| The objectives of this study were: (1) to develop a mass transfer coefficient for natural air drying of rough rice as a function of drying parameters, (2) to develop a mathematical model and model systems for rough rice handling, drying and storage systems, (3) to develop a new approach for design in optimum systems by MODM method, and (4) to develop the optimum systems for various farm sizes.; To accomplish the first objective, rice drying tests of long grain were conducted under the controlled natural air drying conditions. Three levels of drying parameters which were drying air temperature, relative humidity and airflow rate were tested in the experiments. Then, mass transfer coefficients were evaluated from the results of the drying test, and a function was developed for mass transfer coefficient by multiple regression analysis. After evaluation of mass transfer coefficient, the results of drying tests were compared with simulation results in the simulation program which was RICEDRY modified from KSU-DRYER (Maurer, 1977). Then, the compared results were analyzed statistically, and there was not a significant difference between the test results and the simulation results.; For the second objective, systems analyses of rough rice handling, drying and storage were performed. This analysis included four receiving systems, two loading or elevating systems, and six drying and storage systems. Catalogs of grain conditioning and storage equipment, and facility planning manuals were collected from manufacturers and carefully studied. Then model systems were developed for grain drying, storage, receiving, elevating, and unloading. They consisted of number of systems, subsystem, number of subsystems, and considerations of energy and grain damage. Also, a mathematical model was developed, which included price model, energy model, grain damage model, and general multiple objective problem. In this modeling, list prices obtained from manufactures were incorporated into the price model. The general multiple objective problem was formulated to design the optimum systems with multiple conflicting objectives and system constraints.; To fill the third objective, nonlinear goal programming was introduced and the multiple objective design problem was formulated for this method. Then, NEWINGP program was used to solve the nonlinear goal programming problems, which was developed by Hwang and Paidy (1979) at Kansas State University.; Finally, optimum systems for various levels of farm size were developed by an example problem and model sensitivity analysis. The model application procedures were developed and tested by a large example problem including six drying methods and two handling systems. The model developed was used to formulate the system design problem for example, and sensitivity analysis was conducted for the changes of harvest volume and the priority level of objective function for drying and storage systems.; Sensitivity analysis showed that the best systems were the layer drying system for harvest volume under 15,000 bushels per year, and the batch-in-bin drying with stirrer for harvest volume from 15,000 to 150,000 bushels per year, if the highest priority is given to the cost minimization. But, if the highest priority is given to the minimization of energy inputs, the natural air drying system was the best for harvest volume for 5,000 to 150,000 bushels per year. Also, that analysis indicated that the computer program developed for user supplied subroutine of NEWINGP could be used conveniently for sensitivity analysis.; In conclusion, the model developed in this study can be used to formulate the system design problems with multiple objectives and system constraints, and the nonlinear goal programming could solve that problem efficiently. |
