Paper, tea, tobacco, and other food and industrial goods use drying tunnels to remove moisture from wet product sheets during manufacture. The tunnels operate using a fan to supply drying air to the product sheet through an orifice plate. Unfortunately, under some supply conditions, little or no drying occurs. However, by running the tunnels under optimized conditions, one drying tunnel can save up to 10% of the cost of power alone. Inventory control benefits can add to the savings. A model has been created that determines the settings necessary to achieve these optimum conditions.
Computational Fluid Dynamics (CFD) engineers faced with the challenge of simulating the drying tunnel typically grid the whole tunnel geometry and attempt to solve the discretized governing equations over the whole domain. This approach suffers from the difficulty of successfully capturing all the turbulence length scales involved in the process: a drying tunnel can be more than a hundred meters long, whereas the smallest turbulence length scales could be as small as a few millimeters. Currently, CFD engineers use DNS (direct numerical simulation), LES (large eddy simulation), or some turbulent closure model coupled with the governing equations. All of these simulation methods are computationally very intensive and time-consuming.
The drying tunnel model takes an alternative approach and uses existing engineering correlations to predict the heat and mass-transfer coefficients for the tunnel. The model relies on applying a convective heat transfer coefficient determined from experimental data and on prescribed orifice plate array geometry. The relationship currently applied is referenced and presented in Hollworth and Cole's "Heat Transfer to Arrays of Impinging Jets in Crossflow" from the Journal of Turbomachinery (Vol. 109, pp 564-571, 1987). Similarly, one can implement other convective heat transfer predictions for different orifice plate geometries.
The model calculates the mass transfer coefficient based on the Lewis relationship between heat and mass transfer. The model then solves a simple set of (coupled) governing equations for temperature and moisture to get the product conditions in the drying tunnel. It estimates the air conditions through the drying tunnel using a lumped mass approach. Air characteristics at discrete nodes in the flow loop are calculated using loss coefficients, which are estimated from standard engineering correlations. The model features an indigenously developed finite element solver that is a C++ class library whose object-oriented design enables customizing the solution procedure to a specific problem. The end result is a fast and accurate set of optimization conditions.
The model was validated against a standard industrial application and the results agree very closely. Figure 1 shows the result of one such simulation for the drying of a wet tobacco sheet through a simple, one-zone drying tunnel. Calculating the solution took 30 seconds on a 450-MHz processor with 256 Mbytes of RAM. The results show that condensation occurs over the first 2.5 meters of the dryer, and for the given input conditions the amount of moisture condensed is more than the moisture evaporated. The plant engineer has to change the process settings to increase the evaporation. This example illustrates that manufacturers can use this model to optimize their dryers to get the required output moisture loading (kgwater/kgproduct) quickly and accurately.
The plant and design engineers can use this tool to better understand the processes within the drying tunnel. It provides a means for estimating the tunnel operating set points to run the drying process at the most optimal conditions and to reduce the processing costs.
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