Algorithm to calculate limiting cumulative particle size distribution functions from turbidimetric data

2014 ◽  
Vol 53 (2) ◽  
pp. 301 ◽  
Author(s):  
Sergei L. Shmakov
Keyword(s):  
Particle Size ◽  
Particle Size Distribution ◽  
Size Distribution ◽  
Distribution Functions

Classification Inversion Algorithm of Circular Cylinder Particle Size Distribution Based on the Light Extinction Date

2011 ◽  
Vol 105-107 ◽  
pp. 2113-2116
Author(s):  
Hong Tang ◽  
Wen Bin Zheng
Keyword(s):  
Particle Size ◽  
Circular Cylinder ◽  
Particle Size Distribution ◽  
Size Distribution ◽  
Particle System ◽  
Distribution Functions ◽  
Light Extinction ◽  
Particle Sizing ◽  
Inversion Algorithm ◽  
Cylinder Particle

Particulate flow is commonly encountered in industries as well as in many other chemical and mechanical engineering applications. The accurate measurement of particle size distribution is of the utmost importance since it decides the physical and chemical characteristic of the particles. The light extinction method can be used for in-line monitoring of particle systems thus providing real time measurements of both particle size distribution and particle concentration. In light extinction particle sizing, a classification inversion algorithm is proposed for the circular cylinder particles. The measured circular cylinder particle system is inversed with different particle distribution functions and classified according to the inversion errors in the dependent model. The simulation experiments illustrate that it is feasible to use the inversion errors of object functions to inverse the circular cylinder particle size distribution in the light extinction particle sizing technique. This classing inversion algorithm can avoid the defects that the type of the size distribution must be assumed beforehand for the light extinction method.

scholarly journals Improved Parameterization of Fertilizer Particle Size Distribution

1998 ◽  
Vol 81 (5) ◽  
pp. 935-942 ◽  
Author(s):  
Edmund Perfect ◽  
Qiang Xu ◽  
David L Terry
Keyword(s):  
Particle Size ◽  
Particle Size Distribution ◽  
Size Distribution ◽  
Goodness Of Fit ◽  
Physical Property ◽  
Distribution Functions ◽  
Parameter System ◽  
Common System ◽  
Best Parameter ◽  
Log Normal

Abstract Particle size distribution is an important physical property of granular fertilizers that influences their bulk behavior (e.g., packing and segregation). Several parameter systems for fertilizer particle size distributions are analyzed in this paper. The most common system used by the fertilizer industry is the SGN-UI system, where SGN is the size guide number (the median particle size) and Ul is the uniformity index (the 10th percentile particle size expressed as a percentage of the 95th percentile particle size). This 2-parameter system, however, has many limitations. For example, it does not give a distribution function. Furthermore, the Ul parameter does not accurately reflect the spread of particle sizes. It is therefore necessary to find a better parameterization system. Three size distribution functions (the log-normal, the Rosin-Rammler and the Gaudin-Schuhmann equations) were tested on a size distribution database composed of 377 samples from 7 fertilizer materials. Each function was fitted to the data by nonlinear regression. The Rosin-Rammler function is the best parameter system on the basis of an analysis of variance of the sum of squares of error from the nonlinear fits. Comparisons between the Rosin-Rammler and the SGN-UI parameters were also made. The Rosin- Rammler system is more accurate than the SGN-UI system, possesses the ability of prediction, and provides a measure of the goodness of fit. Therefore, the Rosin-Rammler system should be used to characterize size distribution of granular fertilizer materials instead of the SGN-UI system.

Sign in / Sign up

Export Citation Format

Share Document