Modelling Heat and Mass Transfer in High-Capacity Natural Convection Solar Dryers

Abstract

Predicting solar dryer performance under different environmental conditions or assessing their performance to dry different grains is challenging since repeatable full-scale tests are expensive and time consuming. In the present study, computational fluid dynamics approach was used to model the drying of maize in high-capacity dryers such as greenhouse and solar bubble dryer. The absorption of short-wave radiation and the greenhouse effect in the dryer with incident solar radiation was modelled using a dual-band spectrum. The distribution of airflow, temperature, and absolute humidity was analysed in this study to optimise the drying process of maize. Additionally, these results were also used to quantify the drying rate of both greenhouse and solar bubble dryer. The greenhouse dryer model overpredicted the dryer temperatures by an average of 0.12%, and overpredicted absolute humidity by 0.38 per cent. The average Root-Mean-Square Error (RMSE) of temperature prediction was 1.8 °C, and the average RMSE for absolute humidity was 0.0042 for the greenhouse model. On the other hand, the solar bubble dryer model underpredicted temperatures by 1.7%, and underpredicted humidity values by 0.3 per cent. The mean absolute percentage error for the temperature and absolute humidity prediction of the solar bubble dryer model was 1.69% and 0.28%, respectively. The predicted and observed spatial variation in the temperature was similar for both dryers.