The aim of this article is to present a modern method of convective drying intensification caused by the external action of ultrasound. The purpose of this study is to discover the mechanism of ultrasonic interaction between the solid skeleton and the moisture in pores. This knowledge may help to explain the enhancement of drying mechanism affected by ultrasound, particularly with respect to biological products like fruits and vegetables. The experimental kinetics tests were conducted in a hybrid dryer equipped with a new ultrasonic generator. The drying kinetics curves determined on the basis of drying model developed by the author were validated with those by the ones obtained from experimental tests. The intensification of heat and mass transfer processes due to ultrasound induced heating effect and vibration effect are analysed. The obtained results allow to state that ultrasound makes drying processes more effective and enhance the drying efficiency of biological products without significant elevation of their temperature.

Evaluation of moisture absorption in foodstuffs such as black chickpea is an important stage for skinning and cropping practices. Water uptake process of black chickpea was discussed through normal soaking in four temperature levels of 20, 35, 50 and 65 °C for 18 hours, and then the hydration kinetics was predicted by Peleg’s model and finite difference strategy. Model results showed that with increasing soaking temperature from 20 to 65 °C, Peleg’s rate and Peleg’s capacity constant reduced from 13.368×10-2 to 5.664×10-2 and 9.231×10-3 to 9.138×10-3, respectively. Based on key results, a rise in the medium temperature caused an increase in the diffusion coefficient from 5.24×10-10 m2/s to 4.36×10-9 m2/s, as well. Modelling of moisture absorption of black chickpea was also performed employing finite difference strategy. Comparing the experimental results with those obtained from the analytical solution of the theoretical models revealed a good agreement between predicted and experimental data. Peleg’s model and finite difference technique revealed their predictive function the best at the temperature of 65 °C.