Methanol-Ethanol-Acetone

Amer, Hamada H.; Paxton, R. R.

doi:10.1021/ie50553a041

Cited by 84 publications

(24 citation statements)

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“…The temperatures of the individual data points are given in the boxes below the main panels. Experimental data: Sapgir (1929) and Amer et al (1956). agreement with the experimental SLE-derived data (d), while AIOMFAC-P1 shows larger deviations from the experimental data at these low temperatures (a). At high temperatures, the VLE data for both AIOMFAC-P1 and AIOMFAC-P3 show similar results (b, e), with slightly larger deviations of γ (x) org 2 (activity coefficient of component 2, i.e.…”

Section: Binary Organic Mixturessupporting

confidence: 64%

Improved AIOMFAC model parameterisation of the temperature dependence of activity coefficients for aqueous organic mixtures

et al. 2015

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Abstract. This study presents a new, improved parameterisation of the temperature dependence of activity coefficients in the AIOMFAC (Aerosol Inorganic-Organic Mixtures Functional groups Activity Coefficients) model applicable for aqueous as well as water-free organic solutions. For electrolyte-free organic and organic-water mixtures the AIOMFAC model uses a group-contribution approach based on UNIFAC (UNIversal quasi-chemical Functional-group Activity Coefficients). This group-contribution approach explicitly accounts for interactions among organic functional groups and between organic functional groups and water. The previous AIOMFAC version uses a simple parameterisation of the temperature dependence of activity coefficients, aimed to be applicable in the temperature range from ∼ 275 to ∼ 400 K. With the goal to improve the description of a wide variety of organic compounds found in atmospheric aerosols, we extend the AIOMFAC parameterisation for the functional groups carboxyl, hydroxyl, ketone, aldehyde, ether, ester, alkyl, aromatic carbon-alcohol, and aromatic hydrocarbon to atmospherically relevant low temperatures. To this end we introduce a new parameterisation for the temperature dependence. The improved temperature dependence parameterisation is derived from classical thermodynamic theory by describing effects from changes in molar enthalpy and heat capacity of a multi-component system. Thermodynamic equilibrium data of aqueous organic and water-free organic mixtures from the literature are carefully assessed and complemented with new measurements to establish a comprehensive database, covering a wide temperature range (∼ 190 to ∼ 440 K) for many of the functional group combinations considered. Different experimental data types and their processing for the estimation of AIOMFAC model parameters are discussed. The new AIOMFAC parameterisation for the temperature dependence of activity coefficients from low to high temperatures shows an overall improvement of 28 % in comparison to the previous model version, when both versions are compared to our database of experimentally determined activity coefficients and related thermodynamic data. When comparing the previous and new AIOM-FAC model parameterisations to the subsets of experimental data with all temperatures below 274 K or all temperatures above 322 K (i.e. outside a 25 K margin of the reference temperature of 298 K), applying the new parameterisation leads to 37 % improvement in each of the two temperature ranges considered. The new parameterisation of AIOMFAC agrees well with a large number of experimental data sets. Larger model-measurement discrepancies were found particularly for some of the systems containing multi-functional organic compounds. The affected systems were typically also poorly represented at room temperature and further improvements will be necessary to achieve better performance of AIOM-FAC in these cases (assuming the experimental data are reliable). The performance of the AIOMFAC parameterisation is typically better for system...

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Section: Binary Organic Mixturessupporting

confidence: 64%

Improved AIOMFAC model parameterisation of the temperature dependence of activity coefficients for aqueous organic mixtures

et al. 2015

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“…The agreement between our values for the vapor pressure of cyclohexane and those of Cruikshank and Cutler (30) (36,37).…”

Section: Vapor Pressure Of Pure Componentssupporting

confidence: 87%

Phase Equilibria in the Systems Acetone–Methanol, Acetone–Cyclohexane, Methanol–Cyclohexane, and Acetone–Methanol–Cyclohexane

Campbell¹,

Anand²

1972

Can. J. Chem.

