Drying Time, Energy and Exergy Efficiency Prediction of Corn ( Zea mays L.) at a Convective-Infrared-Rotary Dryer: Approach by an Artificial Neural Network

Energy consumption in the drying industry has made drying an energy-intensive operation. In this study, the drying time, quality properties (color, shrinkage, water activity and rehydration ratio), specific energy consumption (S.E.C), thermal, energy and exergy efficiency of corn drying using a hybrid dryer convective-infrared-rotary (CV-IR-D) were analyzed. In addition, the energy parameters and exergy efficiency of corn were predicted using the artificial neural network (ANN) technique. The experiments were conducted at three rotary rotation speeds of 4, 8 and 12 rpm, drying temperatures of 45, 55 and 65 °C, and infrared power of 0.25, 0.5 and 0.75 kW. By increasing drying temperature, infrared power and rotary rotation speed, the drying time, S.E.C and water activity decreased while the D eff , energy, thermal and exergy efficiency increased. In addition, the highest values of rehydration ratio and redness (a*) and the lowest values of shrinkage, brightness (L*), yellowness (b*) and color changes (ΔE) were obtained at an infrared power of 0.5 kW, air temperature of 55 °C and rotation speed of 8 rpm. The range of changes in S.E.C, energy, thermal and exergy efficiency during the corn drying process was 5.05–28.15 MJ/kg, 3.26–29.29%, 5.5–32.33% and 21.22–55.35%. The prediction results using ANNs showed that the R for the drying time, S.E.C, thermal, energy and exergy data were 0.9938, 0.9906, 0.9965, 0.9874 and 0.9893, respectively, indicating a successful prediction.