280984 Generalized Non-Linear QSPR Models for Surface Tension

Monday, October 29, 2012
Hall B (Convention Center )
K. M. Yerramsetty, B. J. Neely and K. A. M. Gasem, School of Chemical Engineering, Oklahoma State University, Stillwater, OK

Surface tension (ST) is of importance for many processes and phenomena, such as mass and heat transfer, gas injection displacement and flow through porous media. At low values, ST is the dominant fluid property which determines relative permeabilities and residual liquid saturations in gas condensate systems. The correlations available in the literature for predicting ST currently have a limited range of applicability or poor suitability for generalization. Further, most literature models exhibit large errors near the critical point of the fluid, and rely on experimentally measured physical properties as input for their predictions. Therefore, there exists a need for generalized models capable of providing a priori predictions for compounds that have no relevant experimental data or for compounds that are yet to be synthesized.

Previously, researchers at Oklahoma State University (OSU) have developed a unified framework for correlating saturation properties. This scaled-variable-reduced-coordinates (SVRC) model is based on the corresponding states theory (CST) and scaling-law behavior and in general is capable of representing saturation properties within their experimental uncertainties. The modeling approach involves the use of quantitative structure-property relationship (QSPR) methodology to generalize the model parameters of the developed SVRC model. Specifically, we use SVRC to develop the behavior model, and QSPR to generalize the SVRC model parameters. This approach, in the past, has proven to be more effective than the typical efforts to develop generalized models directly using QSPR techniques. In this work, we extend the SVRC model to correlate ST values and generalize the model parameters using structure-property modeling. A database of 2,417 data points involving 180 fluids was used in the development of this model. When applied to an external dataset containing 922 data points involving 71 fluids, the ST model predictions were, on average, within 2.6% of the reported values.


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