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

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 biomas...

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Bibliographic Details
Published in:Energies (Basel) 2023-01, Vol.16 (2), p.690
Main Authors: Brandić, Ivan, Antonović, Alan, Pezo, Lato, Matin, Božidar, Krička, Tajana, Jurišić, Vanja, Špelić, Karlo, Kontek, Mislav, Kukuruzović, Juraj, Grubor, Mateja, Matin, Ana
Format: Article
Language:English
Subjects:
agricultural biomass
Agricultural production
Biodiesel fuels
Biofuels
Biomass
Biomass energy
Biomass energy production
Calorific value
Carbon
Climate change
Energy
energy potential
Energy sources
estimation
higher heating value
Machine learning
Quality assessment
Raw materials
Regression analysis
Renewable energy sources
Software
Soybeans
Support vector machines
Variance analysis
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Summary: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 (R2 = 0.79) and SVM (R2 = 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.
ISSN:1996-1073
1996-1073
DOI:10.3390/en16020690