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Energy Potentials of Agricultural Biomass and the Possibility of Modelling Using RFR and SVM Models

Author

Listed:
  • Ivan Brandić

    (Faculty of Agriculture, University of Zagreb, Svetošimunska Cesta 25, 10000 Zagreb, Croatia)

  • Alan Antonović

    (Faculty of Forestry and Wood Technology, University of Zagreb, Svetošimunska Cesta 23, 10000 Zagreb, Croatia)

  • Lato Pezo

    (Institute of General and Physical Chemistry, University of Belgrade, Studentski trg 12/V, 11000 Belgrade, Serbia)

  • Božidar Matin

    (Faculty of Forestry and Wood Technology, University of Zagreb, Svetošimunska Cesta 23, 10000 Zagreb, Croatia)

  • Tajana Krička

    (Faculty of Agriculture, University of Zagreb, Svetošimunska Cesta 25, 10000 Zagreb, Croatia)

  • Vanja Jurišić

    (Faculty of Agriculture, University of Zagreb, Svetošimunska Cesta 25, 10000 Zagreb, Croatia)

  • Karlo Špelić

    (Faculty of Agriculture, University of Zagreb, Svetošimunska Cesta 25, 10000 Zagreb, Croatia)

  • Mislav Kontek

    (Faculty of Agriculture, University of Zagreb, Svetošimunska Cesta 25, 10000 Zagreb, Croatia)

  • Juraj Kukuruzović

    (Faculty of Agriculture, University of Zagreb, Svetošimunska Cesta 25, 10000 Zagreb, Croatia)

  • Mateja Grubor

    (Faculty of Agriculture, University of Zagreb, Svetošimunska Cesta 25, 10000 Zagreb, Croatia)

  • Ana Matin

    (Faculty of Agriculture, University of Zagreb, Svetošimunska Cesta 25, 10000 Zagreb, Croatia)

Abstract

Agricultural biomass is one of the most important renewable energy sources. As a byproduct of corn, soybean and sunflower production, large amounts of biomass are produced that can be used as an energy source through conversion. In order to assess the quality and the possibility of the use of biomass, its composition and calorific value must be determined. The use of nonlinear models allows for an easier estimation of the energy properties of biomass concerning certain input and output parameters. In this paper, RFR (Random Forest Regression) and SVM (Support Vector Machine) models were developed to determine their capabilities in estimating the HHV (higher heating value) of biomass based on input parameters of ultimate analysis. The developed models showed good performance in terms of HHV estimation, confirmed by the coefficient of determination for the RFR (R 2 = 0.79) and SVM (R 2 = 0.93) models. The developed models have shown promising results in accurately predicting the HHV of biomass from various sources. The use of these algorithms for biomass energy prediction has the potential for further development.

Suggested Citation

  • Ivan Brandić & Alan Antonović & Lato Pezo & Božidar Matin & Tajana Krička & Vanja Jurišić & Karlo Špelić & Mislav Kontek & Juraj Kukuruzović & Mateja Grubor & Ana Matin, 2023. "Energy Potentials of Agricultural Biomass and the Possibility of Modelling Using RFR and SVM Models," Energies, MDPI, vol. 16(2), pages 1-10, January.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:2:p:690-:d:1027676
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    References listed on IDEAS

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    1. Qian, Hongliang & Zhu, Weiwei & Fan, Sudong & Liu, Chang & Lu, Xiaohua & Wang, Zhixiang & Huang, Dechun & Chen, Wei, 2017. "Prediction models for chemical exergy of biomass on dry basis from ultimate analysis using available electron concepts," Energy, Elsevier, vol. 131(C), pages 251-258.
    2. Xing, Jiangkuan & Luo, Kun & Wang, Haiou & Gao, Zhengwei & Fan, Jianren, 2019. "A comprehensive study on estimating higher heating value of biomass from proximate and ultimate analysis with machine learning approaches," Energy, Elsevier, vol. 188(C).
    3. Erol, M. & Haykiri-Acma, H. & Küçükbayrak, S., 2010. "Calorific value estimation of biomass from their proximate analyses data," Renewable Energy, Elsevier, vol. 35(1), pages 170-173.
    4. Kamil Roman & Jan Barwicki & Witold Rzodkiewicz & Mariusz Dawidowski, 2021. "Evaluation of Mechanical and Energetic Properties of the Forest Residues Shredded Chips during Briquetting Process," Energies, MDPI, vol. 14(11), pages 1-11, June.
    5. Matthias Schonlau & Rosie Yuyan Zou, 2020. "The random forest algorithm for statistical learning," Stata Journal, StataCorp LP, vol. 20(1), pages 3-29, March.
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