Mathematical Modelling of Orange Slices during Microwave, Convection, Combined Microwave and Convection Drying

References (3)

1. 1 MR=exp(-kt n ) Page (Agrawal and Singh, 1977) 2 MR=a exp(-kt) Henderson and Pabis (Akpınar et al., 2006) 3 MR=a exp(-kt)+c Logarithmic (Yaldız et al., 2001) 4 MR=1+at+bt 2 Wang and Singh (Wang and Singh, 1978) 5 MR=a exp(-kt)+(1-a)exp(-kbt) Diffusion Approach (Toğrul and Pehlivan, 2003) 6 MR=a exp(-kt)+(1-a)exp(-gt) Verma (Verma et al., 1985) 7 MR=a exp(-kt)+bexp(-k 1 t) Two Term (Alibas, 2012) 8 MR=a exp(-kt)+(1-a)exp(-kat) Two Term Exponential (Sharaf et al., 1980) 9 MR=a exp(-k(t m )+bt Midilli and Kucuk (Sacılık and Elicin, 2006) Convective drying experiments were conducted at the temperature values of 100, 150, and 200 °C. The effect of changing the temperature in the microwave oven on the moisture ratio curve of orange samples is shown in Fig 3. The total drying times to reach the final moisture content of the orange slices at the temperatures of 100, 150 and 200 °C were 184, 98 and 68 min, respectively. It is clearly shown that the air temperature had a importantly effect on the drying time. From the range analysis of the experiments, it can be found that the drying time is longest at 100 °C, and shortest at 200 °C. Similar findings were reported by (Alıbas, 2006) for chard leaves, (Vega-Galvez et al., 2008) for apple samples. Mathematical modelling of drying curves The nine thin layer drying models were compared in terms of the statistical parameters R 2 (Coefficient of determination), SEE (Standard error of estimate), RSS (residual sum of square) Nine thin layer drying models were used as described by several researchers and were shown in Table 1. The statistical analyses results applied to 9 drying models at drying process at 180, 360, 540, 720 and 900 W microwave output powers
2. 100, 150, 200 ºC drying air temperatures
3. 100, 150 and 200 ºC drying air temperatures at constant microwave powers of 180 W, 360 W and 540 W are given in Tables 2, 3, 4 and 5 for orange slices. In this work, the thin layer drying model in which (R) value was closest 1.0000 and smallest SEE and RSS values were chosen to be the most optimum model. To take into account the effect of the drying variables on the Midilli–Kucuk model constants a, k, m and b were regressed against those of drying air temperatures using multiple regression analysis (Ertekin and Yaldız, 2004). Based on the multiple regression analysis, the accepted model was as follows: bt kt a M M M M b m k a MR m e e       ) exp( ),,, ( (2) Figure 3. Variation of experimental and predicted moisture rat