Subodh Kumar

TL;DR: In this paper, the application of artificial neural network (ANN) for prediction of temperature variation of food product during solar drying is investigated, where the important climatic variables namely, solar radiation intensity and ambient air temperature are considered as the input parameters for ANN modeling.

TL;DR: In this paper, a methodology is proposed for the determination of these coefficients using variable drying parameters (lag factor, k 0 and drying coefficient, k ) mixed-mode solar dryer with potato cylinders and slices was used to obtain variable (temperature dependent) k0 and k from drying kinetics.

TL;DR: In this paper, a method based on energy balance considering the effects of heat capacity of the food product, radiative heat transfer from food product to the drying chamber and solar radiation absorbed in the product during drying is proposed for determination of convective heat transfer coefficient, h c.

TL;DR: In this article, a methodology for the determination of temperature dependent drying parameters namely drying constant and lag factor from the experimental drying kinetic curves of food product is proposed, and linear and exponential correlations for these parameters are proposed to represent their variation as a function of sample temperature.

TL;DR: In this article, the authors performed rehydration experiments at the temperatures of 40°, 50°, and 60°C with potato samples under mixed-mode solar dryer and open air sun.