Thin-layer drying model of jackfruit using artificial neural network in a far infrared dryer
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Abstract
The purpose of this article was to find the optimal model to illustrate the drying behaviors of jackfruit in a far-infrared (FIR) dryer and to examine the drying characteristics. The drying conditions were operated at drying temperatures of 60, 70 and 80 oC. In the empirical models, the Newton, Page, Modified Page, Midilli et al., Two term exponential, Henderson and Pabis, Logarithmic, and Wang and Singh model, were investigated to find the most suitable model. An artificial neural network model was also studied, with drying temperature and time selected as input variables, and MR values selected as output parameters. The dependability of the model was assessed using the R2,, RMSE and r statistical criteria. The results showed that for the empirical model, the Page model offered excellent results, while the optimal ANN structure was identified as 2-12-1 with Tan-sigmoid transfer functions.
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