Small-scale farmers in developing Asian countries have minimal agricultural mechanisms available to them. In the Philippines, postharvest losses in rice production can reach about 36% in the drying process alone. Thus, the inflatable solar dryer (ISD) was developed through the collaboration of the University of Hohenheim, the International Rice Research Institute, and GrainPro Philippines Inc. Although the ISD was successfully tested with different agricultural products, further characterization of the ISD design is required for predicting the drying performance. To this end, the airflow behavior in the ISD was simulated using computational fluid dynamics (CFD) via ANSYS Fluent. Moreover, a thermal model was developed in MATLAB/Simulink by taking into account heat transfer in the heating area and coupled heat and mass transfer within the drying area. Three batches of drying experiments were performed and airflow measurements were taken inside the dryer to validate the models. The MATLAB/Simulink model was further used to predict the drying performance under various weather conditions spanning 10 years. The simulated temperatures and moisture content in the ISD showed high accuracy (mean absolute percentage error (MAPE) < 10%) with the experimental data. The proposed dynamic model provides an efficient computational tool that can be applied to predict the drying performance and to optimize the ISD design.
The sun drying of agricultural products is a complicated process involving heat transfer, mass transfer, and variable weather conditions. Surface reflection (albedo), a crop’s radiative property, plays an essential role in energy balance, and understanding its contribution can improve the thermal analysis. In this study, field experiments were conducted in the Philippines to explore the influence of surface albedo on the sun drying of paddy rice. First, we implemented energy and mass balance equations in a transient model with the surroundings using a graphical programming language in Matlab/Simulink®. Second, we identified the influence of albedo on the sun drying model by using a sensitivity analysis. Third, we investigated the relationship of paddy rice albedo and the solar zenith angle. Lastly, we integrated the albedo function into the sun drying model. The simulation outputs were validated with field experiments. A better estimation of the measured exit temperature and instantaneous mass were obtained when a variable albedo was applied. This study makes clear that introducing a variable albedo has a positive impact on model improvement. This information is important for application in solar drying technologies, so that the drying process can be better assessed.
The growing market for dried fruits requires more attention to quality parameters. Mango and other tropical fruits are commonly dried at temperatures ranging from 40 °C to 80 °C. Convincing evidence suggests that the nutritional quality of dried fruits is best preserved when dried at low temperatures ≤50 °C, whereas increasing drying temperatures lead to the degradation of the most valuable nutrients inside the fruit. Currently, there is no system or direct measurement method that can assist in identifying the quality deterioration of dried fruits caused by excessive heat exposure during drying. From this perspective, the activity of the heat-sensitive enzyme ‘catalase’ was used for the first time to evaluate and compare mango slices dried at 40 °C, 60 °C and 80 °C. Various methods, including direct and indirect flotation tests and spectrophotometric measurements, were explored to measure the residual catalase activity in the dried samples. Results showed that the spectrophotometry and indirect flotation test produced the best results, revealing a significant difference (p < 0.05) in the catalase activity of mango slices dried at 40 °C, 60 °C and 80 °C, which the direct-dried mango flotation test failed to predict. Furthermore, this study demonstrates the potential applicability of catalase activity to indicate heat stress in dried mango slices processed at different temperatures.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.