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Background. Drying of terebinth fruit was conducted to provide microbiological stability, reduce product deterioration due to chemical reactions, facilitate storage and lower transportation costs. Because terebinth fruit is susceptible to heat, the selection of a suitable drying technology is a challenging task. Artificial neural networks (ANNs) are used as a nonlinear mapping structures for modelling and prediction of some physical and drying properties of terebinth fruit. Materiał and methods. Drying characteristics of terebinth fruit with an initial moisture content of 1.16 (d.b.) was studied in an infrared fluidized bed dryer. Different levels of air temperatures (40,55 and 70°C), air velocities (0.93, 1.76 and 2.6 m/s) and infrared (IR) radiation powers (500, 1000 and 1500 W) were applied. In the present study, the application of Artificial Neural NetWork (ANN) for predicting the drying moisture diffusivity, energy consumption, shrinkage, drying rate and moisture ratio (output parameter for ANN modelling) was investigated. Air temperature, air velocity, IR radiation and drying time were considered as input parameters. Results. The results revealed that to predict drying rate and moisture ratio a network with the TANSIG- -LOGSIG-TANSIG transfer function and Levenberg-Marquardt (LM) training algorithm made the most accurate predictions for the terebinth fruit drying. The best results for ANN at predications were R2 = 0.9678 for drying rate, R2 = 0.9945 for moisture ratio, R2 = 0.9857 for moisture diffusivity and R2 = 0.9893 for energy consumption. Conclusion. Results indicated that artificial neural network can be used as an altemative approach for modelling and predicting of terebinth fruit drying parameters with high correlation. Also ANN can be used in optimization of the process.
Background. Modelling moisture diffusivity of pomegranate cultivars is considered to be a major aspect of the drying process optimization. Its goal is mainly to apply the optimum drying method and conditions in which the final product meets the required standards. Temperature is the major parameter which affects the moisture diffusivity. This parameter is not equal for different cultivars of pomegranate. So modelling of moisture diffusivity is important in designing, optimizing and adjusting the dryer system. Material and methods. This research studied thin layer drying of three cultivars of pomegranate seeds (Alak, Siah and Malas) under fixed, semi fluidized and fluidized bed conditions. Drying process of samples was implemented at 50, 60, 70 and 80°C air temperature levels. Second law of Fick in diffusion was utilized to compute the effective moisture diffusivity (Z) ) of the seeds. Linear and artificial neural networks (ANNs) also were used to model D „of seeds. Results. Maximum and minimum values of the D were related to malas and alak cultivars, respectively. Three linear models were found to fit the experimental data with average R2 = 0.9350, 0.9320 and 0.9400 for Alak, Siah and Malas cultivars, respectively. The best results for neural network were related to feed forward neural network with training algorithm of Levenberg-Marquardt was appertained to the topology of 3-4-3-1 and threshold function of LOGSIG. By the use of this structure, R2= 0.9972 was determined. Conclusion. A direct relationship was found between Deff „and thickness of fleshy section of the seeds. The Siah cultivar has the highest value of Deff. This is due to higher volume of fleshy section of the siah cultivar. Cultivar type and air velocity have the highest and the least effect on Deff respectively.
Background. The main goal in cantaloupe seed drying is the reduction of its moisture content to a safe level, allowing storage in a long period of time. Fluidized bed dryer is a drying process with better heat and mass transfer and shorter drying time. This method is a gentle and uniform drying procedure. Fluidized bed is suitable for sensitive and high moisture materials. Drying parameters of moisture diffusivity and energy are vitally important in modelling and optimizing of the seed dryer system. Material and methods. This study investigated thin layer characteristics of cantaloupe seeds under fixed, semi fluidized and fluidized bed drying with initial moisture content about 61.99% (d.b.). A laboratory fluidized bed dryer was utilized in this research. Air temperature levels of 45, 55, 65 and 75°C were applied in drying experiments. Effective moisture diffusivity (Z)efr) of cantaloupe seeds was computed by Fick’s second law in diffusion. Activation energy and specific energy consumption of cantaloupe seeds under different drying conditions were calculated. Results. Calculated values of Deff for drying experiments were in the range of 2.23TO40 and 8.61 -10'10 m2/s. Values of Deff increased as the input air temperature increased. Activation energy values were computed between 39.21 and 37.55 kJ/mol for 45°C to 75°C, respectively. Specific energy consumption for cantaloupe seeds was calculated at the boundary of 1.58 105 and 6.18105 kJ/kg. Conclusion. Results indicated that applying the fluidized bed condition is morę effective for convective drying of cantaloupe seeds. Increasing air velocity tends to decrease in activation energy. Decreasing in drying air temperaturę in different bed conditions caused increase in the energy value. The aforesaid drying parameters are necessary to optimize the operational condition of fluidized bed dryer and to perfect design of the system.
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