In this study we employed the Monter Carlo/Latin Hyperculbe sampling technique to generate input parameters for a liquid polymeric-film drying model with prescribed uncertainty distributions. The one-dimensional drying model employed in this study was that developed by Cairncross et al. We found that the non-deterministic with Monte Carlo/latin Hypercube sampling provides a useful tool for characterizing the two responses (residual solvent volume and the maximum solvent partial vapor pressure) of a liquid polymeric-film drying process. More precisely, we found that the non-deterministic analysis via Monte Carlo/Latin Hypercube sampling not only provides estimates of statistical variations of the response variables but also yields more realistic estimates of mean vaues, which can differ significantly from those claculated using deterministic simulation. For input-parameter uncertainties in the range from two to ten percent of their respective means, variations of response variable were found to be comparable to the mean values.
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