Process simulation particle size distribution

Hill, P.J. and Ng, K.M., 1997. Simulation of solids processes accounting for particle size distribution. American Institute of Chemical Engineers Journal, 43, 715. 

The counterparts of dissolving particles are the processes of precipitation and crystallization the description and simulation of which involve several additional aspects however. First of all, the interest in commercial operations often relates to the average particle size and the particle size distribution at the completion of the (batch) operation. In precipitation reactors, particle sizes strongly depend on the (variations in the) local concentrations of the reactants, this dependence being quite complicated because of the nonlinear interactions of fluctuations in velocities, reactant concentrations, and temperature. 

Figures 16.38 and 16.39 demonstrate that the form of the particle size distributions is once again almost constant during the process time, and consequently the pneumatic recycled dust is not used for seed production. Dust is deposited on the particles because the nozzle position is close to the dust recycle tube (uniform wetted dust), and this leads to an enlarged particle growth. The measured time-dependent gas outlet temperature and the measured time-dependent conversion corresponds with simulations (Fig 16.40). The bed mass growth is linear at constant liquid injection rates (Fig. 16.41). The change in particle size distribution value and of the Sauter diameter is, again, declining.

In summary, ASPEN has many features, discussed earlier in this paper, which qualify it as a third generation process simulator. A flexible executive system allows the user to have unlimited numbers of dimensions in streams, components, models and stages in equipment models. Solids may be handled in as many phases as desired. Arbitrary properties, called attributes, may be given to these phases and streams to allow handling properties such as particle size distributions. An engineer... 

In the area of nanomaterials and thin films, product quality is judged from the sharpness of interfaces, crystallinity, defects, polymorphism, shape, uniformity in particle-size distribution, film texture, etc. Engineering product quality demands linking of phenomena at very different scales and has attracted considerable interest over the last few years (Alkire and Verhoff, 1998 Christ-ofides, 2001 Raimondeau and Vlachos, 2002a). A recent review of multiscale simulation of CVD processes for various materials is given in Dollet (2004). 

The recent progress in experimental techniques and applications of DNS and LES for turbulent multiphase flows may lead to new insights necessary to develop better computational models to simulate dispersed multiphase flows with wide particle size distribution in turbulent regimes. Until then, the simulations of such complex turbulent multiphase flow processes have to be accompanied by careful validation (to assess errors due to modeling) and error estimation (due to numerical issues) exercise. Applications of these models to simulate multiphase stirred reactors, bubble column reactors and fluidized bed reactors, are discussed in Part IV of this book. 

Simulating the Detachment of Particles. The surface-cleaning factors and particle-size distributions obtained in the large and small troughs respectively after subject to a flow of water (Table VII.2) coincide. We now have to decide how far such results maybe used in order to characterize other cases of the removal of attached particles in a flow of water, for example, with varying depth of flow, plate size, etc., i.e., we wish to know whether the process underlying the detachment of adhering particles by a flow of water may be modeled or simulated. 

For the above reasons, it is essential to understand and appreciate what happens to the product and dust inside each process or during each operation. Such knowledge will assist in minimising the risk of explosion hazards and assist in designing the explosion tests to simulate on-site conditions as closely as possible, in terms of process parameters, turbulence levels, etc. In this way, the explosion data can be scaled up with confidence. For this purpose, all pertinent parameters should be recorded accurately (e.g. particle size distribution, particle density, moisture, initial pressure/temperature, etc, as described above). 

The particle size distributions for different materials have been extracted from numerical simulations of the FFA processes as shown in Fig. 18.26. In experiments, the melt jet is poured from an orifice 4 mm in diameter, and the initial jet velocity is 2.2 m/s. It is assumed that the temperature of the molten is the same as the melting... 

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