Vapor pressure model

A second example refers to the comparison between the vapor pressure model proposed by Dunn and Tichenor (1988) and Tichenor s mass transfer model (1993). The analytical formulas of both models which describe the concentration in the chamber as a function of time are completely equivalent. They only reinterpret the k, 2, fcj coefficients of the vapor pressure model in terms of N, Df, C d, Mq and L of the mass transfer model, i.e. 

Consequently, it would be expected that the predictive capability of the two models will also be equivalent, and any limitations in terms of prediction of one model will reflect analogous limitations of the other model. Indeed, if the vapor pressure model fails to describe the tailing of the concentration versus time curve - which is an indication of sink effects - the mass transfer model is also unable to describe the same part of the experimental data (Tichenor et al., 1993). It should be noted, however, that the parameters of the mass transfer model have well-defined physical meanings [e.g., vapor pressure (C ), molecular diffusivity (Dy), boundary layer thickness ( )], and the parameter estimation does not rely heavily on curve fitting. The parameter estimation is the first step in the model performance and validation process, as we will see later in this Chapter. 

The property packages available in HYSYS allow you to predict properties of mixtures ranging from well defined light hydrocarbon systems to complex oil mixtures and highly nonideal (non-electrolyte) chemical systems. HYSYS provides enhanced equations of state (PR and PRSV) for rigorous treatment of hydrocarbon systems semiempirical and vapor pressure models for the heavier hydrocarbon systems steam correlations for accmate steam property predictions and activity coefficient models for chemical systems. All of these equations have their own inherent limitations and you are encouraged to become more familiar with the application of each equation. 

Aspen Plus extended Antoine vapor pressure model is... 

The analyst now has available the complete details of the chemical composition of a gasoline all components are identified and quantified. From these analyses, the sample s physical properties can be calculated by using linear or non-linear models density, vapor pressure, calorific value, octane numbers, carbon and hydrogen content. 

There are two approaches to explain physical mechanism of the phenomenon. The first model is based on the existence of the difference between the saturated vapor pressures above two menisci in dead-end capillary. It results in the evaporation of a liquid from the meniscus of smaller curvature ( classical capillary imbibition) and the condensation of its vapor upon the meniscus of larger curvature originally existed due to capillary condensation. 

The first term on the right is the common inverse cube law, the second is taken to be the empirically more important form for moderate film thickness (and also conforms to the polarization model, Section XVII-7C), and the last term allows for structural perturbation in the adsorbed film relative to bulk liquid adsorbate. In effect, the vapor pressure of a thin multilayer film is taken to be P and to relax toward P as the film thickens. The equation has been useful in relating adsorption isotherms to contact angle behavior (see Section X-7). Roy and Halsey [73] have used a similar equation earlier, Halsey [74] allowed for surface heterogeneity by assuming a distribution of Uq values in Eq. XVII-79. Dubinin s equation (Eq. XVII-75) has been mentioned another variant has been used by Bonnetain and co-workers [7S]. 

Polymer simulations can be mapped onto the Flory-Huggins lattice model. For this purpose, DPD can be considered an off-lattice version of the Flory-Huggins simulation. It uses a Flory-Huggins x (chi) parameter. The best way to obtain % is from vapor pressure data. Molecular modeling can be used to determine x, but it is less reliable. In order to run a simulation, a bead size for each bead type and a x parameter for each pair of beads must be known. 

An extensive pesticide properties database was compiled, which includes six physical properties, ie, solubiUty, half-life, soil sorption, vapor pressure, acid pR and base pR for about 240 compounds (4). Because not all of the properties have been measured for all pesticides, some values had to be estimated. By early 1995, the Agricultural Research Service (ARS) had developed a computerized pesticide property database containing 17 physical properties for 330 pesticide compounds. The primary user of these data has been the USDA s Natural Resources Conservation Service (formerly the Soil Conservation Service) for leaching models to advise farmers on any combination of soil and pesticide properties that could potentially lead to substantial groundwater contamination. 

A tabulation of the partial pressures of sulfuric acid, water, and sulfur trioxide for sulfuric acid solutions can be found in Reference 80 from data reported in Reference 81. Figure 13 is a plot of total vapor pressure for 0—100% H2SO4 vs temperature. References 81 and 82 present thermodynamic modeling studies for vapor-phase chemical equilibrium and liquid-phase enthalpy concentration behavior for the sulfuric acid—water system. Vapor pressure, enthalpy, and dew poiat data are iacluded. An excellent study of vapor—liquid equilibrium data are available (79). 

Theoretically based correlations (or semitheoretical extensions of them), rooted in thermodynamics or other fundamentals are ordinarily preferred. However, rigorous theoretical understanding of real systems is far from complete, and purely empirical correlations typically have strict limits on apphcabihty. Many correlations result from curve-fitting the desired parameter to an appropriate independent variable. Some fitting exercises are rooted in theory, eg, Antoine s equation for vapor pressure others can be described as being semitheoretical. These distinctions usually do not refer to adherence to the observations of natural systems, but rather to the agreement in form to mathematical models of idealized systems. The advent of readily available computers has revolutionized the development and use of correlation techniques (see Chemometrics Computer technology Dimensional analysis). 

Mathematical Consistency Requirements. Theoretical equations provide a method by which a data set s internal consistency can be tested or missing data can be derived from known values of related properties. The abiUty of data to fit a proven model may also provide insight into whether that data behaves correctiy and follows expected trends. For example, poor fit of vapor pressure versus temperature data to a generally accepted correlating equation could indicate systematic data error or bias. A simple sermlogarithmic form, (eg, the Antoine equation, eq. 8), has been shown to apply to most organic Hquids, so substantial deviation from this model might indicate a problem. Many other simple thermodynamics relations can provide useful data tests (1—5,18,21). 

