Energy and exergy investigation of microwave assisted thin-layer drying of pomegranate arils using artificial neural networks and response surface methodology

Abstract

Energy and exergy analyses of thin-layer drying of sour pomegranate arils with microwave treatment were conducted in this research. Three levels of air temperature (50, 60 and 70 °C) and air velocity (0.5, 1 and 1.5 m/s) were tested for evaluation of dryer parameters. Energy utilization and energy utilization ratio increased with time, while exergy efficiency decreased with time. Application of microwave pretreatment to assist convective drying resulted in decreased energy utilization and drying time. Minimum exergy loss and exergy efficiency were also obtained using microwave pretreatment. Artificial neural networks (ANN) performed desirably in modeling energy and exergy criteria regarding input factors. Results showed that the training algorithm of back-propagation was suitable for predicting the drying parameters. It was also found that response surface methodology (RSM) predicted desirably the output parameters. Coefficient of determination (R2) values for regression of drying energy and exergy criteria based on input factors were obtained to be highly acceptable for both ANN and RSM models.