Document Type : Research Paper
Authors
 Kamalodin Momeni ^{1}
 Hesam Najibi Najibi ^{} ^{2}
 Mohammad T. Sadeghi ^{3}
^{1} 1PhD Candidate, Department of Gas Engineering, Petroleum University of Technology, Ahwaz, Iran
^{2} Professor, Department of Gas Engineering, Petroleum University of Technology, Ahwaz, Iran
^{3} 3Associate Professor, Department of Chemical Engineering, Iran University of Science and Technology, Tehran, Iran
Abstract
In this work, the performance of four electrolyte models for prediction the osmotic and activity coefficients of different aqueous salt solutions at 298 K, atmospheric pressure and in a wide range of concentrations are evaluated. In two of these models, (electrolyte NonRandom TwoLiquid eNRTL and Mean Spherical ApproximationNonRandom TwoLiquid MSANRTL), association between ions of opposite charges for simplification purposes is ignored and in the other two ones, (Associative Mean Spherical ApproximationNonRandom TwoLiquid AMSANRTL and Binding Mean Spherical Approximation BiMSA) association and solvation effects are considered. The predictions of these four models for the osmotic and activity coefficients of electrolyte solutions at 298 K and atmospheric pressure are compared with the experimental data reported in the literature. This comparison includes, 28 different aqueous salt solutions including thiocyanates, perchlorates, nitrates, hydroxides, quaternary ammonium salts and others. The results show, the performance of models that consider association effects are better than others especially for higher salt concentrations. However, the best performance belongs to BiMSA model which has some parameters with physical meaning.
Keywords
Main Subjects
The reliable prediction of thermodynamic properties of electrolyte solutions is crucial in design and operation of oil and gas production and processing facilities. Vaporliquid phase equilibria simulation is the most important process which needs reliable thermodynamic properties of aqueous electrolyte solutions. Formation water produced with oil and gas contains dissolved salts, which makes the vaporliquid phase equilibria simulation for hydrocarbon systems in the presence of aqueous electrolyte solutions be a practical case. Therefore, it is needed to have accurate and reliable theoretical models for the predictions of thermodynamic properties of aqueous electrolyte solutions (Najibi et al., 2015). Different models have been developed and presented so far in the literature (Loehe and Donohue, 1997; Rafael et al., 1994), but semiempirical and theoretical models developed based on mean spherical approximation theory have received more attention in recent years (Bernard and Blum, 1996; Chen et al., 1982; Papaiconomou et al., 2002; Simonin et al., 2006). The challenge facing these models is the validity of the results for a wide range of salt concentrations in the aqueous electrolyte solutions.
Four of the important models developed in this context are eNRTL proposed by Chen et al. (1982); MSANRTL proposed by Papaiconomou et al. (2002); AMSANRTL proposed by Simonin et al. (2006); and BiMSA proposed by Bernard and Blum (1996). In the first two models, the main simplifying assumption is that no association of ions of opposite charges will take place in the aqueous electrolyte solution. In this work, these four models are used to predict the osmotic and activity coefficients of different aqueous electrolyte solutions, and the results are compared.
2. Electrolyte models
The eNRTL and MSANRTL models are developed based on this assumption that the salt is completely dissolved in the liquid phase and dissociates into its forming ions, and no ion pairing takes place in the liquid phase. The dissociation reaction can be written as follows:
(1) 
where, ν_{i} is the stoichiometric valence of the ions making the salt. The eNRTL model provides an expression for the excess Gibbs molar energy of electrolyte systems, which is assumed to be the sum of two terms
G_{eNRTL} = G_{PDH} + G_{NRTL} 
(2) 
where, the effect of longrange electrostatic interactions is described using the PitzerDebyeHückel (PDH), and shortrange interactions are described using the classic NRTL for all the species (ions and molecules) in the solution. Main assumption made in the classical eNRTL is that the number of cations surrounding a central solvent molecule is the same as the number of anions surrounding it, i.e. local electro neutrality assumption. With this simplification, this model has three basic parameters which need to be adjusted using experimental data. These parameters are the nonrandomness factor and the dimensionless energetic interaction parameters, i.e. and . The nonrandomness factor in this model is often set to a fixed value of 0.2 (Chen et al., 1982). In this model, it is assumed that t_{CW}= t_{AW }(denoted by t_{CA,W}) and t_{WC,AC}= t_{WA,CA}(denoted by t_{W,CA}). More details of the model are described elsewhere (Chen et al., 1982; Chen and Evans, 1986).
