Establishing the kinetic regularities and energy efficiency of thermally-treated rice drying considering partial vapor pressure
Abstract
The process of drying thermally treated rice has been considered in this study for different energy supply techniques. The task addressed relates to the high energy intensity of convective drying, the duration of the process, as well as the limitations in existing models that do not take into account the partial pressure of steam as the driving force of the drying process. A bench has been designed for measuring the partial pressure of water vapor over the product based on the data from humidity and temperature sensors. The duration of the system reaching thermodynamic equilibrium is no more than 5 minutes. Accuracy in humidity measurement is ±2–5% RH, humidity measurement limits are 31...100% RH. The kinetics of change in the driving force for different drying techniques were studied. Approximate equations have been obtained that describe the change in the driving force and the kinetics of drying. Specific energy consumption was determined. It was found that the minimum energy consumption corresponds to infrared drying of boiled rice and is 4 MJ/kg, while the convective method is the most energy-intensive at 7.3–9.45 MJ/kg. An equation in similarity numbers (modified Stanton and Euler numbers) has been constructed, which includes the difference in partial water vapor pressures. Low energy consumption in drying boiled rice is explained by the destruction of the cellular structure of the grain during cooking. The energy minimum of infrared drying is due to the supply of heat to the rice by radiation without energy losses on air heating. Real-time measurement of the partial water vapor pressure was used according to the data from humidity and temperature sensors. The studies were conducted on heat-treated (boiled and steamed) rice without husks. The results could be used for making instant dry cereals and ready-made meals. The findings are a tool for scaling up drying equipment and optimizing operating modes