73C-25 |
Modeling the Transport phenomena and structural changes during deep fat frying |
R. G. MOREIRA and R. Yamsaengsung. Agricultural Engineering, Texas A&M University, Scoates Hall, 310, College Station, TX 77843-2117 Various models have been proposed to study the mass and energy transport phenomena during the frying process. Oil, vapor, and air are phases that must be considered in producing appropriate models. However, none has accounted for the influence of structural changes of the product, which include shrinkage and thickness expansion, on the mass and energy transport of the system. The objective of this study was develop a fundamental model to predict the heat and mass transfer that occur during the frying and cooling processes of tortilla chips considering changes in the product's structure. A finite element method was used to solve the sets of partial differential equations. All water present in the tortilla chip was considered bound and led to shrinkage when removed. The expansion of the tortilla chip began at about a water saturation of 0.20. The parameters that were studied included water saturation, oil saturation, temperature, and pressure. Semi-empirical correlation were included to account for structural changes. Liquid flow results from convective flow due to the gradient in total gas pressure and capillary flow due to the gradient of capillary force. Gas movement results from convective flow due to the total gas pressure gradient and Knudsen diffusion due to the concentration gradient. The model with shrinkage and expansion predicted the frying and the cooling process well compared to the experimental results. For good convergence, the mesh distribution must be concentrated along the surface and the regions of high water saturation and temperature gradients. Good input parameters are vital in accurately predicting the transport phenomena and shrinkage and expansion. This includes (capillary pressure curve, vapor pressure curve, shrinkage and expansion data, etc.). The fundamental frying model that is presented in this research offers a step toward a fully predictive model that can be applied in the industry.
Session 73C, Food Engineering: Transport Processes and Kinetics
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