scholarly journals Estimation of uncertainty of percentile values in particle size distribution analysis as a function of number of particles

2019 ◽  
Vol 30 (11) ◽  
pp. 2616-2619 ◽  
Author(s):  
Tatsushi Matsuyama
Keyword(s):  
Particle Size ◽  
Particle Size Distribution ◽  
Size Distribution ◽  
Distribution Analysis ◽  
Estimation Of Uncertainty ◽  
Number Of Particles

scholarly journals Digital microscopic image application (DMIA), an automatic method for particle size distribution analysis in waste activated sludge

2021 ◽  
Author(s):  
S. Cazares ◽  
J. A. Barrios ◽  
C. Maya ◽  
G. Velásquez ◽  
M. Pérez ◽  
...  
Keyword(s):  
Particle Size ◽  
Particle Size Distribution ◽  
Activated Sludge ◽  
Size Distribution ◽  
Particle Diameter ◽  
Diffraction Method ◽  
Laser Diffraction ◽  
Waste Activated Sludge ◽  
Equivalent Diameter ◽  
Distribution Analysis

Abstract An important physical property in environmental samples is particle size distribution. Several processes exist to measure particle diameter, including change in electrical resistance, blocking of light, the fractionation of field flow and laser diffraction (these being the most commonly used). However, their use requires expensive and complex equipment. Therefore, a Digital Microscopic Imaging Application (DMIA) method was developed adapting the algorithms used in the Helminth Egg Automatic Detector (HEAD) software coupled with a Neural Network (NN) and Bayesian algorithms. This allowed the determination of particle size distribution in samples of waste activated sludge (WAS), recirculated sludge (RCS), and pretreated sludge (PTS). The recirculation and electro-oxidation pre-treatment processes showed an effect in increasing the degree of solubilization (DS), decreasing particle size and breakage factor with ranges between 44.29%, and 31.89%. Together with a final NN calibration process, it was possible to compare results. For example, the 90th percentile of Equivalent Diameter (ED) value obtained by the DMIA with the corresponding result for the laser diffraction method. DMIA values: 228.76 μm (WAS), 111.18 μm (RCS), and 84.45 μm (PTS). DMIA processing has advantages in terms of reducing complexity, cost and time, and offers an alternative to the laser diffraction method.

scholarly journals Particle size distribution of sawdust and wood shavings mixtures

2010 ◽  
Vol 56 (No. 4) ◽  
pp. 154-158 ◽  
Author(s):  
T. Vítěz ◽  
P. Trávníček
Keyword(s):  
Experimental Data ◽  
Particle Size ◽  
Distribution Function ◽  
Network Analysis ◽  
Particle Size Distribution ◽  
Size Distribution ◽  
Mass Fraction ◽  
Particle Sizes ◽  
Wood Shavings ◽  
Number Of Particles

Particle size distribution of the sample of waste sawdust and wood shavings mixtures were made with two commonly used methods of mathematical models by Rosin-Rammler (RR model) and by Gates-Gaudin-Schuhmann (GGS model).On the basis of network analysis distribution function F (d) (mass fraction) and density function f (d) (number of particles captured between two screens) were obtained. Experimental data were evaluated using the RR model and GGS model, both models were compared. Better results were achieved with GGS model, which leads to a more accurate separation of the different particle sizes in order to obtain a better industrial profit of the material.

Study on Soil Particle Size Distribution by Digital Image Processing

2013 ◽  
Vol 788 ◽  
pp. 627-630
Author(s):  
Jian Shu Hou
Keyword(s):  
Mechanical Property ◽  
Image Processing ◽  
Particle Size ◽  
Particle Size Distribution ◽  
Size Distribution ◽  
Digital Image ◽  
Traditional Method ◽  
Processing Method ◽  
Distribution Analysis ◽  
Processing Technologies

The particle size distribution of soil is very importantto its physical and mechanical property. The ordinary method of the particlesize distribution analysis is based on shaking the soil through a set of sieves.But it will be difficult to use the method while there have particles largerthan the biggest aperture of the size sieves. Then the digital image processingwas used to solve the problem here. The processing technologies, such as imageanalysis and enhancement, deblurring, edge detection were studied to analyzethe image of soil particles. Then the image processing method was used to getthe particle size distribution accurately. Though some promotions need to becarried out in the further study, it is can be found that the image processingmethod is more efficiently than the traditional method.

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