Universal Functional Activity Coefficient Research Articles

Thermodynamic modelling of the partitioning of lysozyme in aqueous two-phase systems composed of polyethylene glycol and salt at different temperatures

Knowledge about the interactions during the protein transfer is necessary to optimize the application of aqueous two-phase systems (ATPS) in purification processes. Theoretical knowledge combined with modelling and simulation can help to select the systems with the best partition values. Therefore, this study aimed to determine new interaction parameters through the contribution groups of lysozyme, using UNIFAC (Universal Functional Activity Coefficient) modelling. First, the partition of lysozyme was carried out, and the polymer molar mass (4000 or 6000), and the effects of the type of salt (Na2SO4 or Na3C6H5O7), the overall composition of the system (3 mooring lines), and temperature T = (293.15, 298.15, 303.15 and 308.15) K were evaluated. The results showed that lysozyme had a preference for the bottom phase of the ATPS system, the increase in the overall composition led to a tendency of increasing the partition coefficient (Kp), and the systems made up of higher molar mass polymers exhibited a bottom Kp value. The results of the thermodynamic parameters showed that the transfer of lysozyme in the phases was governed by entropy contribution. New interaction parameters of the contribution groups of lysozyme in the system phases were estimated. The UNIFAC model showed low deviation values. Therefore, the present results allow estimating new partitioning data for proteins presenting the same contribution groups, which can reduce the number of experiments and optimize the partitioning process.

Surface tension of binary and ternary mixtures mapping with ASP and UNIFAC models based on machine learning

Modeling predictions of surface tension for binary and ternary liquid mixtures is difficult. In this work, we propose a machine learning model to accurately predict the surface tension of binary mixtures of organic solvents-ionic liquids and ternary mixtures of organic solvents-ionic liquids–water and analytically characterize the proposed model. In total, 1593 binary mixture data points and 216 ternary mixture data points were collected to develop the machine learning model. The model was developed by combining machine learning algorithms, UNIFAC (UNIversal quasi-chemical Functional group Activity Coefficient) and ASP (Abraham solvation parameter). UNIFAC parameters are used to describe ionic liquids, and ASP is used to describe organic solvents. The effect of each parameter on the surface tension is characterized by SHAP (SHapley Additive exPlanation). We considered support vector regression, artificial neural network, K nearest neighbor regression, random forest regression, LightGBM (light gradient boosting machine), and CatBoost (categorical boosting) algorithms. The results show that the CatBoost algorithm works best, MAE = 0.3338, RMSE = 0.7565, and R2 = 0.9946. The SHAP results show that the surface tension of the liquid decreases as the volume and surface area of the anion increase. This work not only accurately predicts the surface tension of binary and ternary mixtures, but also provides illuminating insight into the microscopic interactions between physical empirical models and physical and chemical properties.

Predicting viscosity of ionic liquids - water mixtures by bridging UNIFAC modeling with interpretable machine learning

Predicting the viscosity of ionic liquids (ILs)-water mixtures precisely is considerable for diversity applications in chemical industries. In this work, interpretable machine learning models incorporate physics information developed to link the UNIversal quasi-chemical Functional group Activity Coefficient (UNIFAC) models as new predictive tools for the viscosity of ILs-water mixtures. Machine learning algorithms including Adaptive Boosting (AdaBoost), Categorical Boosting (CatBoost), Extreme Gradient Boosting (XGBoost), K-Nearest-Neighbour Regression (KNNR) and Random Forest (RF) are compared, with the Catboost model achieving the best accuracies (RMSE = 0.0084, MSE = 0.0001, MAE = 0.0020, R2 = 0.9941). We demonstrate that the UNIFAC model and the Stokes-Einstein relation assisted to reduce the feature dimensionality, and improve the predictive power of the viscosity of ILs-water mixtures. The describe of the Shapley’s additive explanations (SHAP) and Partial dependence plots (PDP) are giveing expression to the features importance of the UNIFAC model. Our work created a new paradigm to hyperlink the machine learning models with ILs-related properties, the UNIFAC model contains prior physical information to optimize the features of the experiment, and fill the gap of the input features and the viscosity of ILs-water mixtures.

