Drying Kinetics Prediction of Solid Waste Using Semi-Empirical and Artificial Neural Network Models

The drying process of organic solid waste is investigated, based on an experimental study involving its drying kinetics. The experiments were conducted in a thin‐layer fixed‐bed dryer under various operational conditions. The problem of selecting the best fit for solid waste moisture content as a function of time is addressed as well, using artificial neural network (ANN) models and four well‐known drying kinetics correlations commonly applied to biological materials. According to the statistical analysis employed, the simulations showed good results for the ANN, and the Overhults model provided optimum agreement with experimental data among all other models evaluated. Empirical correlations between the Overhults model parameters and the drying operational conditions using nonlinear regression techniques were determined. The kinetics of the drying process of solid citrus wastes in a thin‐layer fixed‐bed dryer under different operational conditions is investigated. By means of artificial neural networks and four well‐known drying kinetics correlations, the problem of selecting the best fit for solid waste moisture content as a function of time is addressed as well.