Authors:
Alvin M. Ante1*, Arnold R. Elepaño2, Kevin F. Yaptenco2 and Delfin C. Suministrado3
1Department of Agricultural and Biosystems Engineering, College of Engineering, Dr. Emilio B. Espinosa Sr. Memorial State College of Agriculture and Technology, Mandaon, Masbate, Philippines
2Agricultural, Food and Bio-process Engineering Division, Institute of Agricultural and Biosystems Engineering, College of Engineering and Agro-Industrial Technology, University of the Philippines Los Baños, Laguna, Philippines
3Agribiosystems Machinery and Power Engineering Division, Institute of Agricultural and Biosystems Engineering, College of Engineering and Agro-Industrial Technology, University of the Philippines Los Baños, Laguna, Philippines
*Correspondence: amante@up.edu.ph or antealvin@gmail.com
How to cite:
Ante AM, Elepaño AR, Yaptenco KF and Suministrado DC. 2023. Numerical modeling of the drying behavior of Adlai (Coix lacryma-jobi L.) grain. The Palawan Scientist, 15(2): 55-68.
ABSTRACT
The study aimed to develop computational fluid dynamics (CFD) models for simulating the drying performance of Adlai grain during convective drying with an air temperature range of 30°C to 60°C at around 10% to 80% relative humidity (RH). Before CFD modeling, the calculation of selected thermophysical properties of Adlai through mathematical modeling of its food constituents and thin-layer drying experiments were conducted. The simulation of heat and mass (moisture) transfer and visualization of moisture and temperature gradients in Adlai grain during drying were carried out using Analysis Systems (Ansys) Student 2020 R2 software package, specifically the Fluent solver. Results showed that the CFD models exhibited good agreement with the actual drying performance of Adlai. The models were validated using three statistical parameters: coefficient of determination (R2), standard error (S), and percent mean deviation modulus (P%). The R2 values ranged from 0.94-0.98; the S values ranged from 0.0018-0.0066; and the P% values ranged from 6.5%-8.68%. Overall, the models were deemed acceptable in estimating the moisture content of Adlai due to high R2 values, low S values, and P% values of less than 10%. The results validate the use of CFD as a reliable method for predicting the drying performance of Adlai, which contributes to the optimization of the drying process, the improved designing of drying systems, and the enhancement of product quality.
Keywords: grain drying, hot air, multiphysics, simulation
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