Experimental Study and Modeling of Solid-liquid Equilibrium for Binary and Ternary Pharmaceutical and Food Systems

Background: The experimental measurement of the solubility of a solute in a given solvent is a difficult task; hence, there is a need for reliable thermodynamic models for its prediction. However, these models require either molecular or group interaction parameters, which are not always available. Objective: The objective of the present study is to measure experimentally the solubility of different solutes in solvents selected from pharmaceutical or food fields on the basis that not all the required model interaction parameters are available to use the obtained experimental data for their determination. Methods: The experimental study of solid-liquid equilibrium for chosen binary and ternary systems at different temperatures was carried out using differential scanning calorimetry to determine the essential thermophysical properties like melting temperature and enthalpy. The modeling of these phase equilibrium data was performed using thermodynamic models like Random Two Liquids (NRTL) and Universal Functional Activity Coefficient (UNIFAC). The required NRTL interaction parameters were determined by minimizing a well-defined objective function using the simplex method. Results: The solubilities of the solutes in the different considered solvents at different temperatures obtained experimentally provided the required NRTL molecular interaction parameters. Solute solubilities were obtained using NRTL, UNIFAC, and ideal models. The comparisons showed an excellent agreement between the experimental values and the NRTL results, contrary to UNIFAC and ideal case models. Conclusion: This study shows the importance of thermodynamic modeling to predict solubility data that may be difficult, time-consuming, and costly to obtain experimentally.

Thermodynamically predicting liquid/solid phase change of long-chain fatty acid methyl esters (FAMEs) and its application in evaluating the low-temperature performance of biodiesel

BackgroundLong-chain FAMEs, also called biodiesel, are viewed as the alternative to petroleum diesel for renewability and sustainability. Because the fatty acid profiles significantly varied for the sources, the compositions significantly affected the biodiesel properties, such as cloud points. The cloud point of biodiesel indicates the phenomenon of solid-liquid phase change. Previous models for cloud point predictions were limited to known components in the mixtures. MethodsThe cloud points of the binary, ternary and multicomponent mixtures of fatty acid methyl esters were measured in this study. A cloud point prediction model was established based on phase equilibrium with the modified Universal functional activity coefficient (UNIFAC) model and excess fusion enthalpy for predicting the nonideal behaviors of the components resulting from the molecular shape and molecule interactions. Significant findingsThe developed thermodynamic model accurately predicts the cloud point according to compositions. The model extends the application scope to the low-temperature range. There had eutectic points in the phase diagrams when the mixtures consisted of either saturated FAMEs or unsaturated FAMEs. This study proved that saturated FAMEs regulate the cloud points, but unsaturated FAMEs affect them through group interactions. Moreover, the proposed model can predict unknown FAMEs mixture as it is built on the group contributions.

Designing and Characterization of Low-Temperature Eutectic Phase Change Materials Based on Alkanes

The research presented here focuses on investigating the relationship between the structure of components and the eutectic temperature of binary systems designed as phase change material (PCM). For two-component systems containing long-chain alkanes (n-tetradecane, n-pentadecane, n-hexadecane, or n-heptadecane) and decanoic acid or 1-decanol, experimental data of the solid–liquid equilibrium are presented. The collected data were used to determine the composition and temperature of eutectic mixtures that are promising PCMs with a narrow melting point range. An essential aspect of this work is to confirm the possibility of determining eutectics of similar systems using predictive models, mainly the modified Dortmund universal quasichemical functional group activity coefficients method, without the need to perform expensive and time-consuming experimental research. The experiments were supplemented with the physicochemical characteristics (density and viscosity) of the liquid mixtures. The obtained PCM was closed in a polymer matrix to prevent material leakage into the environment, and the obtained composite materials were characterized by using scanning electron microscopy and differential scanning calorimetry techniques.

Liquid-liquid equilibrium data for systems formed by PEG (4000 or 6000) or alcohol (1-propanol or 2-propanol) + potassium phosphate + water: Experimental measurements, correlations and thermodynamic modeling

Experimental liquid–liquid equilibrium data for aqueous two-phase systems (ATPSs) formed by polyethylene glycol (4000 or 6000 g mol−1)/potassium phosphate and alcohol (1-propanol or 2-propanol)/potassium phosphate + water were determined at 298.15 K and pH 7.0. Four binodal curves and five tie-lines for each curve were obtained. The effective excluded volume (EEV) and salting-out effect parameters were determined. The diagrams formed by PEG 6000/salt and 1-propanol/salt showed a higher biphasic region as well as higher values of EEV and salting-out coefficient when compared to those of ATPSs formed by PEG 4000/salt or 2-propanol/salt, respectively. The interaction parameters were obtained using the UNIversal Functional Activity Coefficient (UNIFAC) model. From the interaction energy values calculated using the new binary interaction parameters estimated in the thermodynamic modeling it was possible to gain a better understanding of the phase-forming process. The UNIFAC model showed satisfactory agreement between the experimental data and the correlated values, indicating that its use is reliable to obtain equilibrium data for systems that contain the same contribution groups.