The MSANRTL model is a combination of the MSA model with the classic NRTL model (Papaiconomou et al., 2002). The latter is used to take into account the shortrange interactions, but the former describes the longrange electrostatic interactions. The MSA model can yield analytic expressions in terms of parameters, such as ion size and permittivity, which have physical meaning. In the MSANRTL approach, the molal activity coefficient for each component is given by the following expression:
(3) 
where, represents the MSA contribution, which has one adjustable parameter, i.e. (mean ionic diameter), and is the NRTL interaction contribution and contains four adjustable parameters, including , , and two concentration dependent parameters, i.e. and as follows:
(4) 
where, is the mole fraction of water in solution. The nonrandomness factor in this model is also set to 0.2. The details of the model equations are described elsewhere (Papaiconomou et al., 2002).
The AMSANRTL model (Simonin et al., 2006) is a modified version of MSANRTL, which includes ionic solvation and physical association for the ions present in the electrolyte solution. The inclusion of ion pairing causes a better prediction of the thermodynamic properties of aqueous electrolyte solutions. In this model, the hydration of ions is accounted by introducing a constant hydration number which is independent of salt concentration, as used in the classical model of Robinson and Stokes (Robinson and Stokes, 1959). Five adjustable parameters of this model are two dimensionless interaction energy parameters (τ_{CA,W}, τ_{WC,AC}); hydration number (h); equilibrium constant of ionpair formation reaction (K); and mean ionic diameter (σ). These parameters are optimized by fitting the data to experimental data. To reduce the total number of model parameters as much as possible, it is assumed that:
= = 
(5) 
(6) 

(7) 

h= h_{A} + h_{C} 
(8) 
where, subscript P represents the molecular species in solution. Hydration numbers for cations and anions are denoted as h_{C} and h_{A} respectively. The overall equation which describes this model is given by:
(9) 
where, is defined as , and the fraction of the bounded ion (x) is obtained by the following equation:
(10) 
where, g_{i} is fugacity coefficients of ionic species in the model level, and is the total stoichiometric number; m is molality of salt (mol.kg^{1}), and M_{W} stands for the molecular weight of water (18.0153 g·mol^{1}). The detail description of this model is presented by Simonin and his coworkers elsewhere (Simonin et al., 2006).
BiMSA model is developed based on the Wertheim formalism theory (Wertheim, 1988, 1987) and is proposed by Bernard and Blum (1996). The parameters in this model have some physical meaning from a microscopic point of view. The two important parameters are cation diameter (σ_{C}) and the relative permittivity of the solution (ε), which, in this work, are chosen as a function of salt concentration (C_{S}) as follows:
(11) 

(12) 
where, σ_{C}^{(0)} is the diameter of cation at an infinite dilution of salt, and σ_{C}^{(1)} is the concentration dependent term of cation diameter; ε_{W} is the relative permittivity of the pure solvent and is calculated using relation proposed by Uematsu and Frank (1980); β accounts for the variation of the permittivity of solution against concentration. This model can predict the properties of very nonideal salt solutions up to high concentrations, and its detail description is presented elsewhere (Bernard and Blum, 1996; Papaiconomou et al., 2012; Simonin et al., 1998).
3. Results and discussion
The experimental data for the osmotic and activity coefficients of 28 strong electrolyte aqueous solutions at a temperature of 298 K, at atmospheric pressure, and in a wide range of salt concentrations are gathered from literature. The model parameters are fitted to the gathered data using a reliable technique which works based on genetic algorithm (GA) optimization method. This technique allows finding the global optimum for all the parameters. In this method, the best parameters for the selected models are found by minimizing the summation of differences between the calculated and experimental values of the properties. For example, for the activity coefficients, the following objective function is minimized:
(13) 
All the minimizations are performed by using MATLAB software version R2011a. More details about genetic algorithm technique used herein can be found in the literature (Alvarez et al., 2008).
