Using comsol multiphysics in study of beebread drying modes

Abstract

One of the most important bee products for humans is beebread, as it has dietary and healing properties. After gathering, beebread has a high humidity -about 30% and it must be dried to 18% state that it can be stored. It is usually dried in convective drying installations. However, most dryers have a common disadvantage -there is a large spread in the drying time between the trays filled with beebread. The purpose of these studies was to intensify the drying process in the first period due to the short-term supply of additional thermal energy. The arrival of increased heat in the first period will allow the source material to heat up faster to the set temperature and start the evaporation process from the surface earlier. The main task was without changing the design of the dryer to recommend a new mode of operation, which would reduce the spread of humidity parameters between the trays. A geometric model of the object of research has been developed for its introduction into the Comsol software product. A mathematical model of the main physical processes occurring in the drying chamber is obtained, which makes it possible to analyze the main parameters that affect the drying rate. The modeling was carried out in two stages. At the first stage, the normal operation mode was investigated with three variants of the supplied thermal power of 300 W, 340 W, and 400 W. At the second stage, a new regime was investigated. It consists in a temporary summing up of the thermal power equal to 500 W for 2 hours and returning to the previous power in all three variants. As a result of the analysis of the obtained data, it was found that the drying time in all modes is from 22 to 30 hours. However, the humidity spread between the upper and lower trays reaches up to 13% with a constant power supply mode, whereas with a temporary power supply only up to 7% of humidity. In addition, in the normal mode, the difference in drying time between the extreme trays was from 26 h to 20 h, when applying increased power, the difference is 7 h. Thus, there is a more uniform drying process for all the trays.

Enhancing Grain Moisture Prediction with Artificial Neural Networks and Computational Fluid Dynamic

“…For instance, Zeineldin [26] successfully simulated the temperature and residual moisture content of stationary textiles in a drum dryer. Likewise, Oskin et al [27] delved into the convection drying of bee loaves, and Kumar et al [28] focused on modelling the convective drying of food products, specifically bananas. Their collective findings underscored the software's efficacy in modelling complex drying processes.…”