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Fugacity estimation

Temperature and sulfur fugacity estimated from iron and zinc partitioning between coexisting stannite and sphalerite and coexisting stannoidite and sphalerite... [Pg.241]

The objective of this chapter has been to develop methods of estimating species fugac-ities in mixtures. These methods are very important in phase equilibrium calculations, as will be seen in the following chapters. Because of the variety of methods discussed, there may be some confusion as to which fugacity estimation technique applies in a given situation. The comments that follow may be helpful in choosing among the three main methods discussed in this chapter ... [Pg.473]

These initial estimates are used in the iteration function. Equation (37), to obtain values of the 2 s that do not change significantly from one iteration to the next. These true mole fractions, with Equation (3-13), yield the desired fugacity... [Pg.135]

General Properties of Computerized Physical Property System. Flow-sheeting calculations tend to have voracious appetites for physical property estimations. To model a distillation column one may request estimates for chemical potential (or fugacity) and for enthalpies 10,000 or more times. Depending on the complexity of the property methods used, these calculations could represent 80% or more of the computer time requited to do a simulation. The design of the physical property estimation system must therefore be done with extreme care. [Pg.75]

The fugacity coefficient departure from nonideaHty in the vapor phase can be evaluated from equations of state or, for approximate work, from fugacity/compressibiHty estimation charts. References 11, 14, and 27 provide valuable insights into this matter. [Pg.158]

The chemical literature is rich with empirical equations of state and every year new ones are added to the already large list. Every equation of state contains a certain number of constants which depend on the nature of the gas and which must be evaluated by reduction of experimental data. Since volumetric data for pure components are much more plentiful than for mixtures, it is necessary to estimate mixture properties by relating the constants of a mixture to those for the pure components in that mixture. In most cases, these relations, commonly known as mixing rules, are arbitrary because the empirical constants lack precise physical significance. Unfortunately, the fugacity coefficients are often very sensitive to the mixing rules used. [Pg.145]

The estimation of the two parameters requires not only conversion and head space composition data but also physical properties of the monomers, e.g. reactivity ratios, vapor pressure equation, liquid phase activity coefficients and vapor phase fugacity coefficients. [Pg.299]

For the monomers in the polymerization under consideration the fugacity coefficients were estimated by Redlich-Kwong equation of state and were found to be close to unity. The activity coefficients (8) for the monomers were estimated by Scatchard-Hildebrand s method (5) for the most volatile monomer there was a temperature dependence but none for the other monomer. These were later confirmed by applying the UNIFAC method (6). The saturation vapor pressures were calculated by Antoine coefficients (5). [Pg.300]

Multimedia models can describe the distribution of a chemical between environmental compartments in a state of equilibrium. Equilibrium concentrations in different environmental compartments following the release of defined quantities of pollutant may be estimated by using distribution coefficients such as and H s (see Section 3.1). An alternative approach is to use fugacity (f) as a descriptor of chemical quantity (Mackay 1991). Fugacity has been defined as fhe fendency of a chemical to escape from one phase to another, and has the same units as pressure. When a chemical reaches equilibrium in a multimedia system, all phases should have the same fugacity. It is usually linearly related to concentration (C) as follows ... [Pg.70]

Carbon dioxide fugacity fcOi- CO2 fugacity (/coa) of ore fluids is estimated based on CO2 concentration of fluid inclusions analyzed. By using equilibrium constant of the reaction, C02(g) + H2O = H2CO3, and assuming uh20 to be unity, /CO2 can be estimated. [Pg.47]

Based on the hydrothermal alteration mineral assemblages and the fluid inclusion, the probable range of gas fugacities (/s2, /o2 /H2S) and temperature can be seen in Figs. 1.81 and 1.82 these estimated fugaeities are quite different from those of the propylitic alteration. [Pg.110]

Sulfur fugacity (/s ) As will be mentioned in section 2.4.3, fs2 can be estimated based on the Ag content of electram coexisting with argentite (or acanthite), the FeS content of sphalerite coexisting with pyrite and temperature estimated from homogenization temperatures of fluid inclusions. [Pg.129]

Carbon dioxide fugacity (fc02h The /CO2 values can be estimated from (1) gangue mineral assemblages including carbonates and (2) fluid inclusion analyses. [Pg.135]

Shimizu and Shikazono (1987) studied the compositional relations of coexisting stannoidite, sphalerite and tennantite-tetrahedrite (Fig. 1.182). Based on these data they estimated the sulfur fugacity of stannoidite-bearing tin ore. Considering the complementary work on stannite-bearing tin ores from Japanese ore deposits (Shimizu and Shikazono, 1985), a comparison between environmental conditions of these two types of tin sulfides was made. Their study is described below. [Pg.244]