The performance of four electrolyte models selected in this study is investigated by comparing the calculated values with the data gathered from literature for the osmotic and activity coefficients of various aqueous salt solutions (Bonner, 1981a, 1982; Bonner, 1981b; Hamer and Wu, 1972; Kálmán and Schwabe, 1979; Lindenbaum and Boyd, 1964; Macaskill and Bates, 1986; Rard et al., 2003). All calculations are performed for single aqueous solution up to the highest concentration for which data are available at atmospheric pressure (0.1 MPa) and a temperature of 298 K. The fitted parameters and average absolute relative deviation percent (AARD (%)) of the osmotic and mean activity coefficients for the four electrolyte models selected in this work are shown in Tables 1 to 4. The results presented in these tables show that the values of ( and in MSANRTL model) for all the solutions are negative, which can be interpreted as the stronger the hydrated ion–solvent interaction than the solvent–solvent interaction which are basically identical to those given by Chen et al. (1982). Moreover, the values of are positive for all the solutions, which means that the hydrated cation/anion–solvent interaction is weaker than the hydrated cation–hydrated anion interaction, and for all the solutions, except for some cases in AMSANRTL model, is much smaller than , i.e. ≪ .
Figure 1
Osmotic coefficients calculated by eNRTL versus those calculated by MSANRTL; the solid line is the 1:1 correspondence line; symbols are define as follows: (○) HCLO_{4}; (□) KOH; (∆) Me_{4}NBr; (◊) Et_{4}NBr; and (*) Bu_{4}NBr.
For the first two models (eNRTL and MSANRTL), which do not consider ion pairing, the total AARD (%) of MSANRTL is slightly better than that of eNRTL model. However, the MSANRTL model has five fitting parameters, while eNRTL needs two parameters to be fitted. Therefore, it can be concluded that the predictive ability of the MSANRTL method is limited and has no special priority to the eNRTL model. The performance of eNRTL model for the prediction of osmotic coefficients of different singlesalt aqueous solutions in the entire range of molality is compared with the MSANRTL model in Figure 1. As shown in this figure, the predictions of these two models for the osmotic coefficients of different single salt aqueous solutions are quite comparable.
Table 1
eNRTL model parameters for a number of single electrolyte solutions and AARD (%) of its predictions for osmotic and activity coefficients at T=298.15 K and P=0.1 MPa.
Electrolyte 
max m 
AARD_{Φ }(%)^{a} 
AARD_{γ± }(%)^{b} 

LiBr 
17 
11.016 
5.545 
12.35 
26.68 
LiCl 
20 
11.113 
5.524 
8.921 
20.04 
LiOH 
4.0 
8.962 
4.388 
1.259 
1.706 
LiNO_{3} 
20 
9.705 
4.961 
3.035 
5.467 
LiCH_{3}SO_{3} 
5.5 
8.565 
4.549 
0.437 
0.630 
HClO_{4} 
16 
10.96 
5.546 
10.33 
21.98 
HNO_{3} 
28 
9.320 
4.767 
2.664 
4.437 
CsBr 
5.0 
8.468 
4.173 
0.426 
0.529 
CsCl 
9.0 
8.454 
4.182 
0.301 
0.389 
RbCl 
7.8 
8.247 
4.153 
0.181 
0.183 
RbNO_{3} 
4.5 
7.684 
3.454 
0.659 
0.686 
KCl 
5.0 
8.085 
4.118 
0.875 
0.340 
KF 
17.5 
9.856 
4.917 
4.823 
8.115 
KOH 
20 
10.69 
5.349 
8.296 
17.83 
NaClO_{4} 
6.0 
7.977 
4.112 
0.509 
0.751 
NaCF_{3}SO_{3} 
5.4 
6.498 
3.754 
0.555 
0.887 
NaSCN 
18 
9.673 
4.902 
3.209 
5.531 
Me_{4}NBr 
5.5 
9.017 
4.303 
1.241 
1.364 
Me_{4}NCl 
19 
9.523 
4.670 
2.575 
3.591 
Et_{4}NBr 
12 
9.947 
4.653 
1.355 
1.679 
Et_{4}NCl 
9.0 
10.18 
4.993 
2.121 
3.813 
Pr_{4}NBr 
9.0 
10.367 
4.856 
2.604 
3.013 
Pr_{4}NCl 
18 
9.888 
4.992 
3.610 
6.271 
Bu_{4}NBr 
10 
8.186 
3.617 
3.487 
3.285 
NH_{3}MeCl 
20 
7.996 
4.059 
0.978 
1.511 
NH_{3}MeNO_{3} 
9.5 
7.227 
3.530 
0.332 
0.324 
NH_{2}Me_{2}ClO_{4} 
7.5 
7.683 
3.530 
1.704 
2.132 
NH_{4}SCN 
23.5 
7.221 
3.712 
0.900 
2.113 
Average 



2.848 
5.188 
^{a} , ^{b} , where, N is the number of experimental data points.
