IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i2p690-d1027676.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/2/690/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/2/690/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sascha O. Becker, Sascha O & Voth, Hans-Joachim, 2023. "From the Death of God to the Rise of Hitler," The Warwick Economics Research Paper Series (TWERPS) 1478, University of Warwick, Department of Economics.
    2. Saidur, R. & Abdelaziz, E.A. & Demirbas, A. & Hossain, M.S. & Mekhilef, S., 2011. "A review on biomass as a fuel for boilers," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(5), pages 2262-2289, June.
    3. Kim, Jun Young & Kim, Dongjae & Li, Zezhong John & Dariva, Claudio & Cao, Yankai & Ellis, Naoko, 2023. "Predicting and optimizing syngas production from fluidized bed biomass gasifiers: A machine learning approach," Energy, Elsevier, vol. 263(PC).
    4. Wang, Feipeng & Wong, Wing-Keung & Wang, Zheng & Albasher, Gadah & Alsultan, Nouf & Fatemah, Ambreen, 2023. "Emerging pathways to sustainable economic development: An interdisciplinary exploration of resource efficiency, technological innovation, and ecosystem resilience in resource-rich regions," Resources Policy, Elsevier, vol. 85(PA).
    5. Charalampos Michalakakis & Jeremy Fouillou & Richard C. Lupton & Ana Gonzalez Hernandez & Jonathan M. Cullen, 2021. "Calculating the chemical exergy of materials," Journal of Industrial Ecology, Yale University, vol. 25(2), pages 274-287, April.
    6. Eksi, Guner & Karaosmanoglu, Filiz, 2017. "Combined bioheat and biopower: A technology review and an assessment for Turkey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 73(C), pages 1313-1332.
    7. Huang, Youwang & Wang, Haiyong & Zhang, Xinghua & Zhang, Qi & Wang, Chenguang & Ma, Longlong, 2022. "Accurate prediction of chemical exergy of technical lignins for exergy-based assessment on sustainable utilization processes," Energy, Elsevier, vol. 243(C).
    8. Chen, Xiaoling & Zhang, Yongxing & Xu, Baoshen & Li, Yifan, 2022. "A simple model for estimation of higher heating value of oily sludge," Energy, Elsevier, vol. 239(PA).
    9. Xiaxuan He & Qifeng Yuan & Yinghong Qin & Junwen Lu & Gang Li, 2024. "Analysis of Surface Urban Heat Island in the Guangzhou-Foshan Metropolitan Area Based on Local Climate Zones," Land, MDPI, vol. 13(10), pages 1-34, October.
    10. Sabarathinam Srinivasan & Suresh Kumarasamy & Zacharias E. Andreadakis & Pedro G. Lind, 2023. "Artificial Intelligence and Mathematical Models of Power Grids Driven by Renewable Energy Sources: A Survey," Energies, MDPI, vol. 16(14), pages 1-56, July.
    11. Becker, Sascha O. & Voth, Hans-Joachim, 2023. "From the Death of God to the Rise of Hitler," CEPR Discussion Papers 18543, C.E.P.R. Discussion Papers.
    12. Nahar, Gaurav & Rajput, Shailendrasingh & Grasham, Oliver & Dalvi, Vishwanath Haily & Dupont, Valerie & Ross, Andrew B. & Pandit, Aniruddha B., 2022. "Technoeconomic analysis of biogas production using simple and effective mechanistic model calibrated with biomethanation potential experiments of water lettuce (pistia stratiotes) inoculated by buffal," Energy, Elsevier, vol. 244(PB).
    13. Ahmet Faruk Aysan & Bekir Sait Ciftler & Ibrahim Musa Unal, 2024. "Predictive Power of Random Forests in Analyzing Risk Management in Islamic Banking," JRFM, MDPI, vol. 17(3), pages 1-19, March.
    14. Sakiru Adebola Solarin & Muhammed Sehid Gorus & Onder Ozgur, 2024. "Modelling the economic effect of inbound birth tourism: a random forest algorithm approach," Quality & Quantity: International Journal of Methodology, Springer, vol. 58(5), pages 4223-4240, October.
    15. Zhu, Xinyi & Shen, Xiaoyan & Chen, Kailiang & Zhang, Zeqing, 2024. "Research on the prediction and influencing factors of heavy duty truck fuel consumption based on LightGBM," Energy, Elsevier, vol. 296(C).
    16. Yang, Shiliang & Dong, Ruihan & Du, Yanxiang & Wang, Shuai & Wang, Hua, 2021. "Numerical study of the biomass pyrolysis process in a spouted bed reactor through computational fluid dynamics," Energy, Elsevier, vol. 214(C).
    17. Murat Aslan & Onder Ozgur, 2024. "Financial dollarization and its effects on inflation and output in Turkey: a machine learning approach," Quality & Quantity: International Journal of Methodology, Springer, vol. 58(6), pages 5777-5804, December.
    18. Maria A. F. Silva Dias & Yania Molina Souto & Bruno Biazeto & Enzo Todesco & Jose A. Zuñiga Mora & Dylana Vargas Navarro & Melvin Pérez Chinchilla & Carlos Madrigal Araya & Dayanna Arce Fernández & Be, 2024. "Reduction of Wind Speed Forecast Error in Costa Rica Tejona Wind Farm with Artificial Intelligence," Energies, MDPI, vol. 17(22), pages 1-12, November.
    19. Özer Depren & Mustafa Tevfik Kartal & Serpil Kılıç Depren, 2021. "Recent innovation in benchmark rates (BMR): evidence from influential factors on Turkish Lira Overnight Reference Interest Rate with machine learning algorithms," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-20, December.
    20. Tomasz Rymarczyk & Konrad Niderla & Edward Kozłowski & Krzysztof Król & Joanna Maria Wyrwisz & Sylwia Skrzypek-Ahmed & Piotr Gołąbek, 2021. "Logistic Regression with Wave Preprocessing to Solve Inverse Problem in Industrial Tomography for Technological Process Control," Energies, MDPI, vol. 14(23), pages 1-21, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:16:y:2023:i:2:p:690-:d:1027676. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.