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Seller Inventory LIE Publisher: Cambridge University Press , This specific ISBN edition is currently not available. View all copies of this ISBN edition:. Synopsis About this title This book describes the most reliable methods for evaluating the transport properties of pure gases and fluid mixtures, such as viscosity, thermal conductivity and diffusion. Book Description : This book describes the most reliable methods available for evaluating the transport properties, such as viscosity, thermal conductivity and diffusion, of pure gases and fluid mixtures. From the Back Cover : This comprehensive book describes the most soundly based methods currently available for evaluating the transport properties, particularly viscosity and thermal conductivity, of pure fluids and fluid mixtures.
Buy New Learn more about this copy. Other Popular Editions of the Same Title. Subjects Fluid dynamics.
Transport theory. Fluids -- Thermal properties. Contents Pt. General: 1. Introduction ; 2. Technological importance ; 3. Methodology Pt. Theory: 4. Transport properties of dilute gases and gaseous mixtures ; 5. Dense fluids ; 6. The critical enhancements Pt. Data Representation: 7.
Correlation techniques ; 8. Equations of state Pt. Application of Selected Methods: 9.https://alevbadocu.gq
Transport Properties of Fluids Their Correlation Prediction and Estimation
Computer calculation ; Modified hard-spheres scheme ; The corresponding-states principle: dilute gases ; The corresponding-states principle: dense fluids ; Empirical estimation B. Thus, the model makes use of only one adjustable parameter, s , which is obtained from pure component information. Note that the components figuring in Table 1 are the most commonly used solvents for supercritical fluid processing purposes and once the size parameters have been obtained safe predictions can be accomplished.
Table 1: Average absolute deviations of the fitted self-diffusion coefficient and Lennard-Jones size and energy parameters. As an example, Figure 1 depicts experimental and predicted values in a wide range of density for the self-diffusion coefficient of carbon dioxide, outside the temperature range where the size parameter was fitted. It can be seen from this figure a reasonable agreement between experiment and theory showing that the hard-sphere diameter is capable of accounting very well for changes in temperature and density.
Note also that there is some divergence in the experimental data obtained by different authors at similar conditions. Figure 1: Experimental and predicted self-diffusion coefficient for carbon dioxide at The parameter d 12 is calculated as the arithmetic mean of the pure component hard-sphere diameters, d 1 and d 2 , following the approach of Lee and Levesque , at the temperature of the mixture and density of the pure substances:. Considering the importance of some natural products in the field of food industries, soil decontamination as well as for pharmaceutical applications and trying to test the present approach for a greater number of systems, we have predicted mutual diffusivities of a variety of compounds of prominent interest.
For these substances, not appearing in Table 1 , the Lennard-Jones energy parameters were estimated from the simple relation:. In Table 2 and from Figures 2 to 6 the predicted mutual diffusion coefficients at infinite dilution are compared with the experimental data. Though these systems incorporate both great differences in size asymmetries and in intermolecular forces chemical nature , making them difficult to predict, the agreement between experiment and theory can be considered quite good, with an overall average absolute error of Notice that no binary adjustable parameters were employed in the calculations.
It should also be noted that good predictions, when compared to those obtained from the most recent methods available in the literature Eaton et al. Table 2: Average absolute deviations of the predicted values for the mutual diffusion coefficient of species 1 at infinite dilution in component 2 D These results show that very good predictions can be obtained even when the temperature of the system is out of the range where the size parameters were fitted. For instance, diffusivities of solutes in n-hexane were predicted in a wide range of temperature, from sub to the supercritical solvent region.
Thus, as found by Rocha et al. Since the WCA theory overestimates the hard-sphere diameter Kang et al. As in the case of self-diffusivities of carbon dioxide, one can observe from Figure 2 some divergence in the experimental data leading to some of the scatter in the predictions shown in Figures 3 to 6.
Predicting diffusivities in dense fluid mixtures
It is also worth noticing from Figures 1 to 6 that as the density increases the agreement between experimental and calculated values for both self- and mutual diffusivities is much improved. This fact might be explained in terms of the WCA perturbation theory of liquids which works very well at higher densities where the structure is primarily determined by repulsive forces.
Concerning the infinite dilution diffusivities, some recent developments should be commented such as those presented by Eaton et al. Both methods contain two adjustable parameters obtained from experimental mutual diffusion coefficient of solutes at infinite dilution in some supercritical solvents and they cannot be applied to estimating the concentration dependence of mutual diffusivities.
View Transport Properties Of Fluids: Their Correlation, Prediction And Estimation
Considering that the present approach is not limited to infinite dilution case and, in fact, has been successfully used to predict mutual diffusion coefficients in liquid mixtures Rocha et al. Firstly, the vapor-liquid equilibrium data for carbon dioxide-heptane from Kalra et al. To do so, Eqs. In order to compare the predicted and experimental mutual diffusion values it is important to mention that the phase equilibrium data from Kalra et al. We have not attempted to use an equation of state to fit the phase equilibrium data at K and perform extrapolations since the model is very sensitive to the mixture density values.
Consequently, we present in Figure 7 the experimental mutual diffusion of Saad and Gulari from to K and our predictions at K. Though at higher pressures large deviations are observed, the predicted results can be considered encouraging since a qualitative analysis is possible and, to the best of our knowledge, there is no other methodology available in the literature to perform such predictions.
Frontiers in Massive Data Analysis
It should be stressed again that no binary interaction parameters or empirical mixing rules were employed in the calculations. Figure 7: Experimental Saad and Gulari, and predicted mutual diffusion coefficient for the system carbon dioxide-heptane in the liquid phase along the gas-liquid boundary. Vapor-liquid equilibrium data from Kalra et al. According to Saad and Gulari experimental data, two interesting phenomena can be observed: crossover points and the appearance of minima.