Data correlation, particle size

CSIRO Minerals has developed a particle size analyzer (UltraPS) based on ultrasonic attenuation and velocity spectrometry for particle size determination [269]. A gamma-ray transmission gauge corrects for variations in the density of the slurry. UltraPS is applicable to the measurement of particles in the size range 0.1 to 1000 pm in highly concentrated slurries without dilution. The method involves making measurements of the transit time (and hence velocity) and amplitude (attenuation) of pulsed multiple frequency ultrasonic waves that have passed through a concentrated slurry. From the measured ultrasonic velocity and attenuation particle size can be inferred either by using mathematical inversion techniques to provide a full size distribution or by correlation of the data with particle size cut points determined by laboratory analyses to provide a calibration equation. 

Burning times for coal particles are obtained from integrated reaction rates. For larger particles (>100 fim) and at practical combustion temperatures, there is a good correlation between theory and experiment for char burnout. Experimental data are found to obey the Nusselt "square law" which states that the burning time varies with the square of the initial particle diameter (t ). However, for particle sizes smaller than 100 p.m, the Nusselt... 

It was shown in laboratory studies that methanation activity increases with increasing nickel content of the catalyst but decreases with increasing catalyst particle size. Increasing the steam-to-gas ratio of the feed gas results in increased carbon monoxide shift conversion but does not affect the rate of methanation. Trace impurities in the process gas such as H2S and HCl poison the catalyst. The poisoning mechanism differs because the sulfur remains on the catalyst while the chloride does not. Hydrocarbons at low concentrations do not affect methanation activity significantly, and they reform into methane at higher levels, hydrocarbons inhibit methanation and can result in carbon deposition. A pore diffusion kinetic system was adopted which correlates the laboratory data and defines the rate of reaction. 

In this equation, Summerfield has shown that the parameter b1 should be very sensitive to the flame temperature of the propellant. At the same time, the factor b2 should be strongly dependent on oxidizer particle size. To check these predictions, Summerfield prepared four propellants using 120 and 16 oxidizer particles at 75 and 80% loadings. Correlation of the burning-rate data with Eq. (39) yields the values for the parameters given in Table I. The experimentally observed trends are consistent with predicted effects. 

In a comprehensive study carried out at roughly the same time by Durand(J5 36 3,) the effect of pipe diameter was examined using pipes of large diameter (40-560 mm) and a range of particle sizes dp. The experimental data were correlated by ... 

Some workers have correlated experimental data in terms of k at the arithmetic mean temperature, and some at the temperature of the bulk plasma. Experimental validation of the true effective thermal conductivity is difficult because of the high temperatures, small particle sizes and variations in velocity and temperature in plasma jets. 

Fig. 23. Average particle size dp after t = 120 h stirring for various impeller types and working conditions (left hand diagram data from [60]) and correlation with the maximum energy dissipation 8 (right hand diagram) stirred bioreactor with 4 baffles V = 6L D = 0.2m H/D = 0.96 zi=l...

This completes the types of particle distributions that we might encounter. It is now time to show how particle size counting-data are used. To do this, we must select an instrument that produces counts of size of particles correlated with numbers of particles in each size reuige. There are several types of such instruments whose nature will be delineated below. But, first, we must show how this is done. Let us now examine a method of calculating aparticel size distribution. 

The study of fine particles in pharmaceutical applications involves a number of different techniques. Micromeritic investigations involve surface areas, particle sizes and their distributions, the nature of solid surfaces, and particle shapes [4]. Scientists working in this field realize that a number of techniques are necessary to fully investigate a system and that an interdisciplinary approach is essential. This ability to correlate data from different techniques allows a more thorough understanding of the system, process, or problem being investigated. 

The minimum fluidising velocity is a function of both emf and 4>s, neither of which is easily measured or estimated, and Wen and Yu have shown that these two quantities are, in practice, inter-related. These authors have published experimental data of emf and characterised particles, and it has been shown that the relation between these two quantities is essentially independent of particle size over a wide range. It has also been established that the following two expressions give reasonably good correlations between emf and [Pg.297]

The evolving structural characteristics of CLs are particularly important for further analysis of transport of protons, electrons, reactant molecules (O2), and water as well as for the distribution of electrocatalytic activity at Pt-water interfaces. In principle, the mesoscale simulations allow relating these properties to the choices of solvent, ionomer, carbon particles (sizes and wettability), catalyst loading, and hydration level. Explicit experimental data with which these results could be compared are still lacking. Versatile experimental techniques have to be employed to study particle-particle interactions, structural characteristics of phases and interfaces, and phase correlations of carbon, ionomer, and water in pores. 

Raman spectroscopy s sensitivity to the local molecular enviromnent means that it can be correlated to other material properties besides concentration, such as polymorph form, particle size, or polymer crystallinity. This is a powerful advantage, but it can complicate the development and interpretation of calibration models. For example, if a model is built to predict composition, it can appear to fail if the sample particle size distribution does not match what was used in the calibration set. Some models that appear to fail in the field may actually reflect a change in some aspect of the sample that was not sufficiently varied or represented in the calibration set. It is important to identify any differences between laboratory and plant conditions and perform a series of experiments to test the impact of those factors on the spectra and thus the field robustness of any models. This applies not only to physical parameters like flow rate, turbulence, particulates, temperature, crystal size and shape, and pressure, but also to the presence and concentration of minor constituents and expected contaminants. The significance of some of these parameters may be related to the volume of material probed, so factors that are significant in a microspectroscopy mode may not be when using a WAl probe or transmission mode. Regardless, the large calibration data sets required to address these variables can be burdensome. 

Literature has revealed limited kinetic data on secondary nucleation of alumina trihydrate in the precipitator of the Bayer Process for alumina production. A batch agitated, isothermal, three litre crystallizer was used in the study. A Coulter-Counter was utilized as the particle sizing equipment. The effects of seed density, supersaturation and temperature on secondary nucleation were investigated. Maximum nucleation rates were found to occur at about 70 C and for any crystallization temperature, the nucleation rate passed through a maximum. The correlated equation for the effective secondary nucleation rate of alumina trihydrate is... 

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