Shikazono, N. (1985b) Gangue minerals from Neogene vein-type deposits in Japan and an estimate of their CO2 fugacity. Econ. Geol., 80, 754-768. [Pg.286]

Shikazono, N. and Takeuchi, K. (1984) Estimates of selenium and sulfur fugacities and formation temperatures for selenium-rich gold-silver vein-type deposits. Geochem. J. 18, 263-268. [Pg.287]

The expression for the fugacity of a component j in a gas or liquid mixture, fj, based on the Trebble-Bishnoi EoS is available in the literature (Trebble and Bishnoi, 1988). This expression is given in Appendix 1. In addition the partial derivative, (dlnf/dx j>P, for a binary mixture is also provided. This expression is very useful in the parameter estimation methods that will be presented in this chapter. [Pg.231]

Activity coefficient models offer an alternative approach to equations of state for the calculation of fugacities in liquid solutions (Prausnitz ct al. 1986 Tas-sios, 1993). These models are also mechanistic and contain adjustable parameters to enhance their correlational ability. The parameters are estimated by matching the thermodynamic model to available equilibrium data. In this chapter, vve consider the estimation of parameters in activity coefficient models for electrolyte and non-electrolyte solutions. [Pg.268]

Alternatively, one may use implicit LS estimation, e.g., minimize Equation 14.23 where liquid phase fugacities are computed by Equation 15.5 whereas vapor phase fugacities are computed by an EoS or any other available method (Prausnitz et al., 1986). [Pg.279]

The other state variables are the fugacity of dissolved methane in the bulk of the liquid water phase (fb) and the zero, first and second moment of the particle size distribution (p0, Pi, l )- The initial value for the fugacity, fb° is equal to the three phase equilibrium fugacity feq. The initial number of particles, p , or nuclei initially formed was calculated from a mass balance of the amount of gas consumed at the turbidity point. The explanation of the other variables and parameters as well as the initial conditions are described in detail in the reference. The equations are given to illustrate the nature of this parameter estimation problem with five ODEs, one kinetic parameter (K ) and only one measured state variable. [Pg.315]

In the case of vapor-liquid equilibrium, the vapor and liquid fugacities are equal for all components at the same temperature and pressure, but how can this solution be found In any phase equilibrium calculation, some of the conditions will be fixed. For example, the temperature, pressure and overall composition might be fixed. The task is to find values for the unknown conditions that satisfy the equilibrium relationships. However, this cannot be achieved directly. First, values of the unknown variables must be guessed and checked to see if the equilibrium relationships are satisfied. If not, then the estimates must be modified in the light of the discrepancy in the equilibrium, and iteration continued until the estimates of the unknown variables satisfy the requirements of equilibrium. [Pg.64]

If the K-value requires the composition of both phases to be known, then this introduces additional complications into the calculations. For example, suppose a bubble-point calculation is to be performed on a liquid of known composition using an equation of state for the vapor-liquid equilibrium. To start the calculation, a temperature is assumed. Then, calculation of K-values requires knowledge of the vapor composition to calculate the vapor-phase fugacity coefficient, and that of the liquid composition to calculate the liquid-phase fugacity coefficient. While the liquid composition is known, the vapor composition is unknown and an initial estimate is required for the calculation to proceed. Once the K-value has been estimated from an initial estimate of the vapor composition, the composition of the vapor can be reestimated, and so on. [Pg.65]

Application of Fugacity Models to the Estimation of Chemical Distribution and Persistence in the Environment... [Pg.175]

The current version of CalTOX (CalTOX4) is an eight-compartment regional and dynamic multimedia fugacity model. CalTOX comprises a multimedia transport and transformation model, multi-pathway exposure scenario models, and add-ins to quantify and evaluate variability and uncertainty. To conduct the sensitivity and uncertainty analyses, all input parameter values are given as distributions, described in terms of mean values and a coefficient of variation, instead of point estimates or plausible upper values. [Pg.60]

Mackay D, Paterson S, Joy M (1983) Application of fugacity models to the estimation of chemical distribution and persistence in the environment. In Swann Eschenroeder (eds) Fate of chemicals in the environment. American Chemical Society Symposium Series 225 175-196... [Pg.382]


See other pages where Fugacity estimation is mentioned: [Pg.12]    [Pg.242]    [Pg.12]    [Pg.242]    [Pg.182]    [Pg.143]    [Pg.154]    [Pg.171]    [Pg.258]    [Pg.270]    [Pg.107]    [Pg.107]    [Pg.108]    [Pg.111]    [Pg.112]    [Pg.139]    [Pg.167]    [Pg.261]    [Pg.331]    [Pg.89]    [Pg.97]    [Pg.98]    [Pg.161]   
See also in sourсe #XX -- [ Pg.247 ]

See also in sourсe #XX -- [ Pg.370 ]




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