Suitability of Thin-Layer Drying Models for Halogen Lamp Drying of Sugarcane Bagasse

Abstract

The drying process of bagasse particles was investigated in this study using an artificial neural network (ANN) and 18 thin-layer drying (TLD) models. These models are used for investigating kinetics and understanding engineering parameters involved in the drying process of food and agricultural products. Bagasse particles were studied at 105 to 135 °C and an initial moisture content of 180% (based on the dry weight) using a halogen moisture analyzer. The results showed that an increase in the temperature decreased the bagasse drying period and increased the constant drying. The whole drying process of bagasse happened in a falling drying rate period. The fitness of drying curves on semi-experimental TLD models based on statistical parameters, including root mean square error (RMSE), sum of square errors (SSE), and coefficient of determination (R2) showed that the Hii et al. model had the highest coefficient of determination and the lowest error percentage. The ANN predicted changes in the bagasse moisture content through time more accurately than the Hii et al model. Also, the results demonstrated that the selected ANN model and a number of semi-empirical models with less than 3 adjustable parameters provided good agreement and can be considered promising tools to predict drying kinetic of bagasse particles.