Mixing process computation fluid dynamics

Mixing properties and flow fields in stirred tanks are usually studied on a laboratory scale. Practical scale-up of a stirred tank cannot be performed requiring that every individual mixing and fluid mechanical parameters in the small scale tank should be maintained in the larger one. Therefore, scale-up procedures for different types of processes have been determined through experience, testing and computational fluid dynamics simulations. 

As a result of the interdisciplinary meeting, the members decide to make a detailed analysis of the homogenization processes in the mixing section by use of 3D-CFD tools (Computational Fluid Dynamics). Afterwards the results are discussed among the plastics engineers in a second meeting to prepare a report for the chemical engineering contractor. 

One method is to solve the population balance equation (Equation 64.6) and to take into account the empirical expression for the nucleation rate (Equation 64.10), which is modified in such a way that the expression includes the impeller tip speed raised to an experimental power. In addition, the experimental value, pertinent to each ch ical, is required for the power of the crystal growth rate in the nncleation rate. Besides, the effect of snspension density on the nucleation rate needs to be known. Fnrthermore, an indnstrial suspension crystallizer does not operate in the fully mixed state, so a simplified model, such as Equation 64.6, reqnires still another experimental coefficient that modifies the CSD and depends on the mixing conditions and the eqnipment type. If the necessary experimental data are available, the method enables the prediction of CSD and the prodnction rate as dependent on the dimensions of the tank and on the operating conditions. One such method is that developed by Toyokura [23] and discussed and modified by Palosaari et al. [24]. However, this method deals with the CTystaUization tank in average and does not distinguish what happens at various locations in the tank. The more fundamental and potentially far more accurate simulation of the process can be obtained by the application of the computational fluid dynamics (CFD). It will be discussed in the following section. 

Considerable advances have been made in recent years, using computational fluid dynamics (CFD), towards a better understanding of mixing effects and their influence on precipitation processes (Leeuwen, Bruinsma and van Ros-malen, 1996 Wei and Garside, 1997 Leeuwen, 1998 Al-Rashed and Jones, 1999 Zauner and Jones, 2000b). Several commercial and private CFD packages are now available to facilitate solution of the relevant mass, momen-... 

The consequences of insufficient mixing cannot be assessed with the CSTR model. They require the space dependence of process parameters like concentration and temperature to be accounted for. This may be done by a model on the basis of Computational Fluid Dynamics (CFD). 

It has been demonstrated, at least for nanospheres, that the Damkholer number, ratio of the mixing and particle formation characteristic time, allows the influence of hydrodynamics and concentration on final size to be taken into account at constant Da, in a specified mixer, the same size is obtained. Actually the Damkholer number is difficult to use for practical design work, as it requires the evaluation of the mixing time (this can be done using computational fluid dynamics [CFD]) and of the process time, but it gives theoretical support to Equation 9.8, as the two characteristic times can be assumed proportional to a certain power of fluid velocity and concentration, respectively. 

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