A predictive model for the excess gibbs free energy of fully dissociated electrolyte solutions

Lin, Shiang‐Tai

doi:10.1002/aic.12325

Cited by 42 publications

(42 citation statements)

References 58 publications

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“…The differential of activity to concentration is determined by the predictive activity coefficient model, COS-MO-SAC model (Hsieh and Lin 2011).…”

Section: Molecular Dynamics Simulationmentioning

confidence: 99%

Measurements of diffusion coefficient of methane in water/brine under high pressure

Chen¹,

Chu²,

Chen³

et al. 2018

Terr. Atmos. Ocean. Sci.

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The diffusion coefficient of methane in water plays an important role in the formation and dissociation of methane hydrate. However, most of the previous studies on the diffusion coefficient of methane in brine are performed at room temperature and low pressures, which is quite different from the formation condition of methane hydrate. In this study, we measure the diffusion coefficient of methane in pure water and brine in capillary tube at 10.3 MPa and temperature ranging from 283.15 to 308.15 K. We use the Raman spectrum to measure the ratio of C-H bound signal of methane to the O-H bound signal of water, to estimate the concentration of methane dissolves in water/brine. The Raman spectrum is collected at different time and different positions away from the liquid-gas interface. Diffusion coefficient is determined by fitting the experimental data with the concentration profiles solved from Fick's second law and semi-infinity boundary condition. By this method, we can evaluate the diffusion coefficient at different temperatures or salinities. The diffusion coefficient of methane in water/brine increases as the temperature increases. The diffusion coefficient of methane in brine is lower than that in pure water. Molecular dynamics (MD) simulation is also performed in this study to calculate the diffusion coefficient of methane in water/brine. The MD results can successfully predict the tendency of temperature effect and adding electrolyte.

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“…The differential of activity to concentration is determined by the predictive activity coefficient model, COS-MO-SAC model (Hsieh and Lin 2011).…”

Section: Molecular Dynamics Simulationmentioning

confidence: 99%

Measurements of diffusion coefficient of methane in water/brine under high pressure

Chen¹,

Chu²,

Chen³

et al. 2018

Terr. Atmos. Ocean. Sci.

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“…An alternative to this approach is the modification of the interaction energy equations in COSMO‐RS with empirical relations. Based on COSMO‐SAC, a modified variant of the COSMO‐RS model, Hsieh and Lin were able to correlate a large number of mean ionic activity coefficients (MIACs) of different salts in binary aqueous electrolyte solutions. For this purpose, they combined COSMO‐SAC with the Pitzer Debye‐Hückel term and introduced simple empirical relations for different ion interactions into the model.…”

Section: Introductionmentioning

confidence: 99%

“…For this purpose, they combined COSMO‐SAC with the Pitzer Debye‐Hückel term and introduced simple empirical relations for different ion interactions into the model. The model was successfully applied to the prediction of MIACs in mixed‐solvent systems as well as the prediction of thermodynamic properties of binary mixtures containing ionic liquids . However, the model was not directly evaluated for the prediction of LLE of systems containing strong electrolytes.…”

Section: Introductionmentioning

confidence: 99%

Development of a COSMO‐RS based model for the calculation of phase equilibria in electrolyte systems

2017

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A new electrolyte model, which is based on the predictive thermodynamic model COSMO-RS, is presented. For this purpose, an implementation of COSMO-RS that allows the integration of multiple segment descriptors was developed. To aid in the development of the electrolyte model, a new technique is presented that allows the evaluation of the different contributions of the interaction terms of COSMO-RS to the partial molar enthalpies. General empirical interaction energy equations are introduced into the electrolyte model. They are parameterized based on a large training set of mean ionic activity coefficients as well as liquid-liquid equilibrium data close to ambient conditions. The model is shown to be capable of predicting properties of systems containing anions that were not part of the training set of the model. Furthermore, it is demonstrated that the model can also lead to satisfying predictions if compared to vaporliquid equilibrium data.

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“…Numerous theoretical frameworks for aqueous electrolyte solution modelling have been described in the literature; recent examples include the publications of Afanas'ev (2011), Fraenkel (2011, Haghtalab et al (2011), Hsieh and Lin (2011), Hu et al (2011), Lamperski and Pluciennik (2011), Li et al (2011), Partanen (2012), Sun and Dubessy (2012), Tian et al (2012) and Xiao and Song (2011). Of the various approaches proposed none has been more widely adopted than that of Pitzer (1973).…”

Section: Introductionmentioning

confidence: 99%

Aqueous electrolyte solution modelling: Some limitations of the Pitzer equations

Rowland

Königsberger

Hefter

et al. 2015

Applied Geochemistry

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Despite intense efforts, general thermodynamic modelling of aqueous electrolyte solutions still presents a difficult challenge, with no obvious method of choice. Even though the Pitzer equations seemingly provide a well-established theoretical framework applicable to many chemical systems over a wide range of temperatures and pressures, they are not as widely adopted as their early promise might have suggested. This is strikingly illustrated by the simultaneous appearance in the literature of numerous, different (and potentially incompatible) Pitzer models alongside a proliferation of alternative theoretical approaches with inferior capabilities.To better understand this problem, the ability of the Pitzer equations to represent the physicochemical properties of aqueous solutions has been systematically investigated for exemplar electrolyte systems. Pitzer ion-interaction parameters have been calculated for selected systems by least-squares regression analysis of published solution data for activity coefficients, osmotic coefficients, relative enthalpies, heat capacities, volumes and densities to high temperatures and pressures. Although satisfactory fits can be achieved when the ranges of conditions are carefully chosen and when sufficient data are available to constrain the regression, the fits obtained tend otherwise to be unsatisfactory. The Pitzer equations do not cope well with gaps and other deficiencies in the regressed data. Profound difficulties, poorly recognized hitherto, can also arise because of variation in the sensitivity of the Pitzer functions to values for different physicochemical properties when these are combined. Given the dimensionality of numerous related thermodynamic properties, all changing as functions of composition, temperature and pressure, these problems are difficult to detect, let alone address, especially in multicomponent systems. The growing practice of improving fits simply by adding basis functions (thereby increasing the number of adjustable parameters) should be deprecated because it increases the likelihood of error propagation, introduces subjectivity, makes independent verification difficult and has deleterious implications for both automated data processing and for consistency between thermodynamic models.

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A predictive model for the excess gibbs free energy of fully dissociated electrolyte solutions

Cited by 42 publications

References 58 publications

Measurements of diffusion coefficient of methane in water/brine under high pressure

Measurements of diffusion coefficient of methane in water/brine under high pressure

Development of a COSMO‐RS based model for the calculation of phase equilibria in electrolyte systems

Aqueous electrolyte solution modelling: Some limitations of the Pitzer equations

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