An Investigation of Drying Process of Shelled Pistachios in a Newly Designed Fixed Bed Dryer System by Using Artificial Neural Network


Balbay A., Sahin Ö. , Karabatak M.

DRYING TECHNOLOGY, vol.29, no.14, pp.1685-1696, 2011 (Journal Indexed in SCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 29 Issue: 14
  • Publication Date: 2011
  • Doi Number: 10.1080/07373937.2011.600843
  • Title of Journal : DRYING TECHNOLOGY
  • Page Numbers: pp.1685-1696
  • Keywords: Artificial neural network, Drying, Heat transfer, Pistachio, MOISTURE DIFFUSIVITY, RESPONSE-SURFACE, CARROT CUBES, ENERGY, QUALITY, PREDICTION, KINETICS, EXERGY, SLICES, ANN

Abstract

In this paper, the drying of Siirt pistachios (SSPs) in a newly designed fixed bed dryer system and the prediction of drying characteristics using artificial neural network (ANN) are presented. Drying characteristics of SSPs with initial moisture content (MC) of 42.3% dry basis (db) were studied at different air temperatures (60, 80, and 100 degrees C) and air velocities (0.065, 0.1, and 0.13 m/s) in a newly designed fixed bed dryer system. Obtained results of experiments were used for ANN modeling and compared with experimental data. Falling rate period was observed during all the drying experiments; constant rate period was not observed. Furthermore, in the presented study, the application of ANN for predicting the drying time (DT) for a good quality product (output parameter for ANN modeling) was investigated. In order to train the ANN, experimental measurements were used as training data and test data. The back propagation learning algorithm with two different variants, so-called Levenberg-Marguardt (LM) and scaled conjugate gradient (SCG), and tangent sigmoid transfer function were used in the network so that the best approach can be determined. The most suitable algorithm and neuron number in the hidden layer are found out as LM with 15 neurons. For this number level, after the training, it is found that Root-mean squared (RMS) value is 0.3692, and absolute fraction of variance (R-2) value is 99.99%. It is concluded that ANNs can be used for prediction of drying SSPs as an accurate method in similar systems.