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The objective of the study was to investigate the influence of high power ultrasound on a laboratory-scale fluidized bed shelled corn dryer. The drying time, moisture content variation, specific energy consumption and quality parameters including ultimate compressive strength, toughness, shrinkage and color of corn kernels were investigated. Furthermore, Artificial Neural Network (ANN) simulation models were developed for predicting the drying variables. Machine vision techniques were used to determine color and shrinkage as qualitative indices. Results showed that the lower frequencies had better penetrations at lower temperatures and cause a significant reduction in drying time. In addition, the ultrasound application led to reduction of ultimate compressive strength and toughness of the dried samples although ultrasound has non-thermal character as the subsidiary factor, it plays an important role in shrinkage and color specification. Based on error analysis results, the prediction capability of ANN model is found to be reasonable for the developed models. Application of ultrasound significantly decreased the specific energy consumption (SEC) of drying process at the optimal drying condition.

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