Robert L. Robinson

A modified temperature dependence for the Peng-Robinson equation of state

Abstract Numerous modifications have been suggested for the temperature dependence of the Peng–Robinson equation of state (PR-EOS) through the α function in the attraction term in the equation. In most cases, these modifications were motivated by the need to improve vapor pressure predictions for heavy hydrocarbons. While improvements have been realized, even the most successful α modifications suffer from (a) limited experimental pvT and equilibrium measurements for heavy hydrocarbons on which to base the modification, and (b) reliance on the use of switching functions below and above the critical temperature. In this work, we proposed a new α function for the PR-EOS. This new function is based on pure-component vapor pressures for different molecular species, including heavy hydrocarbons. The new PR-EOS α function leads to improved vapor pressure predictions (about 1% absolute average percentage deviations) for the systems considered (28 pure fluids, encompassing over 1100 measurements). In addition, we updated the original PR-EOS α function to improve its predictions for the heavy hydrocarbons.

Modeling the adsorption of pure gases on coals with the SLD model

The simplified local density/Peng–Robinson model (SLD-PR) was modified to improve its predictive capability when dealing with near-critical and supercritical adsorption systems of the type encountered in coalbed methane recovery and CO2 sequestration. The goal was to develop efficient equation-of-state (EOS) computational frameworks for representing adsorption behavior, as well as to improve our understanding of the phenomenon. The ability of the modified SLD-PR to correlate accurately data for supercritical adsorption systems is demonstrated using adsorption measurements on activated carbon, Illinois #6 coal, Fruitland coal, and Lower Basin Fruitland coal. The results indicate that the modified SLD-PR model, which incorporates a modified repulsive parameter “b” for the PR EOS, is capable of modeling the adsorption of pure methane, nitrogen, and CO2 at coalbed conditions. Inclusion of a slit geometry in the adsorbent matrix yields results superior to our previous two-dimensional EOS models for the adsorbates considered. The results also indicate that accounting for the adsorption surface structure within the SLD-EOS framework is effective in improving modeling capability for high-pressure adsorption phenomena. An explanation is offered as to why the adsorbed-phase densities are close to the EOS reciprocal co-volumes. Further, the model (a) generates direct estimates for the adsorbed-phase densities (which facilitate reliable prediction of absolute gas adsorption) and (b) readily describes the observed maximum in Gibbs-adsorption isotherms of CO2 at the temperatures and pressures encountered in coalbeds.

Evaluation of the simplified perturbed hard chain theory (SPHCT) for prediction of phase behavior of n-paraffins and mixtures of n-paraffins with ethane

Abstract The ability of the simplified perturbed hard chain theory (SPHCT) equation of state (EOS) to represent the phase behavior of both pure n-paraffins and mixtures of ethane + n-paraffins has been evaluated. The SPHCT EOS predicts reasonably well the vapor pressures and saturated liquid densities of n-paraffins extending from methane to n-C 64 (average absolute deviations, AAD, of 4% and 7%, respectively). Less accurate predictions, however, are obtained from the SPHCT at temperatures near the triple and the critical points. In general, comparable predictions of phase compositions are obtained from the SPHCT and the Soave (SRK) EOS for ethane + n-paraffin binary systems. For the heavier paraffins (n-C 20 and heavier), however, the SPHCT EOS produces better results. Accurate representation of the experimental data (root mean square error (RMSE within 1 bar for bubble point pressures) requires the use of an interaction parameter for ethane with each n-paraffin. Optimum binary interaction parameters for ethane with n-paraffin solvents extending from C 3 to n-C 44 are presented for the two equations. Simple generalized correlations have been developed for the SPHCT and SRK EOS input variables ( T * , v * and c for SPHCT; and T c , P c , and w for SRK). Using such correlations, a signle parameter ( C ij ) produces useful predictions for ethane in the complete n-paraffin series (RMSE of 1.5 bar for bubble point pressures) including the heaviest members of the series.

Nonlinear quantitative structure‐property relationship modeling of skin permeation coefficient

The permeation coefficient characterizes the ability of a chemical to penetrate the dermis, and the current study describes our efforts to develop structure-based models for the permeation coefficient. Specifically, we have integrated nonlinear, quantitative structure-property relationship (QSPR) models, genetic algorithms (GAs), and neural networks to develop a reliable model. Case studies were conducted to investigate the effects of structural attributes on permeation using a carefully characterized database. Upon careful evaluation, a permeation coefficient data set consisting of 333 data points for 258 molecules was identified, and these data were added to our extensive thermophysical database. Of these data, permeation values for 160 molecular structures were deemed suitable for our modeling efforts. We employed established descriptors and constructed new descriptors to aid the development of a reliable QSPR model for the permeation coefficient. Overall, our new nonlinear QSPR model had an absolute-average percentage deviation, root-mean-square error, and correlation coefficient of 8.0%, 0.34, and 0.93, respectively. Cause-and-effect analysis of the structural descriptors obtained in this study indicates that that three size/shape and two polarity descriptors accounted for approximately 70% of the permeation information conveyed by the descriptors.