Table 2
MSANRTL model parameters for a number of single electrolyte solutions and AARD (%) of its predictions for osmotic and activity coefficients at T=298.15 K and P=0.1 MPa.
Electrolyte 
max m (mol.kg^{1}) 
σ 
AARD_{Φ }(%)^{a} 
AARD_{γ± }(%)^{b} 

LiBr 
17 
13.76 
2.383 
14.12 
6.792 
7.999 
7.804 
20.97 
LiCl 
20 
11.186 
0 
5.477 
5.465 
10.00 
8.196 
18.16 
LiOH 
4.0 
3.775 
0.086 
1.669 
3.162 
1.147 
0.071 
0.051 
LiNO_{3} 
20 
15.18 
4.791 
5.469 
5.057 
4.45 
0.939 
38.03 
LiCH_{3}SO_{3} 
5.5 
9.209 
0 
4.254 
4.953 
5.136 
0.253 
0.244 
HClO_{4} 
16 
11.20 
0 
5.526 
5.538 
9.999 
9.550 
20.32 
HNO_{3} 
28 
9.560 
0.039 
4.731 
4.729 
9.999 
1.757 
2.495 
CsBr 
5.0 
6.380 
0 
4.952 
2.393 
3.639 
0.613 
0.248 
CsCl 
9.0 
9.304 
0.400 
4.386 
4.391 
4.171 
0.534 
0.480 
RbCl 
7.8 
8.200 
0 
0.100 
4.973 
4.031 
0.252 
0.315 
RbNO_{3} 
4.5 
7.313 
0 
1.685 
3.681 
3.096 
0.256 
0.389 
KCl 
5.0 
8.780 
0 
3.195 
4.777 
4.545 
0.819 
0.272 
KF 
17.5 
10.19 
0.039 
4.935 
4.900 
9.999 
4.134 
6.329 
KOH 
20 
11.01 
0.003 
5.375 
5.364 
9.999 
7.704 
16.10 
NaClO_{4} 
6.0 
8.029 
0 
4.478 
3.893 
5.309 
0.265 
0.272 
NaCF_{3}SO_{3} 
5.4 
8.048 
0 
3.263 
4.626 
5.688 
0.348 
0.147 
NaSCN 
18 
9.850 
0 
4.908 
4.823 
9.999 
2.427 
3.537 
Me_{4}NBr 
5.5 
5.490 
0.576 
1.203 
3.377 
2.408 
0.604 
0.776 
Me_{4}NCl 
19 
9.604 
0 
4.734 
4.598 
5.543 
2.505 
3.264 
Et_{4}NBr 
12 
5.874 
0.520 
3.489 
2.729 
3.027 
1.551 
1.612 
Et_{4}NCl 
9.0 
10.346 
0.003 
4.992 
4.997 
5.559 
1.998 
3.561 
Pr_{4}NBr 
9.0 
4.361 
0.132 
4.237 
0.613 
4.000 
1.789 
2.081 
Pr_{4}NCl 
18 
6.518 
0 
6.139 
2.232 
1.551 
3.460 
2.843 
Bu_{4}NBr 
10 
5.109 
0.347 
2.224 
2.224 
3.241 
3.155 
3.155 
NH_{3}MeCl 
20 
5.705 
0 
4.961 
2.068 
3.506 
1.387 
0.332 
NH_{3}MeNO_{3} 
9.5 
5.829 
0 
4.202 
2.235 
4.158 
1.294 
0.343 
NH_{2}Me_{2}ClO_{4} 
7.5 
7.200 
0 
2.421 
3.598 
2.817 
0.664 
2.512 
NH_{4}SCN 
23.5 
6.178 
0 
4.424 
2.486 
7.117 
0.701 
0.904 
Average 






2.322 
5.347 
^{a} , ^{b} , where, N is the number of experimental data points.
