Prediction Of Vapor-liquid Equilibrium Data Research Articles

Modeling of Vapor-Liquid Equilibrium for Electrolyte Solutions Based on COSMO-RS Interaction

The modeling of phase equilibrium for electrolyte solutions plays an important role in chemical thermodynamics. Modeling and calculation of the vapor-liquid equilibrium (VLE) for an electrolyte solution are studied in this paper. The influence mechanism for the microscopic interaction on the macroscopic VLE is further studied by COSMOtherm. Then, a developed model is proposed. The interaction parameters in the model are linked to the COSMO interaction energy (H_int). The interaction equations are remodeled using 1stopt software, and components are used to replace the “interaction parameters” used in a previous model. The developed model can be used to successfully predict VLE data for electrolyte solutions based on the α and hi parameters.

Isobaric vapor–liquid equilibria and distillation process design for separating ketones in biomass pyrolysis oil

Biomass pyrolysis oil is a renewable resource and some of compounds (such as ketones, phenols and furans) in biomass pyrolysis oil are very useful compounds that can be used as raw materials for production of fine chemicals. Vapor-liquid equilibrium (VLE) data are critical and fundamental for designing distillation separation process. The VLE experiment and calculation of ketone-related binary systems (2-butanone + cyclopentanone, 2,3-butanedione + cyclopentanone) at atmospheric pressure are studied in this work. Binary interaction parameters of activity coefficient models (NRTL, Wilson and UNIQUAC) are obtained by correlating VLE experimental data. The new group CH2COCH2 is defined and the new group interaction parameters are presented and used to predict VLE data by UNIFAC-DMD model. The distillation separation process of ketones is designed and simulated based on VLE results. This work will provide theoretical basis and technical guidance for industrial separation process of biomass pyrolysis oil.

Prediction of phase equilibrium of CO2/cyclic compound binary mixtures using a rigorous modeling approach

Vapor liquid equilibrium (VLE) data has significant role in designing processes which include vapor and liquid in equilibrium. Since it is impractical to measure equilibrium data at any desired temperature and pressure, particularly near critical region, thermodynamic models based on equation of state (EOS) are usually used for VLE estimating. In recent years due to the development of numerical tools like artificial intelligence methods, VLE prediction has been find new alternatives.In the present study a novel method called Least-Squares Support Vector Machine (LSSVM) used for predicting bubble/dew point pressures of binary mixtures containing carbon dioxide (CO2)+cyclic compounds as function of reduced temperature of the system, critical pressure, acentric factor of the cyclic compound, and CO2 composition. A 333 binary equilibrium data points of CO2 and six cyclic compounds within temperature and pressure ranges of 308.15–473.15K and 0.5–27.71MPa were used to develop the model. Results show that the proposed model is able to predict VLE data for binary systems containing supercritical or near-critical CO2/cyclic compounds with an acceptable average absolute relative deviation percent (AARD%) of 3.9381% and the coefficient of determination (R2) value of 0.9980. For detection of the probable doubtful experimental data, and applicability of the model, the Leverage statistical approach performed on the data sets. This algorithm showed that the proposed LSSVM model is statistically valid for VLE prediction and the whole phase equilibrium data points are in applicability domain of the model.

CORRELATION AND PREDICTION OF VLE OF WATER + CONGENER MIXTURES FOUND IN ALCOHOLIC BEVERAGES USING AN ARTIFICIAL NEURAL NETWORK

Artificial neural networks have been used for the correlation and prediction of vapor-liquid equilibrium data of binary water mixtures found in alcoholic beverage production. The main interest of the study is the accurate modeling of the bubble pressure and concentration of congeners in the vapor phase (substances different from ethanol and water), considered to be an important enological parameter in the alcoholic industry. Nine binary water + congener mixtures were considered for analysis. Vapor-liquid equilibrium data of these systems were taken from the literature (333 data points for training and 111 data points for testing), the artificial neural network results were compared with available literature data, and the accuracy of the modeling is discussed. The study shows that the neural network model is a good alternative method for the estimation of phase equilibrium properties for this type of mixture. Supplemental materials are available for this article. Go to the publisher's online edition of Chemical Engineering Communications to view the free supplemental file. http://www.informaworld.com/smpp/title~db=all~content=t713454788

Prediction of Vapor-Liquid Equilibrium Data from C—H Band Shift of IR Spectra in Some Binary Systems

Prediction of vapor-liquid equilibrium (VLE) is extremely necessary to separate liquid mixture in chemical production, especially when the required experimental data are difficult to measure, or the measurement is not economical. The infinite dilution activities can be used to predict VLE. However, it needs both the ends of the activities that are difficult to obtain for many systems. In the present study, a new model is proposed for correlating the frequency shift of C—H stretching band of IR spectra over the whole concentration. Investigated mixtures include water/2-propanol, water/ N, N-dimethylformamide (DMF), water/methanol, water/ethanol, water/1,4-dioxane, and water/dimethylsulfoxide (DMSO) systems. Simultaneous correlations of C—H frequency shift and VLE data are made. Furthermore, the VLE data were predicted with satisfactory results by the parameters obtained from IR spectra coupled with one of the infinite dilution activity coefficients.