Refrigerants design for an absorption refrigeration machine using group contribution methods

The use of the group contribution concept in the design of refrigerants for a single effect absorption refrigeration machine is presented. Chosen functional groups were combined according to the known chemistry rules to generate refrigerant candidates. The prediction of vapor-liquid phase equilibria data using the UNIFAC (Universal Functional Activity Coefficient) model and the Peng-Robinson equation of state and the resolution of heat and mass balances over different compartments led to the coefficient of performance (COP). An exergy analysis was carried out in order to assess the performance of the machine by calculating the exergy efficiency, the second law efficiency and the exergy destruction ratio. The design strategy was tested with chosen groups such as Fluor (F) and Methyl (CH3), Carbon (C), Methine (CH) and Methylene (CH2) and with N, N Dimethylformamide (DMF) as the absorbent. The results showed Interesting refrigerant-absorbent pairs with reasonably high COP values.

Enhanced thermodynamic modelling for hydrothermal liquefaction

Hydrothermal liquefaction is a promising technology to produce drop-in biofuels from wet biomasses. In this paper a thermodynamic analysis, including chemical equilibrium and gas–liquid equilibrium in high-pressure conditions was conducted. The chemical equilibrium model is based on the minimization of the Gibbs free energy, while the high-pressure gas–liquid equilibrium model is implemented by using the modified universal functional activity coefficient (UNIFAC) model and the Soave-Redlich-Kwong (SRK) equation of state. The Gibbs free energy minimization is performed through the minimization algorithm based on Sequential Least SQuares Programming (SLSQP) available in the Python environment. The research aims at providing an engineering predictive tool to support the design of biomass hydrothermal liquefaction and optimize its operation.

Modeling, simulation, and optimization for producing ultra-low sulfur and high-octane number gasoline by separation and conversion of fluid catalytic cracking naphtha

Contradiction between the desulfurization and loss of octane number (ON) for fluid catalytic cracking (FCC) naphtha is the long-term problem of clean gasoline production. Moreover, ON loss is substantially aggravated during deep hydrodesulfurization because of the global limited supply of sulfur for gasoline. This paper introduced a novel process for ultra-clean gasoline production that solves the above problem by using a unique separation approach. The components were separated by the coupling of distillation and solvent extraction, which the separation efficiency depends on the relationship between solvent and feedstock components. However, the compositions of FCC naphtha are complicated and always changing due to different processing technologies. Therefore, changes in the material composition must be considered during production. In this study, ternary liquid–liquid equilibrium (LLE) was explored to investigate the interaction of olefin, sulfide, and sulfolane solvent. A universal quasi-chemical functional group activity coefficient (UNIFAC) group contribution model was selected to predict the product composition when the FCC naphtha was treated by this process. The lack and necessity of group interaction parameters for the prediction were obtained through the regressive calculation of LLE results. A simulation method was established by Aspen Plus, and the reliability was well illustrated. Finally, the high olefin content of FCC naphtha critical components was analyzed by this simulation method. The sulfur and olefin levels of the product were 9.0 mg/kg and 18.6 wt%, respectively. The operation conditions simulated using the proposed technique met the requirements for the latest commercial gasoline worldwide.

Hygroscopic growth of aerosol particles consisted of oxalic acid and its internal mixture with ammonium sulfate for the relative humidity ranging from 80% to 99.5%

