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Non-deterministic analysis of a liquid polymeric-film drying process
In this study the authors employed the Monte Carlo/Latin Hypercube 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. They found that the non-deterministic analysis 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, they 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 values, which can differ significantly from those calculated using deterministic simulation. For input-parameter uncertainties in the range from 2 to 10% of their respective means, variations of response variables were found to be comparable to the mean values.