(37d) Property Data for Process Industry Applications – Is There a Niche for Molecular Simulations?

Mathias, P. M., Fluor Corporation
Hinkle, K., University of Illinois at Chicago
Murad, S., Illinois Institute of Technology

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.[1]  Currently, the ?data? predominantly come from
experimental measurements or estimation methods ranging from group-contribution
correlations[2] to theoretically-based approaches,[3]
but experimental measurements are strongly preferred.[2]  Molecular
simulation has perhaps reached the stage where it could be used to provide the
?data? to fine-tune industrial correlations.[4]  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.[5]  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.

[1] C.-C. Chen; P. M. Mathias, ?Applied Thermodynamics
for Process Modeling, AIChE J.,? 2002, 48, 194-200.

[2] B. E. Poling; J. M. Prausnitz; J. P. O'Connell, ?The Properties of Gases and Liquids,?
Fifth Ed., McGraw Hill, New York, 2001.

[3] A. Klamt, ?COSMO-RS.
From Quantum Chemistry to Fluid Phase Thermodynamics and Drug
Design,? Elsevier BV, Amsterdam, 2005.

[4] P. Ungerer; C. Nieto-Draghi; B. Rousseau; G. Ahunbay;
V. Lachet, ?Molecular Simulation of the Thermophysical Properties of Fluids:
From Understanding Toward Quantitative Predictions,? J. Molecular Liquids, 2007, 134, 71-89.

[5] F. H. Case, et al. ?The Fourth Industrial Fluid
properties Simulation Challenge,? Fluid
Phase Equilibria
, 2008, 274,


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