Abstract
To analyze the performance of a conical spouted bed dryer for the valorization of wine-production waste (grape skins, seeds, and stalks) in a jet-spouted-bed regime (dilute spouted bed), the operating conditions in the jet and spouted-bed regimes were delimited for comparison. Drying was conducted at different air temperatures and velocities in both regimes, and the time evolution of the gas humidity and solid moisture content was monitored to obtain the most appropriate conditions for drying. An artificial neural network was trained to predict the time evolution of gas humidity in both regimes in a conical spouted bed dryer. From these data, the solid moisture content was determined using differential mass and heat balances, considering equal temperatures in the gas and solid phases. Finally, the fitting of the experimental results of the solid moisture content demonstrates the validity of drying tracking from the time evolution of the air humidity.