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.

scholarly journals Peculiarities of data processing for optical measurements of disperse parameters of bimodal media

2019 ◽  
Vol 43 (4) ◽  
pp. 692-698 ◽  
Author(s):  
A.A. Zhirnov ◽  
O.B. Kudrjashova
Keyword(s):  
Experimental Data ◽  
Particle Size ◽  
Distribution Function ◽  
Particulate Matter ◽  
Particle Size Distribution ◽  
Normal Distribution ◽  
Size Distribution ◽  
A Priori ◽  
Log Normal ◽  
Log Normal Distribution

This study is focused on enhancing the informativity of optical measurement techniques for particulate matter. The problem is that the description of particulate matter with bimodal and multimodal distributions by an a priori defined analytical function of particle size distribution (for example, a log-normal distribution) is not accurate enough. Here, we explore if experimental data can be approximated by a multivariable function of particle size distribution instead of using the a priori defined log-normal distribution. For the comparison of the approximation results, experiments are conducted on standard samples with granulometric compositions OGS-01LM and OGS-08LM separately and jointly in a mix. The experimental data are recorded by a high-selectivity turbidimetric technique in water suspensions of these samples. The purpose of this study is to present the measurement results as a distribution function that enables one to identify more accurately the particle-size distribution profile and the corresponding disperse characteristics of the aerosol in question when measuring parameters of disperse media by optical techniques. The main objective of this work is to develop, implement and verify a search algorithm for the particle-size distribution function by way of a multi-parameter function. We show that the solution to the problem proposed herein is more universal because it allows slow and fast processes in suspensions and aerosols to be examined with a lower error. The algorithm can be applied to the problems which are based on solving first-kind Fredholm equations.

Effects of Multiple Impacts and Size Distribution of Particles on Erosion Predictions Using CFD

2007 ◽  
Author(s):  
Yongli Zhang ◽  
Brenton S. McLaury ◽  
Siamack A. Shirzai
Keyword(s):  
Experimental Data ◽  
Particle Size ◽  
Particle Size Distribution ◽  
Size Distribution ◽  
Target Material ◽  
Average Particle ◽  
Multiple Impacts ◽  
Cfd Simulations ◽  
Erosion Damage ◽  
Wide Range

Erosion equations are usually obtained from experiments by impacting solid particles entrained in a gas or liquid on a target material. The erosion equations are utilized in CFD (Computational Fluid Dynamics) models to predict erosion damage caused by solid particle impingements. Many erosion equations are provided in terms of an erosion ratio. By definition, the erosion ratio is the mass loss of target material divided by the mass of impacting particles. The mass of impacting particles is the summation of (particle mass × number of impacts) of each particle. In erosion experiments conducted to determine erosion equations, some particles may impact the target wall many times and some other particles may not impact the target at all. Therefore, the experimental data may not reflect the actual erosion ratio because the mass of the sand that is used to run the experiments is assumed to be the mass of the impacting particles. CFD and particle trajectory simulations are applied in the present work to study effects of multiple impacts on developing erosion ratio equations. The erosion equation as well as the CFD-based erosion modeling procedure is validated against a variety of experimental data. The results show that the effect of multiple impacts is negligible in air cases. In water cases, however, this effect needs to be accounted for especially for small particles. This makes it impractical to develop erosion ratio equations from experimental data obtained for tests with sand in water or dense gases. Many factors affecting erosion damage are accounted for in various erosion equations. In addition to some well-studied parameters such as particle impacting speed and impacting angle, particle size also plays a significant role in the erosion process. An average particle size is usually used in analyzing experimental data or estimating erosion damage cases of practical interest. In petroleum production applications, however, the size of sand particles that are entrained in produced fluids can vary over a fairly broad range. CFD simulations are also performed to study the effect of particle size distribution. In CFD simulations, particle sizes are normally distributed with the mean equaling the average size of interest and the standard deviation varying over a wide range. Based on CFD simulations, an equation is developed and can be applied to account for the effect of the particle size distribution on erosion prediction for gases and liquids.

