Modeling of thin-layer drying kinetics of sesame seeds : mathematical and neural networks modeling

摘要: Natural drying characteristics of sesame seeds (SS) were investigated under indoor conditions with both forced convection (FC) and natural (NC) air. The kinetics SS was characterized in terms effective diffusion coefficient resistance to diffusion. For the FC method, dried at a constant air velocity 1.1 m s -1 temperature relative humidity range of25-29°C 35-40%, respectively. NC 32-36 30-35%, Six thin-layer models, namely, Khazaei, Peleg, Page, Handerson Pabis, logarithmic, Weibull, fitted data. Modelling correlation between moisture ratio time method also carried out using artificial neural networks (ANN). average initial content around 50.8% (d.b.) final about 3.0-3.7% until no further changes their mass observed. took place falling rate period. During experiments, reach 3% found be 400 min. same achieve its equilibrium (3.7%) after 900 min when method. Thus, times 55% shorter than times. In methods, rates very beginning equal 22.47 6.9 (g H 2 O kg dry matter ), water coefficients 3.1×10 -11 1.1×10 , Corresponding values for overall 70.8×10 5 19.6× 10 6 results showed that Khazaei model gave better fit other five models. Peleg logarithmic models had an acceptable accuracy predicting SS. ANN technology shown useful tool as function time. optimal 2-6-3-1 structure hyperbolic tangent transfer function. This capable R higher 0.998, RMSE less 0.0192 MRE 2.63%. It concluded represented mathematical