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In the study, drying process of quince fruit was accomplished in a microwave‐convective dryer (MCD). The experiments were carried out at microwave power levels of 100, 200, and 300 W, air temperatures of 40, 55, and 70°C, and air velocities of 0.5, 1, and 1.5 m/s. Nevertheless, three artificial intelligence techniques consisted of artificial neural networks (ANNs), particle swarm optimizer (PSO), and grey wolf optimizer (GWO) were evaluated to predict the parameters of Deff, SEC, ΔE, and Sb. In the evaluation the data by ANNs, input parameters of networks consisted the values of air temperature, microwave power, and air velocity. According to the results, the maximum values of effective moisture diffusivity ( Deff) and specific energy consumption (SEC) were 1.71 × 10−9 m2/s and 126.07 kWh/kg, respectively. In addition, minimum values of total change in color (ΔE) and shrinkage ( Sb) of quince achieved 10.85 and 33.85%, respectively. For predicting all parameters, three models used in the study represented good predictive capability with R2 > 0.97. The obtained results showed that the GWO model had better predictive performance than the ANN and PSO models. Practical Application Drying food and agricultural products by application of microwave‐hot air blend dryers can be a good alternative to hot air and microwave dryers. Microwave energy infiltrates the product and facilitates heat release from the product and thus reduces drying time compared to single dryers. The main aim of applying such different models, mathematical simulation or modeling in the drying technology of agricultural products is to transform physical qualities and their interactions into numerical quantities and mathematical relationships.

Golpour I., Parian J.A., Chayjan R.A. (2014): Identification and classification of bulk paddy, brown, and white rice cultivars with colour features extraction using image analysis and neural network. Czech J. Food Sci., 32: 280-287.We identify five rice cultivars by mean of developing an image processing algorithm. After preprocessing operations, 36 colour features in RGB, HSI, HSV spaces were extracted from the images. These 36 colour features were used as inputs in back propagation neural network. The feature selection operations were performed using STEPDISC analysis method. The mean classification accuracy with 36 features for paddy, brown and white rice cultivars acquired 93.3, 98.8, and 100%, respectively. After the feature selection to classify paddy cultivars, 13 features were selected for this study. The highest mean classification accuracy (96.66%) was achieved with 13 features. With brown and white rice, 20 and 25 features acquired the highest mean classification accuracy (100%, for both of them). The optimised neural networks with two hidden layers and 36-6-5-5, 36-9-6-5, 36-6-6-5 topologies were obtained for the classification of paddy, brown, and white rice cultivars, respectively. These structures of neural network had the highest mean classification accuracy for bulk paddy, brown and white rice identification (98.8, 100, and 100%, respectively).

In this research, a comparative approach was carried out between artificial neural networks (ANNs) and response surface methodology (RSM) to optimize the drying parameters during infrared-convective drying of white mulberry. The drying experiments were performed at different air temperatures (40, 55 and 70 °C), air velocities (0.4. 1 and 1.6 m/s) and infrared radiation power (500, 1000 and 1500 W). RSM focuses on maximization of effective moisture diffusivity ( eff D ) and minimization of specific energy consumption ( SEC ) in the drying process. The optimized conditions were encountered for the air temperature of 70 °C, the air velocity of 0.4 m/s and the infrared power level of 1464.57 W. The optimum values of eff D and SEC were 1.77×10 -9 m 2 /s and 166.554 MJ/kg, respectively, with the desirability of 0.9670. Based on the statistical indices, the results showed that the Feed and Cascade Forward Back Propagation neural systems with application of Levenberg-Marquardt training algorithm and topologies of 3-20-20-1 and 3-10-10-1 were the best neural models to predict eff D and SEC , respectively.

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This study aimed to examine the energy and exergy indices of the rosemary drying process in a hybrid-solar dryer (HSD) and the effects of air-drying parameters on these thermodynamic indices. Drying experiments were carried out at four levels of air temperature (40, 50, 60, and 70 ∘C) and three levels of air velocity (1, 1.5, and 2 m/s). Energy and exergy were calculated by application of the first and second laws of thermodynamics. Based on the principal laws, energy efficiency, exergy losses, and exergetic improvement potential rate, were evaluated. The results showed that the energy utilization ratio (EUR) ranged from 0.246 to 0.502, and energy utilization (EU) ranged from 0.017 to 0.060 (kJ/s). Exergy loss and efficiency varied from 0.009 to 0.028 (kJ/s) and from 35.08% to 78.5%, respectively, and increased with increased temperature and air velocity. It was found that the exergy loss rate was affected by temperature and air velocity because the overall heat transfer coefficient was different under these conditions. By comparison, with increasing temperature and air velocity, the exergy efficiency increased. Because most energy is used to evaporate moisture, this behavior may be explained by improved energy utilization. The drying chamber sustainability index ranged from 0.0129 to 0.0293. This study provides insights into the optimization process of drying operations and operational parameters in solar hybrid dryers that reduce energy losses and consumption.

In this study, the drying kinetics, effective moisture diffusivity (D eff), specific energy consumption (SEC), colour, and shrinkage (S b) of pomegranate arils were compared when dried by convective (CV) drying and microwave (MW) drying. The experiments were performed at air temperature of 50, 60, and 70°C and air velocity of 1 m/s for CV drying and 270, 450, and 630 W for MW drying. The results showed that increasing air temperature and MW power increased the D eff. The calculations demonstrated that the maximum D eff for pomegranate arils was obtained for MW drying (630 W). Maximum SEC for pomegranate arils in the CV dryer was 145.12 kWh/kg, whereas in the MW dryer was 35.42 kWh/kg. In MW dryer, the lowest values of colour change and shrinkage were 6.77 and 50.5%, respectively. Comprehensive comparison of the different drying methods (MW and CV) revealed that MW drying had best drying performance for pomegranate arils, considering the drying time, effective moisture diffusion, SEC, colour, and shrinkage.