hyun, 2021. "Development and validation of torrefaction optimization model applied element content prediction of biomass," Energy, Elsevier, vol. 214(C).
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