Table 3
AMSANRTL model parameters for a number of single electrolyte solutions and AARD (%) of its predictions for osmotic and activity coefficients at T=298.15 K and P=0.1 MPa.
Electrolyte 
max m (mol.kg^{1}) 
σ 
h 
(L.mol^{1}) 
AARD_{Φ }(%)^{a} 
AARD_{γ± }(%)^{b} 

LiBr 
17 
0.001 
1.595 
3.310 
3.012 
0 
1.363 
3.743 
LiCl 
20 
0.601 
1.469 
3.886 
2.987 
0 
1.201 
2.799 
LiOH 
4.0 
3.144 
1.960 
1.491 
2.000 
0.071 
0.347 
0.206 
LiNO_{3} 
20 
0.558 
1.332 
4.723 
1.875 
0.0002 
0.348 
0.165 
LiCH_{3}SO_{3} 
5.5 
3.129 
1.777 
5.163 
4.398 
0.013 
0.217 
0.275 
HClO_{4} 
16 
0.382 
1.567 
3.810 
3.468 
0 
1.224 
2.708 
HNO_{3} 
28 
3.391 
1.715 
5.772 
5.160 
0.028 
0.390 
0.570 
CsBr 
5.0 
5.506 
2.666 
3.902 
2.670 
0.208 
0.098 
0.105 
CsCl 
9.0 
0.214 
0.274 
2.831 
2.124 
0 
1.581 
0.691 
RbCl 
7.8 
0.527 
0.013 
3.705 
3.207 
0.072 
0.149 
0.133 
RbNO_{3} 
4.5 
4.659 
1.793 
3.942 
1.882 
0.279 
0.192 
0.137 
KCl 
5.0 
0.527 
0.010 
4.106 
2.981 
0.021 
0.771 
0.194 
KF 
17.5 
0.257 
0.715 
3.802 
2.660 
0.001 
0.361 
0.501 
KOH 
20 
0.169 
1.463 
3.248 
2.489 
0 
0.525 
1.216 
NaClO_{4} 
6.0 
0.669 
0.010 
4.712 
3.301 
0.019 
0.277 
0.138 
NaCF_{3}SO_{3} 
5.4 
0.118 
0.820 
6.693 
3.266 
0.040 
0.299 
0.403 
NaSCN 
18 
0.541 
0.728 
5.391 
3.011 
0.002 
0.439 
0.503 
Me_{4}NBr 
5.5 
5.455 
2.331 
4.973 
7.857 
1.000 
0.565 
0.750 
Me_{4}NCl 
19 
0.875 
0.806 
2.016 
2.570 
0.004 
2.634 
1.150 
Et_{4}NBr 
12 
6.184 
2.797 
2.481 
2.611 
0.068 
1.782 
1.555 
Et_{4}NCl 
9.0 
0.198 
0.010 
1.774 
4.991 
0.007 
2.652 
0.941 
Pr_{4}NBr 
9.0 
7.017 
2.978 
4.700 
11.34 
1.224 
1.355 
1.235 
Pr_{4}NCl 
18 
2.583 
1.999 
1.043 
3.163 
0.002 
3.582 
3.598 
Bu_{4}NBr 
10 
5.610 
2.040 
8.00 
2.160 
2.710 
3.540 
1.170 
NH_{3}MeCl 
20 
0.803 
0.541 
3.309 
2.901 
0.089 
0.340 
0.238 
NH_{3}MeNO_{3} 
9.5 
1.043 
0.010 
4.611 
1.369 
0.316 
0.293 
0.244 
NH_{2}Me_{2}ClO_{4} 
7.5 
4.085 
1.603 
4.771 
1.129 
0.752 
0.347 
0.359 
NH_{4}SCN 
23.5 
0.510 
0.010 
6.186 
2.453 
0.169 
0.976 
0.653 
Average 






0.997 
0.942 
^{a} , ^{b} , where, N is the number of experimental data points.
Table 4
BiMSA model parameters for a number of single electrolyte solutions and AARD (%) of its predictions for osmotic and activity coefficients at T=298.15 K and P=0.1 MPa.
