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A cubic equation of the state model to correlate the solubility of ionic liquids in partially miscible mixtures with alcohols is presented. The model is obtained using the geometric similitude concept between the diagram pressure-volume-temperature (P-v-T) at saturation conditions and the solubility diagram pressureconcentration- temperature (P-x-T). The model that provides the geometric similitude is the Patel-Teja-Valderrama equation of state including the simple mixing rules proposed by van der Waals with one interaction parameter. The adjustable parameter is estimated by fitting 455 experimental data for 28 binary mixtures (322 data for correlation and 133 data for prediction). The temperature range varies from 277 K to 403 K, with all data at 1 bar. Similar deviations below 10% are obtained for the correlation and prediction steps. The model is compared with different approaches found in the literature and the results show that the proposed model is simpler and provides acceptable results. [ABSTRACT FROM AUTHOR]

The modified Peng–Robinson equation of state proposed by Kwak and Mansoori (PR/KM) is used in a thermodynamic consistency test of phase equilibrium data for binary ethanol + congener mixtures found in alcoholic distillation processes. Congener substances are those components in a must that are present at very low concentration, but their presence is necessary to give the distilled liquor their particular aromatic and tasting characteristics. The congener substances considered in this study are: acetic acid, ethyl acetate, furfural, methanol, 2-methyl-1-propanol, 1-pentanol, 1-propanol and methyl acetate. A flexible area test method is applied to analyse 25 isothermal P – x – y data of ethanol + congener mixtures available in the open literature. The consistency method determines the value of three integral expressions derived from the Gibbs–Duhem equation; one integral is calculated using experimental data only and the other two by using values calculated with the PR/KM model. For all cases, the method gives a clear answer about consistency or inconsistency of a set of isothermal P – x – y data. [ABSTRACT FROM AUTHOR]

Artificial neural networks have been used for the correlation and prediction of solubility data of ammonia in ionic liquids. This solubility of ammonia is highly variable for different types of ionic liquids at the same temperature and pressure, its correlation and prediction is of special importance in the removal of ammonia from flue gases for which effective and efficient solvents are required. Nine binary ammonia + ionic liquids mixtures were considered in the study. Solubility data ( P – T – x ) of these systems were taken from the literature (208 data points for training and 50 data points for testing). The training variables are the temperature and the pressure of the binary systems ( T , P ), being the target variable the solubility of ammonia in the ionic liquid ( x ). The study shows that the neural network model is a good alternative method for the estimation of solubility for this type of mixtures. Absolute average deviations were below 5.6%, for each isothermal data set and overall absolute average deviations were below 3.0%. Only in the range of low solubility (below 0.2 in mole fraction) did predicted solubility give deviations higher than 10%. [ABSTRACT FROM AUTHOR]

Highlights: [•] Solubility data of mixtures containing hydrogen sulfide and sulfur dioxide are analyzed. [•] Data for 12 mixtures of H2S and for 3 SO2 +ionic liquid are tested for thermodynamic consistency. [•] The modified Peng–Robinson equation of state of Kwak and Mansoori is used for modeling solubility. [•] A flexible thermodynamic consistency method is applied to analyze 80 isothermal P-x data sets. [•] Thermodynamic consistency method gives a clear conclusion about consistency or inconsistency. [ABSTRACT FROM AUTHOR]

Abstract: Phase equilibrium data of temperature, pressure and solubility (T-P-x) of hydrofluorocarbon-type refrigerants in different types of ionic liquids over wide ranges of temperatures and pressures are modeled and tested for thermodynamic consistency. Experimental data taken from the literature for nineteen binary mixtures refrigerant + ionic liquids with a total of forty eight isothermal data sets are considered in the study. The modified Peng–Robinson equation of state proposed by Kwak and Mansoori is used for correlating the P-T-x data and a flexible thermodynamic consistency method is applied to analyze the data. Modeling is found acceptable in all cases, meaning that deviations in correlating the data are low, proving at the same time the claimed flexibility of a well-founded model that uses simple van der Waals mixing rules. Thirty eight data sets resulted to be thermodynamically consistent, nine were found to be not-fully consistent and only one set was found to be thermodynamically inconsistent. [Copyright &y& Elsevier]

The ability and limitations of Artificial Neural Networks (ANN) for correlating and predicting properties of fluids are discussed. Common mistakes found in applications of ANN to fluid property estimation presented in the literature are analyzed and a useful and appropriate way of presenting results of these applications is proposed. As a study case the use of ANN for correlating and predicting the solubility of difluoromethane (R-32, C 2 H 2 F 2), in seventeen ionic liquids is performed and discussed. The dependent variable is the solubility of R-32 and the independent variables are the temperature (T) and pressure (P), in addition to properties that identify each Ionic Liquid, such as critical temperature (T c), the critical pressure (P c), the mass connectivity index (λ), the acentric factor (ω), the mass of the cation (M+) and the mass of the anion (M−). Six cases, combining these independent variables in different forms were analyzed. The study shows that although some cases of the proposed artificial neural networks model give good results for training and testing, none of them gives acceptable results for prediction. Finally, some indications and suggestions are given to correctly apply ANN and to present the results in the literature. • The ability and limitations of ANN for predicting properties of fluids are discussed. • Common mistakes on applications of ANN to fluid property estimation are analyzed. • A useful and appropriate way of presenting results of ANN applications is proposed. • Most ANN applications give good training results but have no predicting capabilities. [ABSTRACT FROM AUTHOR]