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The vapor pressure and vapor composition of the methanol-cyclohexane system were investigated at temperatures ranging from 25 to 50' . The same properties of the binary systems: acetone-methanol and acetone-cyclohexane, as well as those of the ternary system : methanol-cyclohexane-acetone, were determined experimentally at 25'. The total pressures and compositions of the vapor for solutions lying close to the critical solution temperature of the binary system or close to the plait point of the ternary system are constant within the range of experimental accuracy. All these systems show azeotropic behavior. The methanol-cyclohexane system has an equimolar excess Gibbs free energy, GE, at 25' of 384.7 cal/mol, while the corresponding equimolar value of GE for the acetone-methanol system at 25' is 102.6 and for the acetone-cyclohexane system, at 25", it is 274.5 cal/mol. La pression de vapeur et la composition de vapeur du systeme methanol-cyclohexane ont Cte ttudites sur une echelle de temperature allant de 25 A 50". Les m&mes proprietes des systtmes binaires: acetone-methanol et acetone-cyclohexane, ainsi que celles des systemes ternaires: methanol-cyclohexane-acetone, ont ete determinees experimentalement a 25". Les pressions totales et les compositions de la vapeur pour des solutions proches de la temperature critique de la solution du systtme binaire ou proches du point de plissement du systeme ternaire, sont constantes dans I'intervalle d'exactitude experimental. Tous ces systemes montrent un comportement azeotropique. Le systeme methanol-cyclohexane a un excts d'energie libre tquimolaire de Gibbs, GE, de 384.7 cal/mol a 25", alors que les valeurs tquimolaires correspondantes de GE pour le systtme acetone-methanol est de 102.6 a 25" et pour le systeme acetone-cyclohexane de 274.5 cal/mol a 25".

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“…The embedded ANN toolbox was used in the programming. For a proper and simpler creation/evaluation of the GNM, VLE data from binary systems (AcetoneButanol at 99.46 kPa [15], Acetone-Ethanol at 101.33 kPa [16], and Acetone-Methanol at 13.33 kPa [17]) were used. The data was composed of y i-xi-P-T information and the boiling points, molecular weight, molecular diameter, critical pressure, critical temperature, and acentric factor of each component.…”

Section: Methodsmentioning

confidence: 99%

“…The mixture Methanol (1) -Ethanol (2) at 101.33 kPa, 348.15 K, x 1=0.242 and y1=0.326 [16], was compared with the predicted values. As evaluated, an error of approximately 18% was obtained with a predicted kij value of -0.016.…”

Section: Fig 3 Andmentioning

confidence: 99%

Vapor-Liquid equilibria modeling using gray-box neural networks as binary interaction parameters predictor

Vyhmeister¹,

Rodríguez-Pino²,

Reyes-Bozo³

et al. 2017

DYNA

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Simulations of vapor-liquid equilibrium (VLE) are widely used given their impact on the scale, design, and extrapolation of different operational units. However, due to a number of factors, it is almost impossible to experimentally study each of the VLE systems. VLE simulations can be developed using representations that are strongly dependent on the nature and interactions of the compounds forming mixtures. A model that helps in predicting these interactions would facilitate simulation processes. A Gray Box Neural Network Model (GNM) was created as Binary Interaction Parameters predictors (BIP), which are estimated using state variables and information from pure components. This information was used to predict VLE behavior in mixtures and ranges not used in the mathematical formulation. The GNM prediction capabilities (including temperature dependency) showed an error level lower than 5% and 20% for mixtures considered and not considered in the training data, respectively.Keywords: Acetone-Alcohol System; Peng-Robinson; Non-Linear Evaluations; ANN prediction. Modelamiento de equilibrio Líquido-Vapor usando redes neuronales grises como predictor de parámetros de interacción binariaResumen Las Simulaciones de Equilibrio Líquido Vapor (VLE) son ampliamente utilizadas dado su impacto en el escalamiento, diseño y extrapolación de diferentes operaciones unitarias. Sin embargo, dado considerable factores, es casi imposible experimentalmente estudiar cada uno de los sistemas de VLE. La simulación de VLE puede ser desarrollada utilizando representaciones que son fuertemente dependientes de la naturaleza e interacción de los compuestos que conforman la mezcla. Un modelo que ayude en la predicción de esas interacciones facilitará el proceso de simulación. Una Red Neuronal Gris (GNM) fue creada como un predictor de parámetros de interacción binaria, los que son estimados utilizando variables de estado e información de componentes puros. Esta información fue utilizada para predecir el comportamiento de VLE en mezclas y rangos no utilizados en la formulación matemática. Las capacidades predictivas del GNM (incluida la dependencia de temperatura) mostraron errores menores al 5% y al 20% para mezclas consideradas y no consideradas en los datos de entrenamiento, respectivamente.

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Methanol-Ethanol-Acetone

Cited by 84 publications

References 0 publications

Improved AIOMFAC model parameterisation of the temperature dependence of activity coefficients for aqueous organic mixtures

Improved AIOMFAC model parameterisation of the temperature dependence of activity coefficients for aqueous organic mixtures

Phase Equilibria in the Systems Acetone–Methanol, Acetone–Cyclohexane, Methanol–Cyclohexane, and Acetone–Methanol–Cyclohexane

Vapor-Liquid equilibria modeling using gray-box neural networks as binary interaction parameters predictor

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