Vapor–liquid equilibria for the binary system 2,2−dimethylbutane + 1,1−dimethylpropyl methyl ether (TAME) at 298.15, 318.15, and 338.15 K

Vapor–liquid equilibrium (VLE) data are reported for the binary mixtures formed by 2,2-dimethylbutane and the branched ether 1,1-dimethylpropyl methyl ether (tert-amyl methyl ether or TAME). A Gibbs-van Ness type apparatus was used to obtain total vapor pressure measurements as a function of composition at 298.15, 318.15, and 338.15K. Deviations from Raoult’s law are positive and very small. These VLE data are analyzed together with excess enthalpy (HmE) data previously reported at 298.15K using the UNIQUAC model. The modified UNIFAC (Dortmund) model is used to predict VLE data.

Vapor−Liquid Equilibria for the Binary System Hexane + 1,1-Dimethylpropyl Methyl Ether at 298.15, 308.15, 318.15, and 328.15 K

Vapor−liquid equilibrium (VLE) data are reported for the binary mixtures formed by hexane and the branched ether 1,1-dimethylpropyl methyl ether (tert-amyl methyl ether or TAME). A Gibbs−van Ness type apparatus was used to obtain total vapor pressure measurements as a function of composition at 298.15, 308.15, 318.15, and 328.15 K. The system shows positive deviations from Raoult's law. VLE data are analyzed together with excess enthalpy ( ) and excess volume ( ) data previously obtained at 298.15 K for these mixtures. The UNIQUAC model, the lattice-fluid model, and the Flory theory are used to simultaneously correlate VLE and data and to correlate or predict data. The original UNIFAC group contribution model and the modified UNIFAC (Dortmund model) are used to predict VLE data.

Vapor–liquid equilibria and excess properties of octane + 1,1-dimethylpropyl methyl ether (TAME) mixtures

Vapor–liquid equilibrium (VLE) data are reported for the binary mixtures formed by octane and the branched ether 1,1-dimethylpropyl methyl ether ( tert-amyl methyl ether or TAME). A Gibbs–van Ness type apparatus was used to obtain total vapor pressure measurements as a function of composition at 298.15, 308.15, 318.15 and 328.15 K. The system shows positive deviations from Raoult’s law. These VLE data are analyzed together with data previously reported for octane+TAME mixtures: VLE data at 323.15 and 423.15 K, excess enthalpy ( H m E) data at 298.15 and 313.15 K and excess volume ( V m E) data at 298.15 K. The UNIQUAC model, the lattice–fluid (LF) model, and the Flory theory are used to simultaneously correlate VLE and H m E data. The two latter models are then used to predict V m E data. The original UNIFAC group contribution model and the modified UNIFAC (Dortmund model) are used to predict VLE data.

Potential applications of artificial neural networks to thermodynamics: vapor–liquid equilibrium predictions

The associative property of artificial neural networks (ANNs) and their inherent ability to “learn” and “recognize” highly non-linear and complex relationships finds them ideally suited to a wide range of applications in chemical engineering. Dynamic Modeling and Control of Chemical Process Systems and Fault Diagnosis are the two significant applications of ANNs that have been explored so far with success. This paper deals with the potential applications of ANNs in thermodynamics — particularly, the prediction/estimation of vapor–liquid equilibrium (VLE) data. The prediction of VLE data by conventional thermodynamic methods is tedious and requires determination of “constants” which is arbitrary in many ways. Also, the use of conventional thermodynamics for predicting VLE data for highly polar substances introduces a large number of inaccuracies. The possibility of applying ANNs for VLE data prediction/estimation has been explored using the back propagation algorithm. The methane–ethane and ammonia–water systems have been studied and the VLE predictions have been found to be accurate to within ±1%. Preliminary results confirm exciting possibilities of ANNs for applications to thermodynamics of mixtures. Advantages and limitations of this application are also discussed. An heuristic approach to reduce the trial and error process for selecting the “optimum” net architecture is discussed.

