Mathematical modeling of sunflower seed (Helianthus annuus L.)

Sunflower seeds are an important source of edible oil and animal protein, with potential uses in the biofuel industry. It is produced in a wide range of geographical areas due to its ability to adapt to different growing conditions. The aim of this study was to select the mathematical models that best fit the drying kinetics of sunflower seeds, cultivar Altis 99, at different temperatures. The seeds were dried in an oven with forced air circulation at temperatures of 40, 50, 60, 70, and 80 °C. The reduction in mass during drying was monitored by weighing at pre-established intervals. Ten mathematical models were fitted to the experimental moisture content ratio data based on nonlinear regression analysis using the Gauss-Newton method and complemented by the chi-square test (χ2) and the Akaike information and Schwarz’s Bayesian information criteria. The drying time of sunflower seeds decreased as the temperature of the drying air increased, representing higher rates of water removal. The Midilli model is recommended for describing the drying kinetics of sunflower seeds due to the quality of the fit and the simplicity of its application.