Evaluation of drying behavior and characteristics of potato slices in multi–stage convective cabinet dryer: Application of artificial neural network

Abstract

The inconsistency in the quality of dried products at different coordinates within a conventional multi-stage convective cabinet dryer is a critical but often neglected problem. In this study, the drying behavior (moisture ratio) occurring in each drying tray layer and the drying characteristics (shrinkage or area ratio) occurring at different coordinates within a multi-stage convective cabinet dryer was assessed. Potato slices were used as raw materials in the drying process. Experiments were carried out by varying three different hot air velocities and two different drying temperatures. It was found that under the same hot air temperature and air velocity, the change in moisture content in each drying tray and the shrinkage in each coordinate of the potato slices were different. Artificial neural network model was used to predict the moisture ratio and the area ratio of the potato slices based on the experimental data. The moisture ratio obtained from the experiment was evaluated by comparing it with the drying model. The results showed a good confidence level with the coefficient of determination in the range of 0.962 7–0.993 3. The shrinkage analysis was based on the photographic data taken through image processing before usage as the output data for the predictive model. The predictive model was designed to have various architectures with different parameters; both hidden layer and hidden layer size, learning rate, training cycles, sampling type and split ratio. The best moisture ratio and area ratio model provided the coefficient of determination of 0.996 and 0.970, respectively.