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The aim of the study was to investigate the changes in ultimate, proximate analysis, and calorific properties of Miscanthus x giganteus with three types of planting materials (two rhizomes - R1 and R2 - and one seedling - S) and three ash fertiliser treatments (P0, P2, and P5) were included in the study. The research further examined their effects on crop yield, stem height and various chemical properties. The results showed that the maximum yield was obtained with the R1 x P2 plant type, while the minimum yield was recorded with the R2 x P2 plant type. In addition, the greatest average stem height (3.34 m) was recorded for the R2 x P5 plant type. Significant differences were also found in the chemical components between the plant types and treatments. For example, the highest ash content of 2.25% was found in plant type 'S' x P5, while the highest coke content of 14.48 % was found in plant type R1 x P5. The statistical analysis confirmed that planting material and ash fertilisation had significant influence on the physicochemical properties of Miscanthus x giganteus. This consequently affects the calorific value, with the average higher and lower heating value being 18.32 and 17.04 MJ/kg, respectively. The neural regression network models showed robust predictive performance for the higher heating value and lower heating value, with low chi-square values, X², and high coefficients of determination, R². [ABSTRACT FROM AUTHOR]

The aim of this study was to investigate the potential of using structural analysis parameters for estimating the higher heating value (HHV) of biomass by obtaining information on the composition of cellulose, lignin, and hemicellulose. To achieve this goal, several nonlinear mathematical models were developed, including polynomials, support vector machines (SVMs), random forest regression (RFR) and artificial neural networks (ANN) for predicting HHV. The performed statistical analysis "goodness of fit" showed that the ANN model has the best performance in terms of coefficient of determination (R2 = 0.90) and the lowest level of model error for the parameters X2 (0.25), RMSE (0.50), and MPE (2.22). Thus, the ANN model was identified as the most appropriate model for determining the HHV of different biomasses based on the specified input parameters. In conclusion, the results of this study demonstrate the potential of using structural analysis parameters as input for HHV modeling, which is a promising approach for the field of biomass energy production. The development of the model ANN and the comparative analysis of the different models provide important insights for future research in this field. [ABSTRACT FROM AUTHOR]

Miscanthus is a perennial energy crop that produces high yields and has the potential to be converted into energy. The ultimate analysis determines the composition of the biomass and the energy value in terms of the higher heating value (HHV), which is the most important parameter in determining the quality of the fuel. In this study, an artificial neural network (ANN) model based on the principle of supervised learning was developed to predict the HHV of miscanthus biomass. The developed ANN model was compared with the models of predictive regression models (suggested from the literature) and the accuracy of the developed model was determined by the coefficient of determination. The paper presents data from 192 miscanthus biomass samples based on ultimate analysis and HHV. The developed model showed good properties and the possibility of prediction with high accuracy (R2 = 0.77). The paper proves the possibility of using ANN models in practical application in determining fuel properties of biomass energy crops and greater accuracy in predicting HHV than the regression models offered in the literature. [ABSTRACT FROM AUTHOR]