Date of Graduation
Master of Science in Food Science (MS)
Griffiths G. Atungulu
Second Committee Member
Third Committee Member
Natural air (NA), in-bin drying and storage of rough rice generally maintains high grain quality, but the associated slow movement and occasional stagnation of the drying front during the process may result in problems of rice quality reduction, mold growth, and mycotoxin development, especially for rough rice in the top layers of the bin. Using modeling techniques to simulate in-bin rough rice drying in typically-encountered field scenarios may provide a tool for rapidly predicting the grain condition as drying progresses. The objectives for this study were to (1) investigate accurate models for predicting equilibrium moisture content (EMC) of rough rice at set conditions of air temperature and relative humidity (RH), (2) develop and validate a mathematical model for predicting moisture content (MC) and temperature profiles of rough rice during NA, in-bin drying, and (3) perform computer simulations using the developed mathematical model to determine the impacts of drying strategy (rough rice initial MC, drying-start date, air flowrate, and fan control strategy) on rough rice drying duration, maximum dry matter loss, and percent overdrying. In order to accomplish objective (1), adsorption and desorption isotherms of long-grain hybrid rough rice at temperatures ranging from 15°C to 35°C and RHs of 10% to 90% were determined by using a Dynamic Vapor Sorption analysis device. Non-linear fitting techniques were used to determine constants of models for predicting rough rice adsorption or desorption EMCs. It was determined that the modified Halsey and modified Chung-Pfost equations were the best models to describe rough rice adsorption and desorption isotherms, respectively (RMSEs = 0.54% MC in dry basis and 0.91% MC in dry basis, respectively). To achieve objective (2), Post-Harvest Aeration Simulation Tool - Finite Difference Method, developed by Bartosik and Maier (2004), was modified for rice and used to simulate in-bin rough rice drying in Arkansas. Simulation results were validated by field experiments, which used modern, on-farm bins equipped with “cabling and sensing technology” for in-bin RH and rough rice temperature measurement; the rough rice MC was calculated based on the measured RH and temperature data. The sensor-determined data and simulation results of MC and temperature were compared. The simulation results described well the general trends of rough rice MC and temperature profiles (for MC, mean RMSE = 0.56% MC in wet basis; for temperature, mean RMSE = 1.77°C). The study validated the accuracy of the developed simulation model for prediction of in-bin drying and storage of rough rice. In order to accomplish objective (3), simulations of in-bin drying of rough rice with different drying strategies was performed. A twenty-year weather data set (1995 to 2014) of ambient air temperature and RH of the U.S. Mid-South rice growing locations (Jonesboro, West Memphis, and Stuttgart, Arkansas, and Greenville and Tunica, Mississippi) were procured. Drying simulations were performed using air flowrates 0.55, 1.10, 1.65, and 2.20 m^3 min-t^-1, drying-start dates of 15 August, 15 September, and 15 October, and rough rice initial MCs of 16% to 22% (wet basis). Fan control strategies comprised running the drying fan continuously, at set window of natural air equilibrium moisture content, and air EMC window with supplemental heating option. Results showed that rough rice drying duration, dry matter loss, and percent overdrying were dependent on selected drying strategy with fan control strategy, initial rough rice MC, and air flowrate being key factors. Information generated using the simulations could guide rice producers, especially in selected U.S. Mid-South, to effectively dry rough rice in a timely manner, and mitigate problems of rice quality reduction, excessive mold growth, and mycotoxin contamination.
Zhong, Houmin, "A Simulation Platform for Accurate Prediction of In-bin Drying and Storage of Rough Rice" (2015). Theses and Dissertations. 1413.