Energy and Quality Attributes of Combined Hot-Air/Infrared Drying of Paddy

Abstract

The kinetics of combined hot-air/infrared thin-layer drying of paddy was studied. The mechanical quality aspects of paddy kernels dried at different drying conditions were evaluated in terms of percentage of cracked kernels and also required failure force obtained from bending tests. The well-known Artificial Neural Network (ANN) modeling technique was applied to predict the drying time, variations in paddy moisture content, the percentages of cracked kernels, and the values of required failure force of paddy at different drying conditions. The best ANN topologies, transfer functions, and training algorithms were determined for prediction of the mentioned parameters. In addition to the product quality aspects, the specific energy consumption (SEC) was estimated for all drying conditions. The results indicated that application of a low-intensity IR radiation (2000 W/m²), together with lower values of inlet air temperature (30°C) and moderate values of inlet air velocity (0.15 m/s), can effectively improve the final quality of paddy (as a heat-sensitive product) with a reasonable SEC.

Keywords:

• Artificial neural network (ANN)
• Combined hot-air/IR drying
• Paddy quality
• Specific energy consumption (SEC)