Process modeling is a key enabling technology for process development and design, equipment sizing and rating, and process debottlenecking and optimization, and success in process modeling is critically dependent upon accurate descriptions of the thermophysical properties and phase behavior of the associated chemical systems. Applied thermodynamics uses a wide variety of engineering correlations and reference-quality models, but these depend on “data” to fine-tune the model parameters. Currently, the “data” predominantly come from experimental measurements or estimation methods ranging from group-contribution correlations to theoretically-based approaches, but experimental measurements are strongly preferred. Molecular simulation has perhaps reached the stage where it could be used to provide the “data” to fine-tune industrial correlations. However, the collective experience with using molecular simulations in the data-generation role is limited. It is well known that the uncertainty in the intermolecular potentials can sometimes create serious problems, and hence a simulation challenge was created to test force-field transferability. Why should an engineer trust simulation results in a region where no data exist? The main reason why an engineer could do this is because generally the trends of molecular simulation are expected to be reliable and likely are not adversely affected by uncertainties in the intermolecular potentials. In this presentation we discuss our experiences with the use of molecular simulation to create new “data,” specifically by evaluating available measurements and by extrapolating existing data into new regions where measurements are not currently available. We feel that this initial step is needed to advance molecular simulations into a broadly reliable data-generation tool.
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