418107 Property Data for Process Industry Applications – Is There a Niche for Molecular Simulations?

Sunday, November 8, 2015: 4:50 PM
255B (Salt Palace Convention Center)
Paul M. Mathias, Fluor Corporation, Aliso Viejo, CA, Kevin Hinkle, Chemical Engineering, University of Illinois at Chicago, Chicago, IL and Sohail Murad, Chemical and Biological EngineeringEngineering, Illinois Institute of Technology, Chicago, IL

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.[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, 2-9.

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