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Biomass Higher Heating Value Estimation: A Comparative Analysis of Machine Learning Models

Author

Listed:
  • Ivan Brandić

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

  • Lato Pezo

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

  • Neven Voća

    (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

The research conducted focused on the capabilities of various non-linear and machine learning (ML) models in estimating the higher heating value (HHV) of biomass using proximate analysis data as inputs. The research was carried out to identify the most appropriate model for the estimation of HHV, which was determined by a statistical analysis of the modeling error. In this sense, artificial neural networks (ANNs), support vector machine (SVM), random forest regression (RFR), and higher-degree polynomial models were compared. After statistical analysis of the modeling error, the ANN model was found to be the most suitable for estimating the HHV biomass and showed the highest specific regression coefficient, with an R 2 of 0.92. SVM ( R 2 = 0.81), RFR, and polynomial models ( R 2 = 0.84), on the other hand, also exhibit a high degree of estimation, albeit with somewhat larger modelling errors. The study conducted suggests that ANN models are best suited for the non-linear modeling of HHV of biomass, as they can generalize and search for links between input and output data that are more robust but also more complex in structure.

Suggested Citation

  • Ivan Brandić & Lato Pezo & Neven Voća & Ana Matin, 2024. "Biomass Higher Heating Value Estimation: A Comparative Analysis of Machine Learning Models," Energies, MDPI, vol. 17(9), pages 1-11, April.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:9:p:2137-:d:1386485
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    References listed on IDEAS

    as
    1. Damilola Elizabeth Babatunde & Ambrose Anozie & James Omoleye, 2020. "Artificial Neural Network and its Applications in the Energy Sector An Overview," International Journal of Energy Economics and Policy, Econjournals, vol. 10(2), pages 250-264.
    2. Azwifunimunwe Tshikovhi & Tshwafo Ellias Motaung, 2023. "Technologies and Innovations for Biomass Energy Production," Sustainability, MDPI, vol. 15(16), pages 1-21, August.
    3. Ivan Brandić & Lato Pezo & Nikola Bilandžija & Anamarija Peter & Jona Šurić & Neven Voća, 2023. "Comparison of Different Machine Learning Models for Modelling the Higher Heating Value of Biomass," Mathematics, MDPI, vol. 11(9), pages 1-14, April.
    4. Sun Yong Park & Kwang Cheol Oh & Seok Jun Kim & La Hoon Cho & Young Kwang Jeon & DaeHyun Kim, 2023. "Development of a Biomass Component Prediction Model Based on Elemental and Proximate Analyses," Energies, MDPI, vol. 16(14), pages 1-17, July.
    Full references (including those not matched with items on IDEAS)

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