This research aimed to investigate the process of pistachios solar drying in two different conditions of the designed dryer (recycle and without recycle modes). As well, in this study, the drying kinetics curves of pistachio were drawn and its dewatering behavior was monitored by empirical models. Furthermore, the mass transfer rate (MTR) of the examined product during drying in two noted modes was determined by Fick’s 2nd law. Maximum, minimum, and average values of collector output air temperature were recorded 54.20, 32.30, and 42.99oC & 47.10, 30.70, and 40.03oC in air recycle and without air recycle modes, respectively. The results dedicated that, 18.75 % saving in drying time was obtained for air recycle mode compared with without air recycle mode. Also, the obtained data revealed that the drying rate of pistachio nut under recycle mode was higher than that of pistachio nut under without recycle mode, especially during the first hours of drying. Moreover, the empirical modeling showed that the Parabolic and Diamante et al. models were found to be the most suitable for describing drying curve of the thin layer solar drying process of whole pistachio nut under recycle and without recycle modes, respectively. In addition, the highest value of effective moisture diffusivity was observed in solar dried pistachio nut under air recycle mode with the Deff value of 2.26×10-6 m2/h (R2=0.9265). پرونده مقاله

The physical properties of almond kernel are necessary for the proper design of equipment for transporting, drying, processing, sorting, grading, and storage this crop. In this study, different models of ANNs with different activation functions were used to forecast surface area, volume, mass, and kernel density of almond. The results showed that multilayer perceptron network with tanh-tanh activation function as a goodness activation function can be estimated surface area, volume, mass, and kernel density with R2 value 0.983, 0.986, 0.981, and 0.982, respectively. Furthermore, the physical properties were fitted by regression relationships, the result showed linear regression method can be predicted surface area, volume, mass and kernel density with R2 value 0.979, 0.961, 0.945, and 0.791, respectively. Generally, the result showed neural network model had high ability to forecast the physical properties of almond than the linear regression method. پرونده مقاله