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.

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 Particle wall-loss correction methods in smog chamber experiments

2018 ◽  
Vol 11 (12) ◽  
pp. 6577-6588 ◽  
Author(s):  
Ningxin Wang ◽  
Spiro D. Jorga ◽  
Jeffery R. Pierce ◽  
Neil M. Donahue ◽  
Spyros N. Pandis
Keyword(s):  
Particle Size ◽  
Particle Size Distribution ◽  
Size Distribution ◽  
Rate Constant ◽  
Loss Rate ◽  
Correction Method ◽  
Seed Loss ◽  
Quantification Analysis ◽  
Wall Loss ◽  
Loss Rates

Abstract. The interaction of particles with the chamber walls has been a significant source of uncertainty when analyzing results of secondary organic aerosol (SOA) formation experiments performed in Teflon chambers. A number of particle wall-loss correction methods have been proposed including the use of a size-independent loss rate constant, the ratio of suspended organic mass to that of a conserved tracer (e.g., sulfate seeds), and a size-dependent loss rate constant, etc. For complex experiments such as the chemical aging of SOA, the results of the SOA quantification analysis can be quite sensitive to the adopted correction method due to the evolution of the particle size distribution and the duration of these experiments. We evaluated the performance of several particle wall-loss correction methods for aging experiments of α-pinene ozonolysis products. Determining the loss rates from seed loss periods is necessary for this system because it is not clear when chemical reactions have been completed. Results from the OA ∕ sulfate ratio and the size-independent correction methods can be influenced significantly by the size dependence of the particle wall-loss process. Coagulation can also affect the particle size distribution, especially for particles with diameter less than 100 nm, thus introducing errors in the results of the wall-loss correction. The corresponding loss rate constants may vary from experiment to experiment, and even during a specific experiment. Friction between the Teflon chamber walls and non-conductive surfaces can significantly increase particle wall-loss rates and the chamber may require weeks to recover to its original condition. Experimental procedures are proposed for the characterization of particle losses during different stages of these experiments and the evaluation of corresponding particle wall-loss correction.

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.

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