The hygroscopicity of atmospheric aerosol particles plays an important role in their effects on the environment and the public health. Oxalic acid (OA) has been recognized as the dominant dicarboxylic acid in the urban and the remote aerosol particles, so a number of studies have investigated the hygroscopicity of aerosols consisted of pure OA and its mixture with inorganic salt. However, few experimental studies have focused on the hygroscopicity of nanoscale particles at high relative humidity (RH) below saturation of water vapor (i.e., RH = 90–100%) due to the limitation of traditional measuring instruments. In this work, the hygroscopic growth factor (GF) of the aerosol particles composed of pure OA and internally mixed OA-ammonium sulfate (AS) at RH = 80–99.5% were studied using a high humidity tandem differential mobility analyzer (HHTDMA). The experimental results were used to verify the applicability of models including the ideal solution model, the extended aerosol inorganics model (E-AIM) combining with the revised universal quasi-chemical functional group activity coefficients (UNIFAC-Peng) model, the Aerosol Inorganic–Organic Mixtures Functional groups Activity Coefficients (AIOMFAC) model and the Zdanovskii–Stokes–Robinson (ZSR) model. According to the experimental results, the effect of RH, the composition and the initial dry particle diameter (D0) on the hygroscopic growth factor (GF) and the effective hygroscopicity parameter (κ) of particles at RH = 80–99.5% were analyzed in details. It has been found that for pure OA particles with D0 = 80/100 nm, the prediction of the UNIFAC-Peng model and the AIOMFAC model becomes more accurate than the ideal solution model with the increasing of RH, while all models cannot describe the GF of smaller particles at RH = 80–99.5%. For mixed OA-AS aerosol particles, the chemical reaction is an important reason for the discrepancies between the HHTDMA results and the model prediction. For the pure OA aerosol particles at RH ≤ 96% and particles containing AS at RH ≤ 99.5%, the sensitivity of GF to D0 is positively and negatively correlated with RH and D0, respectively. However, for all aerosol types in this study, the sensitivity of GF to RH is positively correlated with both D0 and RH. In addition, the κ based on the measurement is clearly dependent on D0 (at RH = 95–99.5%), RH and the particle composition (at RH = 80–99.5%). The measured hygroscopicity of aerosol particles at high RH might be useful in addressing challenges in solving the discrepancies between the κ measured under subsaturated and supersaturated conditions.

Artificial neural network approach for predicting blood brain barrier permeability based on a group contribution method

Background and ObjectiveThe purpose of this study was to develop a quantitative structure-activity relationship (QSAR) model for the prediction of blood brain barrier (BBB) permeability by using artificial neural networks (ANN) in combination with molecular structure and property descriptors. MethodsUsing a database composed of 300 compounds, 52 structure descriptors obtained based on the universal quasichemical functional group activity coefficients (UNIFAC) group contribution method and the selected 8 molecular property descriptors were used as the network inputs, whereas logBB values of compounds constituted its output. ResultsThe correlation coefficient R of the constructed prediction model, the relative error (RE) and the root mean square error (RMSE) was 0.956, 0.857, and 0.171, respectively. These indicators reflected the feasibility, robustness and accuracy of the prediction model. Compared with the previously published results, a significant improvement in the predictions of the proposed ANN model was observed. ConclusionsANN model based on the group contribution method could achieve a satisfactory performance for logBB prediction.

Isolation of potential α-glucosidase inhibitor from Inonotus obliquus by combining ultrafiltration-liquid chromatography and consecutive high-speed countercurrent chromatography.

Inonotus obliquus is a rare medicinal fungus that contains several potential therapeutic ingredients. In this study, the α-glucosidase inhibitory activity of I. obliquus was examined, and a potential α-glucosidase inhibitor, (E)-4-(3,4-dihydroxyphenyl)but-3-en-2-one, was isolated from the I. obliquus extract through ultrafiltration-liquid chromatography (UF-LC). Consecutive high-speed countercurrent chromatography (HSCCC) was used for separation to obtain large quantities of the target compound. The universal quasi-chemical functional group activity coefficient (UNIFAC) model was utilized to prepare a two-phase solvent system, n-hexane/ethyl acetate/ethanol/water (4 : 4.5 : 3.5 : 5, v/v/v/v), wherein the proportions of n-hexane/ethyl acetate/ethanol/water in the stationary and mobile phases were 19.8 : 19.7 : 7.9 : 2.2 (v/v/v/v) and 1 : 16.4 : 57.5 : 136.6 (v/v/v/v), respectively. A flow rate of 2.5 mL min-1 and a column speed of 860 rpm were maintained. Consequently, 10.3 mg of the target compound (95.9% purity) was obtained from 900 mg (6 × 150 mg) of the I. obliquus extract. The use of the UNIFAC model, in combination with consecutive HSCCC separations, allows the purification of large quantities of samples over a short time. Furthermore, the volume of the organic solvent required is reduced. Thus, UF-LC is an effective technique for screening potential α-glucosidase inhibitors isolated from I. obliquus. This can ultimately aid in the discovery of bioactive compounds for the prevention and treatment of diabetes.

