Comparison of two artificial intelligence methods ( ANNs and ANFIS ) for estimating the energy and exergy of drying cantaloupe in a hybrid infrared‐convective dryer

Abstract

In this investigation, energy and exergy analysis of a hybrid infrared-convective (IR-CV) dryer is presented for drying cantaloupe slices. The experiments were performed at three temperature levels (40, 55, and 70 °C), one level of air velocity (0.5 m/s), and three IR powers (250, 500, 750 W). The relationships between the input process parameters (IR power, input air temperature, and drying time) and the thermodynamic properties of the dried product (moisture ratio, drying rate, energy efficiency, exergy efficiency, and exergy loss) were modeled by implementing the artificial neural network (ANN) and ANFIS. Results indicated that high IR power and air temperature can shorten the drying time, meanwhile increasing the energy efficiency. Based on the obtained results, input air temperature and IR power highly affect the exergy efficiency. The highest exergy efficiency was obtained at the input air temperature of 70 °C and IR power of 750 W. The exergy loss was increased by increasing both parameters of the air temperature and IR power. Models developed using ANN and ANFIS indicated that the ANFIS model predicted the moisture ratio, energy efficiency and exergy loss better than the ANN model, as it estimated these thermodynamic parameters at a higher regression coefficient (>0.9889) than ANN (0.9850). While, the accuracy of the ANN model was better for predicting drying rate and exergy efficiency.