Axial dispersion model multicomponent

Reactive absorption processes present essentially a combination of transport phenomena and reactions taking place in a two-phase system with an interface. Because of their multicomponent nature, reactive absorption processes are affected by a complex thermodynamic and diffusional coupling which, in turn, is accompanied by simultaneous chemical reactions [14—16], Generally, the reaction has to be considered both in the bulk and in the film region. Modeling of hydrodynamics in gas-liquid contactors includes an appropriate description of axial dispersion, liquid hold-up and pressure drop. 

Finally, Kvaalen et al have shown that the system of equations of the ideal model for a multicomponent system (see later, Eqs. 8.1a and 8.1b) is strictly h5q3er-bolic [13]. As a consequence, the solution includes two individual band profiles which are both eluted in a finite time, beyond the column dead time, to = L/u. The finite time that is required for complete elution of the sample in the ideal model is a consequence of the assumption that there is no axial dispersion. It contrasts with the infinitely long time required for complete elution in the linear model. This difference illustrates the disparity between the hyperbolic properties of the system of equations of the ideal model of chromatography and the parabolic properties of the diffusion equation. 

Chapter 10, which provides satisfactory accuracy and is the simplest and fastest calculation procedure. This method consists of neglecting the second-order term (RHS of Eq. 11.7) and calculating numerical solutions of the ideal model, using the numerical dispersion (which is equivalent to the introduction in Eq. 11.7 of a first-order error term) to replace the neglected axial dispersion term. Since we know that any finite difference method will result in truncation errors, the most effective procedure is to control them and to use them to simplify the calculation. The results obtained are excellent, as demonstrated by the agreement between experimental band profiles recorded with single-component samples and profiles calculated [2-7]. Thus, it appears reasonable to use the same method in the calculation of solutions of multicomponent problems. However, in the multicomponent case a new source of errors appears, besides the errors discussed in detail in Chapter 10 (Section 10.3.5). 

Tingyue et al. [43] presented a general rate model for the study of gradient elution in multicomponent nonlinear chromatography. The model considers axial dispersion, film mass transfer, intraparticle diffusion, and a second-order kinetic of adsorption-desorption. This model can simulate various gradient operations including linear, nonlinear, and stepwise gradients. 

In the case of a step input, the numerical solution of the system of Eqs. 16.30 and 16.31 has been discussed in the literature for multicomponent mixtures [16]. The numerical solution of Eqs. 16.30 and 16.31 without an axial dispersion term i.e., with Di = 0) has been described by Wang and Tien [17] and by Moon and Lee [18], in the case of a step input. These authors used a finite difference method. A solution of Eq. 16.31 with D, = 0, combined with a liquid film linear driving force model, has also been described for a step input [19,20]. The numerical solution of the same kinetic model (Eqs. 16.30 and 16.31) has been discussed by Phillips et al. [21] in the case of displacement chromatography, using a finite difference method, and by Golshan-Sliirazi et al. [22,23] in the case of overloaded elution and displacement, also using finite difference methods. 

Morbidelli et al. [41] discussed a numerical procedure for the calculation of numerical solutions of the GRM model in the case of an isothermal, fixed-bed chromatographic column with a multicomponent isotherm. These authors considered two different models for the inter- and intra-particle mass transfers. These models can either take into account the internal porosity of the particles or neglect it. They include the effects of axial dispersion, the inter- and intra-particle mass transfer resistances, and a variable linear mobile phase velocity. A generalized multicomponent isotherm, initially proposed by Fritz and Schliider [34] was also used ... 

As explained in Sec. 4.4.4, the movement of components through a chromatography column can be modelled by a two-phase rate model, which is able to handle multicomponents with nonlinear equilibria. In Fig. 1 the column with segment n is shown, and in Fig. 2 the structure of the model is depicted. This involves the writing of separate liquid and solid phase component balance equations, for each segment n of the column. The movement of the solute components through the column occurs by both convective flow and axial dispersion within the liquid phase and by solute mass transfer from the liquid phase to the solid. 

The model of Santacesaria et is an extension of the linear driving force model, with fluid side resistance, for a nonlinear multicomponent Langmuir system. It includes axial dispersion, and the combined effects of pore diffusion and external fluid film resistance are accounted for throu an overall rate coefficient. Intracrystalline diffusional resistance is neglected and equilibrium between the fluid in the macfopores and in the zeolite crystals is... 

In the calculation of the predicted response curves the axial dispersion coefficient and the external mass transfer coefficient were estimated from standard correlations and the effective pore diffusivily was determined from batch uptake rate measurements with the same adsorbent particles. The model equations were solved by orthogonal collocation and the computation time required for the collocation solution ( 20 s) was shown to be substantially shorter than the time required to obtain solutions of comparable accuracy by various other standard numerical methods. It is evident that the fit of the experimental breakthrough curves is good. Since all parameters were determined independently this provides good evidence that the model is essentially correct and demonstrates the feasibility of modeling the behavior of fairly complex multicomponent dynamic systems. 

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