Thermophysical Properties Investigation of High-Density Jet Fuel with Alcohols Additives

The densities and dynamic viscosities of three binary liquid mixtures of tricyclo [5.2.1.02,6] deca-3,8-diene(endo-DCPD) with ethanol, 2-propanol and 1-pentanol at different temperatures (293.15, 303.15 and 313.15) K and atmospheric pressure were measured over the entire compositional range. The excess molar volume, viscosity deviation and excess Gibbs energy for activation of the viscous flow of mixtures were calculated and adjusted by the Redlich–Kister equation. The excess molar volume data were correlated using Prigogine–Flory–Patterson (PFP) theory. Five typical semi-empirical correlations with one and two interaction parameters and predictive models UNIFAC Dortmund_VISCO (Universal Functional Activity Coefficients modified by Dortmund-Viscosity) and ASOG_VISCO (Analytical Solution Group-Viscosity) with newly estimated interaction parameters were used to correlate viscosity. In addition, excess Gibbs free energy results were estimated using local composition concept and group contribution models. All of these results provide important information on the research and application of high-density jet fuel with green fuel as additives.

Impacts of water partitioning and polarity of organic compounds on secondary organic aerosol over eastern China

Abstract. Secondary organic aerosol (SOA) is an important component of fine particular matter (PM2.5). Most air quality models use an equilibrium partitioning method along with the saturation vapor pressure (SVP) of semivolatile organic compounds (SVOCs) to predict SOA formation. However, the models typically assume that the organic particulate matter (OPM) is an ideal mixture and ignore the partitioning of water vapor to OPM. In this study, the Community Multiscale Air Quality model (CMAQ) is updated to investigate the impacts of water vapor partitioning and nonideality of the organic–water mixture on SOA formation during winter (January) and summer (July) of 2013 over eastern China. The updated model treats the partitioning of water vapor molecules into OPM and uses the universal functional activity coefficient (UNIFAC) model to estimate the activity coefficients of species in the organic–water mixture. The modified model can generally capture the observed surface organic carbon (OC) with a correlation coefficient R of 0.7 and the surface organic aerosol (OA) with the mean fractional bias (MFB) and mean fractional error (MFE) of −0.28 and 0.54, respectively. SOA concentration shows significant seasonal and spatial variations, with high concentrations in the North China Plain (NCP), central China, and the Sichuan Basin (SCB) regions during winter (up to 25 µg m−3) and in the Yangtze River Delta (YRD) during summer (up to 16 µg m−3). In winter, SOA decreases slightly in the updated model, with a monthly averaged relative change of 10 %–20 % in the highly concentrated areas, mainly due to organic–water interactions. The monthly averaged concentration of SOA increases greatly in summer, by 20 %–50 % at the surface and 30 %–60 % in the whole column. The increase in SOA is mainly due to the increase in biogenic SOA in inland areas and anthropogenic SOA in coastal areas. As a result, the averaged aerosol optical depth (AOD) is increased by up to 10 %, and the cooling effect of aerosol radiative forcing (ARF) is enhanced by up to 15 % over the YRD in summer. The aerosol liquid water content associated with OPM (ALWorg) at the surface is relatively high in inland areas in winter and over the ocean in summer, with a monthly averaged concentration of 0.5–3.0 and 5–7 µg m−3, respectively. The hygroscopicity parameter κ of OA based on the κ–Köhler theory is determined using the modeled ALWorg. The correlation of κ with the O:C ratio varies significantly across different cities and seasons. Analysis of two representative cities, Jinan (in the NCP) and Nanjing (in the YRD), shows that the impacts of water partitioning and nonideality of the organic–water mixture on SOA are sensitive to temperature, relative humidity (RH), and the SVP of SVOCs. The two processes exhibit opposite impacts on SOA in eastern China. Water uptake increases SOA by up to 80 % in the organic phase, while including nonunity activity coefficients decreases SOA by up to 50 %. Our results indicate that both water partitioning into OPM and the activity coefficients of the condensed organics should be considered in simulating SOA formation from gas–particle partitioning, especially in hot and humid environments.

Transesterification of triolein and methanol with Novozym 435 using co-solvents

To enhance the reaction rate and yield during biodiesel synthesis with Novozym 435 (an immobilized lipase), a co-solvent method was applied, after which the relationships between reaction time and reaction yield during the single-phase enzymatic transesterification of triolein and methanol in the presence of co-solvents such as tetrahydrofuran, acetone, and hexane were investigated. The addition of hexane led to inactivation of Novozym 435, whereas in the presence of acetone or tetrahydrofuran, the reaction was accelerated and the yield was higher than with a conventional solvent-free enzymatic reaction. There was a clear difference in the dispersion of Novozym 435 particles between with and without co-solvents. The co-solvents prevented particle aggregation from occurring during the reaction without co-solvent. This is the likely reason for the enhanced reaction yield in the presence of co-solvents. To better understand the difference among the co-solvents employed, we estimated phase compositions during transesterification using the LLE (liquid-liquid equilibrium)-UNIFAC (UNIversal quasichemical Functional group Activity Coefficients) model, and examined the effect of these compositions on the reaction and dispersion of the glycerin by-product phase. Using the model, we also accounted for why hexane led to inactivation by estimating the transfer free energy of methanol, glycerin, and water between the initial bulk reaction solution and the water layer on the enzyme surface.

