In this study, different drying conditions were investigated on quality and thermodynamic properties of almond kernel. Experiments were performed using a convection dryer with ultrasound pretreatment in 40, 50, 60, and 70 °C air temperature, 1 m/s air velocity, and duration of ultrasonic pre‐treatment of 0 min (for control sample), 10, 20, and 40 min. The drying kinetic of the almond kernel was estimated by 15 mathematical models. Furthermore, Artificial Neural Networks (ANNs) and Adaptive Neuro‐Fuzzy Inference Systems (ANFIS) were applied to fit the experimental data on the thin layer drying. The lowest and highest values of the effective moisture diffusivity (Deff) was 1.81 × 10−9 and 9.70 × 10−9 m2/s, respectively. Activation energy (Ea) of the samples was obtained between 26.35 and 36.44 kJ/mol. The highest and lowest values of specific energy consumption (SEC) were calculated 561.72 and 169.88 kW hr/kg, respectively. Maximum (13.14%) and the minimum (7.1%) values of shrinkage were achieved at air temperatures of 70 and 40 °C, respectively. The color changing of dried samples was obtained between 9.14 and 17.96. Furthermore, results revealed that the ANFIS model had the high ability to predict the moisture ratio (R2 = 0.9998 and MSE = 0.0003) during drying. As a result, ANFIS model has the highest ability to evaluate all output as compared with other models and ANNs method. Practical applications Algorithms are modern methods that have been successfully applied to solve the various problems and modeling in engineering and science. Drying is one of the oldest procedures to preserve the food quality. Reduction of moisture content to a certain value can be caused to decay and minimize the microbiological activity and deteriorating chemical reactions in agricultural products, respectively. Determination of almond drying process under convective with ultrasound pre‐treatment dryer in terms of desirable thermal properties (effective moisture diffusivity and energy consumption) provides the high‐quality products. Furthermore, this research can be able to provide a technical basis for almond drying and the related equipment designing.

No abstract

This study aimed to predict the drying kinetics, energy utilization (E u ), energy utilization ratio (EUR), exergy loss, and exergy efficiency of quince slice in a hot air (HA) dryer using artificial neural networks and ANFIS. The experiments were performed at air temperatures of 50, 60, and 70°C and air velocities of 0.6, 1.2, and 1.8 m/s. The thermal parameters were determined using thermodynamic relations. Increasing air temperature and air velocity increased the effective moisture diffusivity (D eff ), E u , EUR, exergy efficiency, and exergy loss. The value of the D eff was varied from 4.19 × 10 -10 to 1.18 × 10 -9 m 2 /s. The highest value E u , EUR, and exergy loss and exergy efficiency were calculated 0.0694 kJ/s, 0.882, 0.044 kJ/s, and 0.879, respectively. Midilli et al. model, ANNs, and ANFIS model, with a determination coefficient (R 2 ) of .9992, .9993, and .9997, provided the best performance for predicting the moisture ratio of quince fruit. Also, the ANFIS model, in comparison with the artificial neural networks model, was better able to predict E u , EUR, exergy efficiency, and exergy loss, with R 2 of .9989, .9988, .9986, and .9978, respectively. K E Y W O R D S adaptive neuro-fuzzy inference system, artificial neural networks, drying, quince, thermodynamic parameters | 595 ABBASPOUR-GILANDEH Et AL.focusing on energy and exergy analysis is very important (Lingayat, Chandramohan, & Raju, 2018;Yogendrasasidhar & Setty, 2018).Şevik, Aktaş, Dolgun, Arslan, and Tuncer (2019) analyzed the exergy and energy in the process of drying mint and apple slices in a solar and solar-infrared. The results indicated that the loss of exergy and exergy efficiency increases by increasing the air temperature. Exergy efficiency for mint in solar dryer and solar-infrared was 69.35% and 59. 07%, respectively. Akpinar, Midilli, and Bicer (2006) analyzed the energy and exergy in pumpkin. They reported that the pumpkin dried within the time range of 5.66-12 hr with a loss of exergy from 0 to 1.165 kJ/s. The maximum exergy of the system input was 2.198 kJ/s. Also, with increased exergy loss, the energy used in the solar dryer increased. Karthikeyan and Murugavelh (2018) studied the energy and exergy required to dry turmeric in a mixed-mode forced convection solar tunnel dryer and concluded that the loss of exergy and energy utilization ratio and its efficiency was increased with increasing temperature.

In this study, the drying kinetics of walnuts was investigated including specific energy consumption (SEC), shrinkage, and walnut kernels' color in two dryers, namely ultrasonic‐convection (US‐CON) and ultrasonic‐microwave (US‐MIC). To carry out this study, walnut samples were dried at three MIC powers (270, 450, and 630 W), three input air temperatures (40, 55, and 70°C) and four duration of ultrasonic pre‐treatment (0 min (control sample), 10, 20, and 40 min). Fifteen mathematical models were used to describe the walnuts' moisture ratio. The results showed that the Midilli et al. model was the best model for describing the process of drying walnuts in US‐CON and US‐MIC dryers. Effective moisture diffusivity (Deff) values were calculated to be between 2.77 × 10–9 and 5.56 × 10–9 m2/s for the US‐CON dryer and 3.12 × 10–9 to 8.99 × 10–9 m2/s for the US‐MIC dryer. The lowest rates of color change were observed in the US‐MIC dryer at the lowest microwave power and the lowest duration of ultrasonic pre‐treatment. The lowest SEC for the US‐CON dryer was 19.52 kWh/kg, and it was 15.90 kWh/kg for the US‐MIC dryer. The highest rate of shrinkage was 14.21%, which was seen in the US‐CON dryer at the air temperature of 70°C and the duration of ultrasonic pre‐treatment of 40 min. According to the results, the best quality of walnut with the lowest changes of color and shrinkage in the microwave dryer was calculated at microwave power 270 W and control sample. Practical applications Drying foods and agricultural products using microwave dryers is a relatively cheap method and has attracted the attention of many researchers today. Also, numerous studies have been conducted on drying agricultural products through microwave and convection dryers with ultrasound pre‐treatment. The result of this study could be applied for gathering comprehensive information regarding the drying process (kinetic, Deff, Ea, and SEC) as well as the qualitative properties (color and shrinkage) of walnut kernels dried using the microwave and convective hot air dryers accompanied by ultrasound pre‐treatment.

No abstract