Fiend s Constant. Henry s law for dilute concentrations of contaminants ia water is often appropriate for modeling vapor—Hquid equiHbrium (VLE) behavior (47). At very low concentrations, a chemical s Henry s constant is equal to the product of its activity coefficient and vapor pressure (3,10,48). Activity coefficient models can provide estimated values of infinite dilution activity coefficients for calculating Henry s constants as a function of temperature (35—39,49). 

Three Parameter Models. Most fluids deviate from the predicted corresponding states values. Thus the acentric factor, CO, was introduced to account for asymmetry in molecular stmcture (79). The acentric factor is defined as the deviation of reduced vapor pressure from 0.1, measured at a reduced temperature of 0.7. In equation form this becomes ... 

Figure 12-36. Vent sizing model for high vapor pressure systems due to nonequilibrium effects turnaround in temperature is assumed to coincide with the onset of complete vapor disengagement.

This type of liquid is characterized by direction independent, relatively weak dispersion forces decreasing with r-6, when r is the distance between neighbouring molecules. A simple model for this type of liquid, which accounts for many properties, was given by Luck 1 2> it is represented by a slightly blurred lattice-like structure, containing hole defects which increase with temperature and a concentration equal to the vapor concentration. Solute molecules are trapped within the holes of the liquid thus reducing their vapor pressure when the latter is negligible. 

The pair of Eqs. 12, 13 epitomizes the relation between the equilibrium vapor pressure, composition, and chemical potential of the solvent in a clathrate obeying the present model. These expressions were used in the calculation of the thermodynamic properties of gas hydrates30 and have also been formulated by Barrer and Stuart 4 for a clathrate with a single type of cavity and one occluded component they reduce to the equations of ref. 52. 

The fact that both heats of formation and equilibrium pressures of the hydrates of spherical molecules correctly follow from one model must mean that the L-J-D theory gives a good account of the entropy associated with the motions of these solutes in the cavities of a clathrate. That the heat of formation of ethane hydrate is predicted correctly, whereas the theoretical value of its vapor pressure is too low, is a further indication that the latter discrepancy must be ascribed to hindered rotation of the ethane molecules in their cavities. 

Vapor pressures and vapor compositions in equilibrium with a hypostoichiometric plutonium dioxide condensed phase have been calculated for the temperature range 1500 I H 4000 K. Thermodynamic functions for the condensed phase and for each of the gaseous species were combined with an oxygen-potential model, which we extended from the solid into the liquid region to obtain the partial pressures of O2, 0, Pu, PuO and Pu02 as functions of temperature and of condensed phase composition. The calculated oxygen pressures increase rapidly as stoichiometry is approached. At least part of this increase is a consequence of the exclusion of Pu +... 

The solidus, the liquidus, the oxygen-potential model for the solid Pu/0 system, and the oxygen-potential model for the liquid Pu/0 system each depend upon the temperature and composition. Because the oxygen-potential model has a greater effect on the vapor pressure and composition at high temperature than do the solidus and liquidus, we have fixed the functional forms and the parameter values for the oxygen-potential model. We choose the IAEA solidus (32) and determine the liquidus that is consistent with it and with the two parts of the oxygen-potential model. The calculated liquidus, which is based on the liquid model parameters, is very close to the IAEA liquidus (33). 

ADMET polymers are easily characterized using common analysis techniques, including nuclear magnetic resonance ( H and 13C NMR), infrared (IR) spectra, elemental analysis, gel permeation chromatography (GPC), vapor pressure osmometry (VPO), membrane osmometry (MO), thermal gravimetric analysis (TGA), and differential scanning calorimetry (DSC). The preparation of poly(l-octenylene) (10) via the metathesis of 1,9-decadiene (9) is an excellent model polymerization to study ADMET, since the monomer is readily available and the polymer is well known.21 The NMR characterization data (Fig. 8.9) for the hydrogenated versions of poly(l-octenylene) illustrate the clean and selective nature of ADMET. 

Although liquid Hg would never be used as a reference (model) surface in surface physics because its liquid state and high vapor pressure do not allow appropriate UHV conditions, this metal turns out to be a reference surface in electrochemistry for precisely the same reasons reproducibility of the surface state, easy cleaning of its surface, and the possibility of measuring the surface tension (surface thermodynamic conditions). In particular, the establishment of a UHV scale for potentials is at present based on data obtained for Hg. 

Mathematical models have also predicted a low volatility for methyl parathion (Jury et al. 1983 McLean et al. 1988). One study using a laboratory model designed to mimic conditions at soil pit and evaporation pond disposal sites (Sanders and Seiber 1983) did find a high volatility from the soil pit model (75% of the deposited material), but a low volatility for the evaporation pond model (3. 7% of the deposited material). A study of methyl parathion and the structurally similar compound ethyl parathion, which have similar vapor pressures, foimd that methyl parathion underwent less volatilization than ethyl parathion in a review of the data, the reduced level of volatilization for methyl parathion was determined to be due to its adsorption to the soil phase (Alvarez-Benedi et al. 1999). 

Curing of Polyimlde Resin. Thermoset processing involves a large number of simultaneous and interacting phenomena, notably transient and coupled heat and mass transfer. This makes an empirical approach to process optimization difficult. For instance, it is often difficult to ascertain the time at which pressure should be applied to consolidate the laminate. If the pressure is applied too early, the low resin viscosity will lead to excessive bleed and flash. But if the pressure is applied too late, the diluent vapor pressure will be too high or the resin molecular mobility too low to prevent void formation. This example will outline the utility of our finite element code in providing an analytical model for these cure processes. 

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