Effects of Particle Shapes on Temperature Distribution under Static Ohmic Heating.
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Date
2014Author
Rohmatin, Annisa
Suyatma, Nugraha Edhi
Kamonpatana, Pitiya
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The ohmic heating is defined as a process wherein an alternating electrical current is passed through materials resulting in the heat generation inside the product. The excellence of ohmic heating in comparison with other thermal processing is its energy efficiency and ohmic heating could keeping nutritional as well quality attribute of food via blanching, sterilization, pasteurization, extraction, fermentation, and evaporation. Nevertheless, in ohmic heating process, the electrical conductivity of product is a key parameter. The worst case in the ohmic heating of multiphase product could be occurred when the solid phase has very lower or very high electrical conductivity than the liquid. This study aims to reveal the temperature distribution and the heating rate properties of solid and liquid phase as affected by particle shape and its orientation to the electric field. Experiments were conducted with salt solution 1% and carrot that blanched in salt solution 6%. Blanching process was conducted in order to increase the electrical conductivity of carrot close to the salt solution 1%. Ohmic heating was applied to the sample using static cell (4.90 cm diameter and 6.00 cm in length of sample area) at constant voltage gradient (40 V). Each experiment was conducted in triplicate. It was observed that the different particle shape and orientation provide the different heating rate properties. The slice particle had the fastest heating rate. Moreover the perpendicular orientation was heated faster than parallel orientation. The parallel cylindrical shape provides the slowest heating rate. This shape possibly induced the worst-case condition and could be used as sufficiency heat parameter. The heating pattern of each solid and liquid were simulated using COMSOL modeling software. The predicted temperature values were in good agreement with the experimental data with the maximum prediction error of 3 °C.