Particulate systems parameter

Without an appreciation for the possible spread of sizes in real particulate systems the values of a in Fig. 11.6 are merely those of an adjustable parameter. We therefore give distribution widths for some natural and artificial aerosols and hydrosols in Table 11.1 we excluded from this list broad distributions, such as raindrops, to which the notion of a width is not really applicable. 

Yu and Standish [100] list the laws presented in Table 2.11 that have been used for particulate systems. The cumulative form of the above distributions may also be presented as cumulative two-parameter equations ... 

On time scales of oceanic circulation (1000 y and less) the internal distribution of carbonate system parameters is modified primarily by biological processes. Gross sections of the distribution of Aj and DIG in the world s oceans (Fig. 4.4) and scatter plots of the data for these quantities as a function of depth in the different ocean basins (Fig. 4.5) indicate that the concentrations increase in deep waters (1-4 Ion) from the North Atlantic to the Antarctic and into the Indian and Pacific Oceans following the conveyer belt circulation (Fig. 1.12). Degradation of organic matter (OM) and dissolution of GaGOs cause these increases in the deep waters. The chemical character of the particulate material that degrades and dissolves determines the ratio of At to DIG. 

In phase II, liquid bridges are formed between the powder particles this results in a steep increase in power consumption. Phase II is defined by the two saturation parameters 52 and 53 which are related to the amount of moisture added to the system. Parameters 5 are determined as a function of time, 5(0, in the energy consumption versus operating time curve (Figure 146). They are associated with those points in time at which the curve for a specific particulate mass to be agglomerated changes its character as shown. From the time necessary to reach these points the amount of moisture which was added can be calculated. 

Particulate size data reported for numerous particulate systems in natural waters can be modeled accurately with a two-parameter power law, given by dN/dl = AV. The exponent of the power law, p, has been shown to be a useful estimator of the relative contribution of particulate size classes to the total number, surface area, mass and volume concentration, and extinction coeflBcient of the particulate fraction. Reported values of p range from 1.8 to 4.5 in low ionic-strength solutions. 

In general, it was found that the morphology and size of the particles are responsible for the release patterns of the particulate system. Each parameter that can vary these characteristics can affect the release behavior of these structures. 

The heat and mass transfer correlations for pipe flow can be applied for particulate systems, if the ratio d / L ) and a corresponding parameter for pipe flow are known (Schliinder 1972 Krischer and Kast 1978 Kast et al. 1974). 

Figure 4.3-3 illustrates this diagram for parameter allocation, which was created on the basis of numerous diying experiments. A particle s ratio mass transfer for pipe flow can also be used for particulate systems. If the volume fractions for the continuous and the dispersed phase are known, the ratio d / L ) can be calculated and in addition the ratio diameter by length can be determined using Fig. 4.3-3. 

For particulate systems, the key characteristics are the surface area per unit volume (as shown in Fig. 1.2), the diameter of the particles (related to a commonly used method called Mesh size), the void volumes for a packed bed of particles and parameters to measure dry particle flowability. 

These assigned values may be quite unreasonable for remission from a layer that contained nothing but those particles. This is a curve fitting exercise. We are manipulating four parameters to fit a line. There are many combinations of these four parameters that give the same line. Presumably, none of the sets will be the properties of a single layer of particles, because we have not included voids in the model, and a layer of particles, one particle thick, would certainly contain voids. Nonetheless, this exercise illustrates the capability of the Dahm equation and the representative layer theory to describe particulate systems, even when there is very high absorption, a place where there... 

I or a particulate system of a relatively wide size distribution, the parameter m in Rq. (56) should be a very large number. Since O, n,ono s usually larger than 0.5, thus [Pg.41]

In his treatment of clouds of particles, Soo (1967) considered most systems having intimate contact all the way from packed beds through fluidization to dense-phase pneumatic transport. His analysis of clouds by individual particle bombardments between themselves and the confining wall relies on impact mechanics. This approach has served as a basis for both heat and electrostatic charge transfer in particulate systems. The elasticity of the particles and contact times became vital parameters in the analysis. 

Most physical properties of a particulate system are ensembles or statistical values of the properties from their individual constituents. Commonly evaluated particle geometrical properties are counts, dimension (size and distribution), shape (or conformation), and surface features (specific area, charge and distribution, porosity and distribution). Of these properties, characterization of particle size and surface features is of key interest. The behavior of a particulate system and many of its physical parameters are highly size-dependent. For example, the viscosity, flow characteristics, filterability of suspensions, reaction rate and chemical activity of a particulate system, the stability of emulsions and suspensions, abrasiveness of dry powders, color and finish of colloidal paints and paper coatings, strength of ceramics, are all dependent on particle size distribution. Out of necessity, there are many... 

Scmbbers make use of a combination of the particulate coUection mechanisms Hsted in Table 5. It is difficult to classify scmbbers predominantly by any one mechanism but for some systems, inertial impaction and direct interception predominate. Semrau (153,262,268) proposed a contacting power principle for correlation of dust-scmbber efficiency the efficiency of coUection is proportional to power expended and more energy is required to capture finer particles. This principle is appHcable only when inertial impaction and direct interception are the mechanisms employed. Eurthermore, the correlation is not general because different parameters are obtained for differing emissions coUected by different devices. However, in many wet scmbber situations for constant particle-size distribution, Semrau s power law principle, roughly appHes ... 

The first essential step in the design of a fume control system and selection of gas-cleaning equipment is the characterization of the fume emission source. Design procedures which can be used for new and existing industrial plants follow. The characterization of fume emission sources includes parameters such as plume flow rates (mVs), plume geometry (m), source heat flux (J/s), physical and chemical characteristics of particulates, fume loadings (mg/m ), etc. 

Despite the difficulties, there have been many efforts in recent years to evaluate trace metal concentrations in natural systems and to compare trace metal release and transport rates from natural and anthropogenic sources. There is no single parameter that can summarize such comparisons. Frequently, a comparison is made between the composition of atmospheric particles and that of average crustal material to indicate whether certain elements are enriched in the atmospheric particulates. If so, some explanation is sought for the enrichment. Usually, the contribution of seaspray to the enrichment is estimated, and any enrichment unaccounted for is attributed to other natural inputs (volcanoes, low-temperature volatilization processes, etc.) or anthropogenic sources. 

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