Experimental Phase Densities and Interfacial Tensions for a CO2/Synthetic-Oil and a CO2/Reservoir-Oil System

Experimental data are presented for equilibrium vapor and liquid densities and interfacial tensions (IFTs) for two multi-component mixtures. Data are presented at 120 and 150°F for a CO 2 /synthetic-oil (containing the n-paraffins, methane to tetradecane) and at 130°F for a CO 2 /recombined-reservoir-oil system. In both systems, measurements include the near-critical region, where IFTs become very low. These data should be useful in developing and testing models to predict phase behavior and IFTs for CO 2 EOR operations

Infinite-dilution activity coefficients for several solutes in hexadecane and in n-methyl-2-pyrrolidone (NMP): experimental measurements and UNIFAC predictions

Abstract Infinite-dilution activity coefficients (γ1∞) are useful for the screening and selection of solvents for extractive distillation. A gas–liquid chromatography method has been used to acquire γ1∞ data for 20 solutes in hexadecane and in n-methyl-2-pyrrolidone (NMP). The present measurements show good agreement with available literature data. However, our study indicates that the degree of solvent pre-saturation significantly affects the quality of the data obtained for NMP. We also assessed the UNIFAC group contribution model for predicting γ1∞ for the hexadecane and NMP systems. The average absolute percent deviations for the 1987 UNIFAC model for NMP and hexadecane were 42 and 11, respectively. The errors for hexadecane were slightly lower for the 1993 UNIFAC model than for the 1987 model (8 and 11%, respectively). However, due to missing groups in the 1993 UNIFAC model, it could not be evaluated for the NMP systems. Functional groups needed for describing NMP were added to the 1993 UNIFAC model. Two cases were studied. In the first case, the entire NMP molecule was treated as one functional group. This yielded more accurate phase behavior predictions. In the second case, we added two functional groups, which could enhance the UNIFAC predictive abilities for other solvents containing these functional groups. The model parameters were optimized to fit data measured recently by our group. The overall average absolute deviations for the two cases were 12 and 20%, respectively.

A framework for the prediction of saturation properties: liquid densities

Abstract Shaver R.D., Robinson R.L., Jr. and Gasem K.A.M., 1992. A framework for the predic- tion of saturation properties: liquid densities. Fluid Phase Equilibria, 78: 81-98. Our scaled-variable-reduced-coordinate framework for correlating saturation properties was applied to pure-fluid saturated liquid densities at temperatures from the triple point to the critical point. The new correlation represents the saturated liquid densities of diverse chemical species with average errors of 0.11% when two adjustable parameters are used to characterize each substance. This compares favorably with the modified Rackett and the Hankinson-Thomson correlations with the added advantages of covering the full saturation range and obeying scaling-law behavior in the near-critical region. The correlation framework is essentially empirical; however, the results suggest an underlying physical significance for the model parameters, which show an excellent poten- tial for generalized predictions. This is demonstrated by the results given here where generalized saturated liquid density predictions over the full saturation range yield average errors of less than 1.0%.

A FRAMEWORK FOR THE PREDICTION OF SATURATION PROPERTIES : VAPOR PRESSURES

Abstract A new framework is proposed for correlating saturation properties using a ‘scaled-variable-reduced-coordinate’ approach. Utility of this approach is demonstrated by correlating the vapor pressures of a number of compounds at temperatures from the triple point to the critical point. The new framework results in precise representation of vapor pressures of diverse chemical species, generally within their experimental uncertainties (%AAD within 0.1) when two adjustable parameters are used to characterize each substance. The proposed model compares favorably with existing correlations, while having the added advantages of covering the full saturation range and obeying scaling-law bahavior in the near-critical region. Although the approach is essentially empirical, results obtained suggest an underlying physical significance for the model parameters and show an excellent potential for generalized predictions. This is demonstrated by the results given here for vapor pressures, where generalized predictions yield average errors of less than 1.5% over the full saturation range, based on a single experimental measurement.

Alternate equation of state combining rules and interaction parameter generalizations for asymmetric mixtures

Following the work of Juris and Wenzel, an alternate combining rule is proposed for cubic equations of state (CEOS). A wide variety of interactions between unlike molecules can be represented effectively by this combining rule. The Soave–Redlich–Kwong (SRK) and Peng–Robinson (PR) equations of state (EOS) have been used to assess the usefulness of the alternate combining rule in describing the types of unlike interactions encountered in asymmetric mixtures. In addition, a study was undertaken to evaluate the predictive capability of both equations of state in representing vapor–liquid equilibrium (VLE) of asymmetric binary mixtures, involving methane, ethane, nitrogen, hydrogen, carbon monoxide and carbon dioxide in n-paraffins (C4–C44). EOS binary interaction parameters generated by the proposed combination rules are presented for the systems considered. The quality of the EOS representation is dependent on the level of complexity applied in the parameter regressions. Overall, average absolute deviations of 1–3% are realized from the various regression scenarios. In addition, generalized EOS parameter correlations for system-dependent parameters have been developed. These generalized interaction parameters represent the solubilities of the selected systems within 5%.

Virtual Design of Chemical Penetration Enhancers for Transdermal Drug Delivery

Traditional drug design is a laborious and expensive process that often challenges the pharmaceutical industries. As a result, researchers have turned to computational methods for computer‐assisted molecular design. Recently, genetic and evolutionary algorithms have emerged as efficient methods in solving combinatorial problems associated with computer‐aided molecular design. Further, combining genetic algorithms with quantitative structure–property relationship analyses has proved effective in drug design. In this work, we have integrated a new genetic algorithm and nonlinear quantitative structure–property relationship models to develop a reliable virtual screening algorithm for the generation of potential chemical penetration enhancers. The genetic algorithms–quantitative structure–property relationship algorithm has been implemented successfully to identify potential chemical penetration enhancers for transdermal drug delivery of insulin. Validation of the newly identified chemical penetration enhancer molecular structures was conducted through carefully designed experiments, which elucidated the cytotoxicity and permeability of the chemical penetration enhancers.