Drying characteristics of yam slices ( Dioscorea rotundata) in a convective hot air dryer: application of ANFIS in the prediction of drying kinetics

Abstract

This study applied Adaptive Neuro-Fuzzy Inference System (ANFIS) to predict the moisture ratio (MR) during the drying process of yam slices ( Dioscorea rotundata) in a hot air convective dryer. Also the effective diffusivity, activation energy, and rehydration ratio were calculated. The experiments were carried out at three (3) drying air temperatures (50, 60, and 70 °C), air velocities (0.5, 1, and 1.5 m/s), and slice thickness (3, 6, and 9 mm), and the obtained experimental data were used to check the usefulness of ANFIS in the yam drying process. The result showed efficient applicability of ANFIS in predicting the MR at any time of the drying process with a correlation value (R ²) of 0.98226 and root mean square error value (RMSE) of 0.01702 for the testing stage. The effective diffusivity increased with an increase in air velocity, air temperature, and thickness and the values (6.382E -09 to 1.641E -07 m ²/s). The activation energy increased with an increase in air velocity, but fluctuate within the air temperatures and thickness used (10.59–54.93 KJ/mol). Rehydration ratio was highest at air velocity×air temperature×thickness (1.5 m/s×70 °C × 3 mm), and lowest at air velocity × air temperature×thickness (0.5 m/s×70 °C × 3 mm). The result showed that the drying kinetics of Dioscorea rotundata existed in the falling rate period. The drying time decreased with increased temperature, air velocity, and decreased slice thickness. These established results are applicable in process and equipment design, analysis and prediction of hot air convective drying of yam ( Dioscorea rotundata) slices.

Abstract

Food science, Food technology, Computer science, Adaptive neuro fuzzy inference system (ANFIS), Drying, Moisture ratio, Effective diffusivity, Activation energy, Rehydration ratio.