Neven Voća

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.

In this study, the physical properties of two hazelnut species were investigated before and after drying at different temperatures and durations. The results showed that the physical properties of the hazelnut samples, including size, volume, density, weight, kernel mass, and shell mass, were significantly affected by temperature, duration, and their interactions. In addition, the moisture content of the samples decreased with increasing temperature and drying duration. The lowest value for the Istarski duguljasti variety was 5.36% (160 °C and 45 min), while the lowest value for Rimski okrugli was measured at 160 °C and 60 min (5.02%). Ash content was affected by both temperature and time, with the Istarski duguljasti variety having a minimum value of 0.84% at 120 °C and 60 min and Rimski okrugli a maximum value of 1.24% at 100 °C and 30 min. The variables of the ultimate analysis, such as nitrogen, carbon, sulfur, and hydrogen, increased with increasing temperature and time. The oxygen content and the higher heating value decreased with increasing temperature. Energy optimization in the drying process is crucial to reduce costs and save time. Effective energy optimization measures can lead to significant cost savings and improved operational efficiency in the drying process.