Electrolyte 
Max m (mol.kg^{1}) 
(Å) 
(Å.L.mol^{1}) 
(L.mol^{1}) 
K (L.mol^{1}) 
AARD_{Φ }(%)^{a} 
AARD_{γ± }(%)^{b} 
LiBr 
17 
4.900 
0.066 
0.0001 
0.240 
0.739 
2.301 
LiCl 
20 
4.900 
0.069 
0.0001 
0.145 
1.435 
3.306 
LiOH 
4.0 
4.900 
0.104 
0.048 
1.266 
0.236 
0.117 
LiNO_{3} 
20 
4.900 
0.074 
0.061 
0.025 
0.504 
0.947 
LiCH_{3}SO_{3} 
5.5 
4.900 
0.266 
0.127 
0.531 
0.574 
0.138 
HClO_{4} 
16 
5.100 
0.103 
0.060 
0.347 
0.754 
2.407 
HNO_{3} 
28 
5.100 
0.076 
0.110 
0.065 
1.488 
2.652 
CsBr 
5.0 
5.100 
0.032 
0.054 
0.667 
0.336 
0.295 
CsCl 
9.0 
3.842 
0.058 
0.092 
0.852 
0.935 
0.421 
RbCl 
7.8 
3.561 
0.025 
0.036 
0.252 
0.176 
0.182 
RbNO_{3} 
4.5 
3.561 
0.0003 
0.112 
0.910 
0.375 
0.352 
KCl 
5.0 
4.700 
0.064 
0.102 
0.428 
0.737 
0.097 
KF 
17.5 
4.700 
0.042 
0.033 
0.237 
0.701 
1.202 
KOH 
20 
4.700 
0.040 
0.003 
0.000 
0.375 
0.740 
NaClO_{4} 
6.0 
3.550 
0.078 
0.102 
0.217 
0.165 
0.123 
NaCF_{3}SO_{3} 
5.4 
3.550 
0.391 
0.093 
0.196 
0.359 
0.407 
NaSCN 
18 
5.382 
0.142 
0.179 
0.439 
0.831 
1.293 
Me_{4}NBr 
5.5 
5.470 
0.061 
0.150 
1.531 
0.807 
0.877 
Me_{4}NCl 
19 
5.470 
0.022 
0.075 
1.124 
0.622 
0.921 
Et_{4}NBr 
12 
6.725 
0.050 
0.413 
2.000 
0.778 
0.865 
Et_{4}NCl 
9.0 
6.725 
0.013 
0.246 
1.193 
0.670 
1.231 
Pr_{4}NBr 
9.0 
8.610 
0.328 
0.814 
3.350 
0.960 
0.394 
Pr_{4}NCl 
18 
8.610 
0.286 
0.472 
2.331 
1.187 
1.919 
Bu_{4}NBr 
10 
8.292 
0.364 
0.677 
2.722 
4.163 
2.429 
NH_{3}MeCl 
20 
4.530 
0.084 
0.039 
0.594 
0.825 
0.261 
NH_{3}MeNO_{3} 
9.5 
3.903 
0.030 
0.069 
0.326 
0.422 
0.410 
NH_{2}Me_{2}ClO_{4} 
7.5 
3.611 
0.052 
0.102 
1.072 
0.348 
0.446 
NH_{4}SCN 
23.5 
3.455 
0.083 
0.092 
0.061 
1.234 
1.722 
Average 





0.812 
1.016 
^{a} , ^{b} , where, N is the number of experimental data points.
The results shown in Tables 1 to 4 reveal that the consideration of ion pairing increases the accuracy of the model, especially at higher concentrations of salt. The accuracy of the four models for the prediction of mean activity coefficients of KOH and Me_{4}NCl single salt aqueous solutions are compared versus experimental data in Figures 2 and 3 respectively. As shown in these figures, the accuracy of eNRTL and MSANRTL models considerably decreases at higher concentrations of salt solutions.
Figure 2
Mean activity coefficients (γ±) calculated for aqueous KOH solution using the (♦) eNRTL, (▲) MSANRTL, (■) AMSANRTL, and (●) BiMSA models; the experimental values are from Hamer and Wu, (1972) and Lindenbaum and Boyd (1964).
Figure 3
Mean activity coefficients (γ±) calculated for aqueous Me_{4}NCl solution using the (♦) eNRTL, (▲) MSANRTL, (■) AMSANRTL, and (●) BiMSA models; the experimental values are from Hamer and Wu (1972) and Lindenbaum and Boyd (1964).
