Investigation the Experimental Method and Modeling for Drying of Parsley Bulk by Using Artificial Neural Network

In this study, the single-leaf of parsley and also bulk of particles at temperatures of 46, 58, and 68°C and the thickness of 0.13 to 0.17 mm and the period from 0 to 140 minutes in a laboratory fixed bed dryer dried. Also, the single leaf of parsley and the mass dried in the sun. To dry the parsley in the dryer, five samples were considered for each temperature and the weight of samples during the drying process is continuously recorded until changes in the volume of samples to zero. Then the influence of parameters such as the temperature of the drying rate is examined. Equilibrium time and humidity were observed to be 140 minutes and 0.0003, respectively. Also, the process kinetics curve shows that more drying occurs in the descending phase due to the low external resistance to mass and heat transfer. In studying the effect of temperature, it was observed that with increasing temperature from 46 to 68℃, parsley reaches equilibrium humidity with a greater slope. Then drying process of a single leaf, and parsley mass, using feed-forward neural networks and leading, was modeled and the results of the neural network with the results obtained from experimental data were compared. The best result by a feed-forward neural network for single-leaf parsley with Levenberg - Marquardt algorithm, and 13 neurons and the coefficient of determination 0.9987 and parsley mass with nine neurons optimized and coefficient 0.9993obtainedwhich demonstrates the high accuracy of the artificial neural network.