Informational Measures of the Shape of the Amoroso Distributions for the Research of Dispersed Materials

2021 ◽  
Vol 1031 ◽  
pp. 58-66
Author(s):  
Vitaly Polosin
Keyword(s):  
Particle Size ◽  
Distribution Function ◽  
Particle Size Distribution ◽  
Size Distribution ◽  
Applied Research ◽  
Particle Distribution ◽  
Size Distribution Function ◽  
Distribution Model ◽  
Information Uncertainty ◽  
Particle Size Distribution Function

For the particle size distribution function various forms of exponential models are used to construct models of the properties of dispersed substance. The most difficult stage of applied research is to determine the shape of the particle distribution model. For the particle size distribution function various forms of exponential models are used to construct models of the properties of dispersed substance. The most difficult stage of applied research is to determine the shape of the particle distribution model. The article proposes a uniform model for setting the interval of information uncertainty of non-symmetric particle size distributions. Based on the analysis of statistical and information uncertainty intervals, new shape coefficients of distribution models are constructed, these are the entropy coefficients for shifted and non shifted distributions of the Amoroso family. Graphics of dependence of entropy coefficients of non-symmetrical distributions show that distributions well-known are distinguish at small of the shapes parameters. Also it is illustrated for parameters of the form more than 2 that it is preferable to use the entropy coefficients for the unshifted distributions.The material contains also information measures for the well-known logarithmic normal distribution which is a limiting case of distribution Amorozo.

scholarly journals MINERAL ABUNDANCE AND PARTICLE SIZE DISTRIBUTION DERIVED FROM IN-SITU SPECTRA MEASUREMENTS OF YUTU ROVER OF CHANG’E-3

2017 ◽  
Vol XLII-3/W1 ◽  
pp. 85-89
Author(s):  
H. Lin ◽  
X. Zhang ◽  
Y. Yang ◽  
X. Wu ◽  
D. Guo
Keyword(s):  
Particle Size ◽  
Particle Size Distribution ◽  
Size Distribution ◽  
Landing Site ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Particle Sizes ◽  
The Mean ◽  
The Difference ◽  
Yutu Rover

From geologic perspective, understanding the types, abundance, and size distributions of minerals allows us to address what geologic processes have been active on the lunar and planetary surface. The imaging spectrometer which was carried by the Yutu Rover of Chinese Chang’E-3 mission collected the reflectance at four different sites at the height of ~ 1 m, providing a new insight to understand the lunar surface. The mineral composition and Particle Size Distribution (PSD) of these four sites were derived in this study using a Radiative Transfer Model (RTM) and Sparse Unmixing (SU) algorithm. The endmembers used were clinopyroxene, orthopyroxene, olivine, plagioclase and agglutinate collected from the lunar sample spectral dataset in RELAB. The results show that the agglutinate, clinopyroxene and olivine are the dominant minerals around the landing site. In location Node E, the abundance of agglutinate can reach up to 70 %, and the abundances of clinopyroxene and olivine are around 10 %. The mean particle sizes and the deviations of these endmembers were retrieved. PSDs of all these endmembers are close to normal distribution, and differences exist in the mean particle sizes, indicating the difference of space weathering rate of these endmembers.

scholarly journals PARTICLE SIZE DISTRIBUTION CORRECTION METHOD USING A SIMULATED ANNEALING TECHNIQUE

2011 ◽  
Vol 10 (1-2) ◽  
pp. 39
Author(s):  
A. N. Diógenes ◽  
L. O. E. dos Santos ◽  
C. P. Fernandes
Keyword(s):  
Particle Size ◽  
Distribution Function ◽  
Simulated Annealing ◽  
Particle Size Distribution ◽  
Size Distribution ◽  
Optimization Method ◽  
Correction Method ◽  
Logistic Function ◽  
Three Dimensions ◽  
Circular Particle

The procedure for obtaining the particle size distribution by visual inspection of a sample involves stereological errors, given the cut of the sample. A cut particle, supposedly spherical, with radius R, will be counted as a circular particle with radius r, r≤R. The difference between r and R depends on how far from the center of the sphere the cut was performed. This introduces errors when the extrapolation of the properties from two to three dimensions during the analysis of a sample. The usual method is to correct the distribution by probabilistic functions, which have large errors. This paper presents a method to reduce the error inherent to this problem. The method is to compute a simulation of the preparation process in a sample whose structure can be described by non-penetrating spheres of various diameters which meet a known probability distribution function, for example, a log-logistic function, or even a constant function. For each distribution radius, a number of spheres is generated and virtually cut, generating a bi-dimensional (2D) distribution. The 2D curves of the spheres distribution obtained in this simulation are compared with that obtained by the experimental procedure and then the parameters of the threedimensional distribution function are adjusted until the 2D curves are similar to the experimental one using the optimization method Simulated Annealing for the curve-fitting. In future this method will be applied to the analysis of the oil reservoir rocks.

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