Din-Sue Fon

Development of SAPGD—A Simulation Software Regarding Grain Drying

Abstract Properties of air are interrelated in a drying process. Equations of psychrometrics and fluid dynamics developed by previous researchers enable the development of simulation models, which become an indispensable tool for the study of drying technology. A Windows based software, entitled SAPGD (Simulation of Air Properties and Grain Drying), written in MATLAB, was developed to simulate the equations/models related to the following four aspects: 1. The equilibrium moisture content of grains under the diverse conditions of atmosphere. 2. The pressure drop to airflow of various grains. 3. The relationship between drying time and moisture content under the model of thin layer drying of grains. 4. The relationships among drying time, moisture content, grain depth, and grain temperature under the logarithmic model of deep bed drying of grains.

Simulation and Validation of Thin Layer Models for Peanut Drying

Simulations of peanut drying in a trailer-type dryer with thin-layer drying models, Newton model and Henderson-Pabis model, and five equilibrium moisture content (EMC) models in this study. The results show that the match of Henderson-Pabis model using Hummeida K-value model and modified Oswin EMC model can yield the best fit of experimental data, although the error in temperature prediction still exists at the middle layer.

Development of a Visual Method to Test the Range of Applicability of Thin Layer Drying Equations Using MATLAB Tools

Abstract Thin-layer drying equations are important in crop drying simulations. Various drying theories based on semi empirical Newton model or diffusion model are employed to predict crop drying. The Page model that is an empirical variation of Newton model has gained popularity due to its simple form yet robustness. The drying equations summarized in the revised ASAE standards S448 are mostly based on Pages model. The MATLAB based software TLDRY is developed to examine the range of applicability of equations in ASAE S 448 and a number of other thin layer drying equations found in literature. The software proved to be a convenient tool to identify the range of applicability of and potential uncertainties for these equations.

Predicting the production schedule of paddy-rice nursery

The objective of this study was to develop a linear programming model to analyze the production schedule of rice seedlings grown in the nursery. The model can help the managers of rice seedling centers in Taiwan to make better production decisions and maximize profit (all monetary units are expressed in US dollars). To verify the model, a rice seedling center was chosen for analysis of the first rice crop in 1997. The results show that the cost is about

Development of simulation software regarding air properties and grain drying using MATLAB

51,856 yielding a profit of about

Simulation of Automatic Output Operations for Rice Seedlings

21,861 to supply 91,385 boxes of seedlings. The maximum number of boxes in the greening field is 4,600. The minimum production in each batch affects the cost significantly. The results show costs of about

Development of an Automatic Pallet Loading/unloading System for Seeded Trays

41,884 for 4,000 boxes batch–1, while costs rise to about

Development of an Automatic Rolling System for Rice Seedlings

56,522 for 6,000 boxes batch–1. Consequently, using the model to predict the production schedule not only reduces costs but also provides the seedling in the best status for transplanting. The demand quantities of the boxes in each day during the transplanting period can be expressed by a fourth-order polynomial. Here, a demand quantity ranging from 73,928 to 110,892 boxes is generated by the polynomial to analyze the production schedule. The result shows that the production scale could be expanded to about 110,892 boxes with the same hardware. In this status, the profit could be increased to about

A Simulation Model of a Seeding System for Rice Nurseries

26,667, and the cost/profit ratio is 2.35. The relationship between supply quantity scale (x) and greening field size (y) can be calculated by a regression formula (y = 0.4609x + 3.143), where both units are in 10,000 boxes.

Simulation of Conveyor Transport Operations using a Gantry System

Properties of air such as temperature, humidity, flow rate, etc. and properties of grain such as moisture content, specific heat, quantity, etc. are interrelated in a drying process. Any one of these factors can affect the quality of grain. Equations of psychrometrics and fluid dynamics developed by previous researchers enable the development of simulation models, which become an indispensable tool for the study of drying technology. A Windows software, entitled SAPGD (Simulation of Air Properties and Grain Drying), written in MATLAB, was developed to simulate the equations/models related to the following 4 aspects: 1. The equilibrium moisture content of grains under the diverse conditions of atmosphere. 2. The pressure drop to airflow of various grains 3. The relationship between drying time and moisture content under the model of thin layer drying of grains. 4. The relationships among drying time, moisture content, grain depth and grain temperature under the logarithmic model of deep bed drying of grains.