The increasing amount of residual waste presents several opportunities to use biomass as a renewable energy source. Agricultural biomass is a raw material with a high ash content, which can be a problem in any form of energy conversion. To obtain better quality biofuel, excess mineral matter must be removed. Demineralization is a simple form of mixing and washing biomass with various liquids to reduce ash content. Water, acetic acid, hydrochloric acid and nitric acid are common solvents used for this purpose. Ash is composed of different micro (Zn, Cu, Fe) and macro elements (Mg, Ca, K), which can have different consequences for the use of biomass for thermal energy. Different solvents have different effects on the individual elements, with inorganic acids having the greatest effect in demineralization processes, with a reduction in ash content of up to 80% for corn and about 99% for soybeans.

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.