Citrus, especially orange, are of great important among agricultural products in the world. In this study thin-layer drying of orange (var. Thompson) was modeled using artificial neural network (ANN). An experimental dryer was used. Thin-layer of orange slices at five air temperatures (40, 50, 60, 70 & 80 ºC), three air velocities (0.5, 1 & 2 m/s) and three thicknesses (2, 4 & 6 mm) were artificially dried. Initial M.C. during all experiments was between 5.4 to 5.7 (g/g) (d.b.). Mass of samples were recorded and saved every 5 sec. using a digital balance connected to a PC. MLP with momentum and LM were used to train the ANNS. In order to develop ANN's models, temperatures, air velocity and time are used as input vectors and moisture ration as the output. Results showed a 3-6-1 topology for thickness of 2 mm, 3-7-1 topology for thickness of 4 mm and 3-5-1 topology for thickness of 6 mm, with LM algorithm and TANSIG activation function were able to predict moisture ratio withof  0.99906, 0.99919 and 0.99930 respectively. The corresponding MSE for this topology were 0.00013, 0.00012 and 0.00009 respectively.

Keywords: Orange (var. Thompson), Thin-layer, artificial neural network, Momentum, Levenberg-Marquardt

Full-Text [PDF 194 kb]   (3237 Downloads)