Computational Fluid Dynamics (CFD) modeling was used to study the air velocity distribution and analyze the unsteady heat transfer and temperature distribution pattern during baking of chhana podo (milk cake). At first, a 3‐D geometry of the baking oven was created, and discrete ordinates (DO) radiation model was employed to predict the distribution of temperature. The data on progressive changes in thermal properties of chhana podo dough during baking were fed into the CFD model. The DO model was found to be adequate to predict the distribution of temperature in the product, with root mean squared error (RMSE) of 1.122 and mean percent relative deviation modulus (%P) of 0.70. Similarly, the DO model was found to be adequate to predict the air temperature distribution in the oven, with RMSE of 1.842 and %P of 1.61. The distribution of air velocity inside the oven was predicted using k‐ω turbulence model. The air velocity near the oven door was 0.505 m/s, whereas it was 5.002 m/s near the fan. Simulation results provided some insights on the effect of spatial location on air velocity. The placement of chhana podo inside the oven was optimized based on this simulation. This CFD simulation has potential application in the design of ovens with enhanced heat transfer and thermal efficiency.Practical ApplicationsThe present work models the heat transfer during baking of chhana podo. Even though a lot of research works related to CFD to products such as bread are available, many studies have not comprehensively considered temperature distribution in the oven, air velocity distribution and heat transfer in the product. The thermo‐physical properties of the product were modeled with respect to temperature and given as inputs into the CFD model. The CFD simulation of air velocity distribution inside the baking chamber will be useful in optimizing the placement of product or dough for uniform baking. The air velocity and heat transfer simulations quantified the diffusion of heat inside the product. This CFD simulation study could be useful in optimization and design of baking ovens for various products. Also, simulation of the baking conditions of a product could be advantageous from the economic perspective.
HighlightsA GUI tool was developed to predict the adventitious presence in non-GM produce.The software calculates tolerance and the probability of GM corn in non-GM corn.Predicted probability of contamination ranged from 0.050 to 0.356 at tolerance levels ranging from 0.1% to 5.0%.Abstract. The current rate of population growth necessitates the use of viable technologies like genetic modification to address estimated global food and feed requirements. However, in recent years, there has been an increase in resistance against the diffusion of genetic modification technology around the world. Many countries have adopted coexistence policies to allow a certain percentage of adventitious presence in non-genetically modified crops. However, the tolerance percentage for adventitious presence has been a bottleneck to free trade in some cases. It is a challenging task to fix a tolerance percentage considering the level of permeation of genetic modification technology in agriculture. This article introduces a software developed to serve as a decision-making tool to predict the probability distribution of genetically modified (GM) contamination in non-GM grain lot using user inputs such as final quantity of processed corn, overall tolerance level, and moisture content. The output from the software includes the mass of corn in each processing stage, the tolerance level and the probability distribution of potential GM contamination. The software predicted the probability of contamination with adventitious presence at tolerance levels of 5.0%, 3.0%, 1.0%, 0.9%, 0.5%, and 0.1% as 0.05, 0.07, 0.11, 0.12, 0.16, and 0.36, respectively. The predictions from the model were compared to a similar study wherein the effect of tolerance levels incurred in the costs of segregation was studied. The mean absolute percentage error for the predictions was found to be 3.07%. This software can be used as a tool in testing GM contamination in non-GM grain against a desired threshold levels in a grain elevator. Keywords: Corn, Genetic modification, Graphical User Interface (GUI), Threshold level.
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