Liquid–Liquid Equilibrium Data and Thermodynamic Modeling for Aqueous Two-Phase System Peg 1500 + Sodium Sulfate + Water at Different Temperatures

Liquid–liquid equilibrium data of aqueous two phase systems (ATPS) composed of polyethylene glycol (PEG) 1500 g·mol–1 + sodium sulfate + water at T = (293.15, 303.15 and 313.15) K and p = 0.1 MPa were determined. The density of the top and bottom phases of tie-lines (TL) was determined. The universal functional activity coefficient (UNIFAC) model was correlated to the experimental tie-line data, and the root-mean-square deviations (RMSD) between experimental and calculated data were considered in the calculation. The salting-out effect was evaluated using the model of the effective volume (EEV). The temperature had no influence on the binodal curves and TL, this effect can be attributed to a small enthalpic contribution in the phase separation process. Increases in the ATPS constituents concentrations resulting in an increase in the density of the phases. The increase in temperature resulted in an increase in the salting-out effect. The highest estimated value of EEV was obtained for a system at 293.15 K....

Blended E85–Diesel Fuel Droplet Heating and Evaporation

The multidimensional quasi-discrete (MDQD) model is applied to the analysis of heating and evaporation of mixtures of E85 (85 vol % ethanol and 15 vol % gasoline) with diesel fuel, commonly known as “E85–diesel” blends, using the universal quasi-chemical functional group activity coefficients model for the calculation of vapor pressure. The contribution of 119 components of E85–diesel fuel blends is taken into account, but replaced with smaller number of components/quasi-components, under conditions representative of diesel engines. Our results show that high fractions of E85–diesel fuel blends have a significant impact on the evolutions of droplet radii and surface temperatures. For instance, droplet lifetime and surface temperature for a blend of 50 vol % E85 and 50 vol % diesel are 23.2% and up to 3.4% less than those of pure diesel fuel, respectively. The application of the MDQD model has improved the computational efficiency significantly with minimal sacrifice to accuracy. This approach leads to a s...

Solid–liquid equilibria for selected binary systems containing diphenyl carbonate

The solid–liquid equilibrium (SLE) data for five binary systems containing diphenyl carbonate (DPC), i.e., ethanol, 1-propanol, 2-propanol, diethyl carbonate, or dipropyl carbonate + DPC, were determined experimentally. We designed a new experimental apparatus based on a synthetic method for the SLE measurements. We verified the applicability of the designed apparatus by determining the binary SLE data for the system dimethyl carbonate + DPC. The experimental SLE data were represented by a non-random two-liquid model. Predictions of SLE data for the binary systems were also tested on the basis of the National Institution of Standards and Technology-modified universal functional activity coefficient group contribution model.

Solvent screening and liquid-liquid measurement for extraction of phenols from aromatic hydrocarbon mixtures

The separation of phenolic compounds from low temperature coal tar has great significance for further application of the coal tar. In this paper, the separation of special phenolic compounds from model coal tar (phenols + toluene) was studied. The universal quasi chemical functional group activity coefficient (UNIFAC) and conductor-like screening model COSMO-SAC (segment activity coefficient) was carried out to screen suitable solvent. Ethylene glycol was selected as the potential solvent to extract the phenols from aromatic hydrocarbon, evaluated by area of two-phase region and the criterion of solvent power (SPi∞), selectivity (S∞) and performance index (PI). Liquid-liquid equilibria of the ethylene glycol + phenols + toluene ternary systems were measured in the temperature range (303.15–323.15) K under 101.3 kPa, and the results showed that ethylene glycol has a high extraction efficiency with distribution coefficient (between 2 and 12) and selectivity (between 7 and 255). Moreover, the NRTL and UNIQUAC models were successfully applied to correlate the experimental LLE data with RMSD less than 1.85%, indicating that both models can accurately describe LLE behaviour of this system. Thus, the corresponding binary interaction parameters would be helpful for separation process designing or optimizing.