Genetic algorithm for the selection of rough rice drying model for the free-fall paddy dryer

Abstract

The purpose of this study was to seek the proper thin-layer rough rice model for the free-fall dryer. Experimental data for drying temperatures 100, 130 and 150C, drying velocity 2 m/s and rest period between drying round of 1 minute were fitted into the 9 theoretical models by a genetic algorithm (GA). The GA Toolbox in MATLAB was used to generate the correlation coefficients of the models. The best fitting of the experimental data by these models was specified by comparing the correlation coefficients, standard errors and mean square deviations. The Midilli model was found to be the most suitable model for drying condition at high relative humidity of ambient air while the Two-term exponential model was most appropriate for ambient air of low relative humidity.