UNIQUAC interaction parameters for molecules with OH group on adjacent carbon atoms in aqueous solution determined by molecular mechanics—glycols, glycerol and glucose

UNIQUAC interaction parameters have been determined, using molecular mechanics calculations, for 1,2-ethanediol, 1,2-propanediol, glycerol and glucose with water in aqueous solution. Conformational space for individual pairs of molecules was explored using a stochastic method, the Boltzmann Jump Procedure (BJP). The predicted vapor-liquid equilibrium (VLE) data for the water 1,2- ethanediol system are in excellent agreement with the experimental values and good results were obtained for the water glycerol system by taking larger samples. This was not true for the other two systems for which there were difficulties in sampling conformational space in a statistically acceptable manner. As the parameters represent interactions per unit molecular surface area and all of the molecules consist of CH 2OH or CH 2O units, the values obtained for the water 1,2- ethanediol system were used to predict VLE data for the other systems. This gave results in good agreement with the experimental data. The possibility of transferring interaction parameters is very interesting, especially as it can be applied to systems for which the additivity principle of the UNIFAC method fails. Simulations were carried out for 1,2-ethanediol, 1,2-propanediol and glycerol, surrounded by different numbers of water molecules. The interaction energy was observed to be linearly dependent on the number of water molecules present. This constant increment means that the additional interaction energy for each water molecule added is the same. This might be another aspect of transferability of interaction parameters.

Correlation and prediction of phase equilibria in liquid mixtures by means of the uniquac equation

The prediction of Vapor-Liquid Equilibrium data (VLE) from Excess Enthalpy (h e) data by means of the Hanks-Gupta-Christensen (HGC) method was examined. VLE data for a variety of binary and ternary mixtures can be succesfully predicted using this method in conjunction with an appropiate model for the Excess Gibbs Energy (g E). In this study, the well known UNIQUAC model for (g E) was chosen. Correlation of VLE data using this equation was examined. Results from this correlation were compared with VLE data predicted from h E data by means of the HGC method and good agreement was found. In this prediction, the parameters of the UNIQUAC model are evaluated by fitting the experimental h E data to the algebraic equation for h E derived from the g E model. The dependence of the accuracy of the predictions on the experimental uncertainties of h E data and the values obtained for the UNIQUAC parameters by fitting these data to the algebraic equation for h E was also discussed.

Prediction of vapor-liquid equilibrium from excess enthalpy data for binary ether +n-alkane or cyclohexane mixtures

Vapor liquid equilibrium (VLE) is successfully predicted from excess enthalpy HE data for binary ether + n-alkane or cyclohexane mixtures. Parameters for the continuous linear association model (CLAM) and for the UNIQUAC Model for the excess Gibbs energy GE were determined from HE data measured at a low temperature (ambient temperature). These parameters are used to predict VLE data at low and high temperatures. The dependence of the accuracy of predictions on the set of HE data chosen to evaluate the parameters and on the model for GE are discussed.

Thermodynamics of alcohol-alcohol mixtures. 2. Prediction of vapor-liquid equilibrium data from heat of mixing data for alcohol-alcohol mixtures

An expression for the heat of mixing (h /SUP E/ ) of binary mixtures based on the assumption of the continuous linear association of both components is fitted to experimental h /SUP E/ data of alcohol-alcohol binary mixtures to determine the adjustable parameters. By use of these parameters, either isothermal or isobaric vapor-liquid equilibrium data are predicted following the method of Hanks, Gupta, and Christensen for 21 binary systems for which VLE and h /SUP E/ data have been found in the literature. The predictions obtained agree well with the experimental vapor-liquid equilibrium values even when the temperature difference between VLE and h /SUP E/ data is large.

The prediction of vapor-liquid equilibrium data from heat of mixing data for three non-ideal binary systems

The method of Hanks et al. was used with the heat of mixing data of McFall et al. for 1,3-butadiene + propylene, 1-butene + methyl tert.-butyl ether, and carbon disulfide + methanol to predict the vapor-liquid equilibrium behavior for these systems. The method involves curve-fitting an excess enthalpy model derived from an excess Gibbs energy model by means of the Gibbs-Helmholtz equation to the heat of mixing data, determining the adjustable parameters from this fit, and using the original excess Gibbs function equation to predict the vapor-liquid equilibrium behavior. The predicted vapor-liquid equilibrium values were compared with experimental values and good agreement was found.

A generalized one‐parameter redlich‐kwong equation for vapor‐liquid equilibrium calculations

AbstractThe two modified parameters previously proposed for the Redlich‐Kwong (RK) equation of state are simplified and reduced to a one‐parameter model, and extended to vapor‐liquid equilibrium (VLE) calculations involving supercritical components. However, the original RK parameters are still treated as temperature‐ and substance‐dependent.In this investigation, this parameter is generalized in terms of Tr and Ω. The values obtained from the generalized correlation are suitable for calculating VLE data at T ≤ Tc for all components, and at temperatures where some of the components are in the supercritical region.The validity of the generalized correlation is supported by its ability to represent saturated properties of the pure components and to predict VLE data for five binary systems at 58 isothermal conditions, and one ternary system at two isothermal conditions.