In order to demonstrate the capability of AMSANRTL model to predict the thermodynamic properties of electrolyte solutions, salting out effect of Bu_{4}NBr in a CO_{2}+water system is considered in this work. As discussed by Lin et al. (2008), the solubility of carbon dioxide in the presence of Bu_{4}NBr drops. This salting out effect is shown in Figure 4. The interaction parameters between CO_{2} and water for these predictions are taken from Hou et al. (2013). The solubility data of carbon dioxide in pure water is predicted by PengRobinson equation of state (Melhem et al., 1989), and for the solution with a mass fraction of 0.09 Bu_{4}NB, solubility is predicted using AMSANRTL model. It is observed that the model results are in satisfactory agreement with the experimental data.
Figure 4
Carbon dioxide solubility versus pressure at a temperature of 283.15 K; symbols represent experimental data from literature: (●), pure water (Houghton et al., 1957); (■), 0.09 mass fraction Bu_{4}NBr (Lin et al., 2008); solid line (──) shows PREOS prediction for pure water, and dash line () denotes AMSANRTL model predictions for the electrolyte solution.
BiMSA is a model based on the Wertheim formalism theory (Wertheim, 1988, 1987) with considering ion pairing of unlike hydrated ions and chemical association. The capability of this model to predict the osmotic and activity coefficients of different aqueous salt solutions is reported in Table 4, and it is plotted in Figure 5 for some cases. Four adjustable parameters of this model, including , , , and K are fitted using activity coefficients data found in literature for these solutions. The values obtained for these parameters in this work are very close to the values reported in the literature (Marcus, 2008; Wright, 2007). The positive value found for is in accordance with the observation that solution permittivity decreases by increasing salt concentration. The results show that, among the four investigated models in this study, the BiMSA model can predict the osmotic and activity coefficients of different aqueous salt solutions better than the others.
Figure 5
Predictions of the BiMSA model for (a) mean activity coefficient and (b) osmotic coefficient at a temperature of 298.15 K as a function of concentration; symbols represent experimental data: (●), KF(Hamer and Wu, 1972); (■), NH_{3}MeCl (Macaskill and Bates, 1986); (♦), Pr_{4}NBr (Lindenbaum and Boyd, 1964); (▲), HNO_{3} (Hamer and Wu, 1972); solid line (—), model prediction.
4. Conclusions
Electrolyte models are widely used for modelling or predicting the thermodynamic behavior of chemical mixtures. In this work, the ability of four electrolyte models, including eNRTL, MSANRTL, AMSANRTL, and BiMSA to predict the properties of different aqueous electrolyte solutions in a wide range of concentrations is comprehensively evaluated. Literature data for osmotic and activity coefficients of different solutions are used for these evaluations. The results indicate that models which do not consider association between ions present in the solution have relatively large errors, especially at higher concentrations of dissolved salt. The results also denote that both AMSANRTL and BiMSA models can accurately predict the experimental data of osmotic and activity coefficients of different electrolyte solutions. However, the BiMSA model, in comparison to the AMSANRTL model, requires lower number of adjustable parameters, and its parameters have physical meaning. Therefore, it can be concluded that among the four electrolyte models investigated for the prediction of 28 systems in this work, the BiMSA model presents the best performance. The average values of AARD (%) of the BiMSA model in the prediction of osmotic and activity coefficients of different systems investigated in this work are 0.856 and 1.016 respectively. For a further progress, it is recommended that the MSA terms be modified to the unrestricted primitive model, which leads to a more precise description of salt effects at low concentrations. Finally, the introduction of a hard sphere term could take the missing effect into account.
Nomenclature
A : Anion C : Cation K : Equilibrium constant K_{B} : Boltzmann constant N : Number of data P : Ionpair assuming as neutral species P : Pressure S : Salt T : Temperature x : Unbound ion fraction Greek Letter : Nonrandomness factor : Concentration dependent parameter of permittivity : Relative permittivity : Osmotic coefficient : Mean ionic activity coefficient in the molality scale : Ionic diameter : Dimensionless interaction energy parameter v : Stoichiometric number Subscript and Superscript A : Anion aq : Aqueous phase C : Cation i : Component i P : Molecular species W : Water Z : Ion charge 
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