scholarly journals Comparison of alternative soil particle-size distribution models and their correlation with soil physical attributes

2019 ◽  
Vol 67 (2) ◽  
pp. 179-190
Author(s):  
Fatemeh Afrasiabi ◽  
Habib Khodaverdiloo ◽  
Farrokh Asadzadeh ◽  
Martinus Th. van Genuchten
Keyword(s):  
Particle Size ◽  
Particle Size Distribution ◽  
Size Distribution ◽  
Geometric Mean ◽  
Particle Diameter ◽  
Pedotransfer Functions ◽  
Model Parameters ◽  
Distribution Models ◽  
Mean Weight Diameter ◽  
Physical Attributes

Abstract Complete descriptions of the particle-size distribution (PSD) curve should provide more information about various soil properties as opposed to only the textural composition (sand, silt and clay (SSC) fractions). We evaluated the performance of 19 models describing PSD data of soils using a range of efficiency criteria. While different criteria produced different rankings of the models, six of the 19 models consistently performed the best. Mean errors of the six models were found to depend on the particle diameter, with larger error percentages occurring in the smaller size range. Neither SSC nor the geometric mean diameter and its standard deviation correlated significantly with the saturated hydraulic conductivity (Kfs); however, the parameters of several PSD models showed significant correlation with Kfs. Porosity, mean weight diameter of the aggregates, and bulk density also showed significant correlations with PSD model parameters. Results of this study are promising for developing more accurate pedotransfer functions.

Prediction of CEC Using Fractal Parameters by Artificial Neural Networks

2014 ◽  
Vol 28 (2) ◽  
pp. 143-152 ◽  
Author(s):  
Hossein Bayat ◽  
Naser Davatgar ◽  
Mohsen Jalali
Keyword(s):  
Particle Size ◽  
Particle Size Distribution ◽  
Size Distribution ◽  
Distribution Curve ◽  
Exchange Capacity ◽  
Pedotransfer Functions ◽  
Model Parameters ◽  
Particle Size Distribution Curve ◽  
Fractal Parameters ◽  
Curve Model

Abstract The prediction of cation exchange capacity from readily available soil properties remains a challenge. In this study, firstly, we extended the entire particle size distribution curve from limited soil texture data and, at the second step, calculated the fractal parameters from the particle size distribution curve. Three pedotransfer functions were developed based on soil properties, parameters of particle size distribution curve model and fractal parameters of particle size distribution curve fractal model using the artificial neural networks technique. 1 662 soil samples were collected and separated into eight groups. Particle size distribution curve model parameters were estimated from limited soil texture data by the Skaggs method and fractal parameters were calculated by Bird model. Using particle size distribution curve model parameters and fractal parameters in the pedotransfer functions resulted in improvements of cation exchange capacity predictions. The pedotransfer functions that used fractal parameters as predictors performed better than the those which used particle size distribution curve model parameters. This can be related to the non-linear relationship between cation exchange capacity and fractal parameters. Partitioning the soil samples significantly increased the accuracy and reliability of the pedotransfer functions. Substantial improvement was achieved by utilising fractal parameters in the clusters.

scholarly journals Evolution of particle size distribution in air in the rainfall process via the moment method

2012 ◽  
Vol 16 (5) ◽  
pp. 1372-1376
Author(s):  
Fu-Jun Gan ◽  
Jian-Zhong Lin
Keyword(s):  
Particle Size ◽  
Particle Size Distribution ◽  
Size Distribution ◽  
Geometric Mean ◽  
Dynamical Behavior ◽  
Particle Diameter ◽  
Geometric Standard Deviation ◽  
Raindrop Size Distribution ◽  
Raindrop Size ◽  
The Moment

Population balance equation is converted to three moment equations to describe the dynamical behavior of particle size distribution in air in the rainfall. The scavenging coefficient is expressed as a polynomial function of the particle diameter, the raindrop diameter and the raindrop velocity. The evolutions of particle size distribution are simulated numerically and the effects of the raindrop size distribution on particle size distribution are studied. The results show that the raindrops with smaller geometric mean diameter and geometric standard deviation of size remove particles much more efficiently. The particles which fall in the ?greenfield gap? are the most difficult to be scavenged from the air.

scholarly journals Comparison of Particle Size Distribution Models for Polymer Swelling

2018 ◽  
Vol 46 (2) ◽  
pp. 79-84
Author(s):  
Ádám Wirnhardt ◽  
Tamás Varga
Keyword(s):  
Particle Size ◽  
Ion Exchange ◽  
Particle Size Distribution ◽  
Size Distribution ◽  
Model Parameters ◽  
Ion Exchange Resins ◽  
Size Distributions ◽  
Distribution Models ◽  
Polymer Swelling ◽  
Exchange Resins

Abstract In polymer technologies, various particle shapes and size distributions can be found. One of these are heterodisperse polymer beads. The capabilities of polymer swelling can be used in industries, e.g in the production of ion-exchange resins, to intensify specific technological steps such as sulphonation in the manufacturing process of ion-exchange resins. According to the literature different approaches can be used to create models for describing the behavior of disperse systems, of which the simplest models are the particle size distribution models for a given state of the solid phase. The aim of our examination was to compare and evaluate these simple models in terms of modeling polymer swelling. Hence, most of these models examine how each of the investigated models can be applied to approximately describe growth in a heterodisperse polymer system and how the identified model parameters in each time step could be interpreted. All the models were fitted to generate particle size distributions based on a swelling rate constant. The swelling of a styrene divinylbenzene-based copolymer was chosen as the basis of our examination. A model is proposed that is capable of describing the changes in the size of beads over time in this system.

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 Use of the Swebrec Function to Model Particle Size Distribution in an Industrial-Scale Ni-Co Ore Grinding Circuit

Metals ◽  
2019 ◽  
Vol 9 (8) ◽  
pp. 882
Author(s):  
Alfredo L. Coello-Velázquez ◽  
Víctor Quijano Arteaga ◽  
Juan M. Menéndez-Aguado ◽  
Francisco M. Pole ◽  
Luis Llorente
Keyword(s):  
Particle Size ◽  
Particle Size Distribution ◽  
Size Distribution ◽  
Estimation Error ◽  
Simulated Data ◽  
Operating Parameters ◽  
Distribution Models ◽  
Sampling Campaign ◽  
Swebrec Function ◽  
Statistical Criteria

Mathematical models of particle size distribution (PSD) are necessary in the modelling and simulation of comminution circuits. In order to evaluate the application of the Swebrec PSD model (SWEF) in the grinding circuit at the Punta Gorda Ni-Co plant, a sampling campaign was carried out with variations in the operating parameters. Subsequently, the fitting of the data to the Gates-Gaudin-Schumann (GGS), Rosin-Rammler (RRS) and SWEF PSD functions was evaluated under statistical criteria. The fitting of the evaluated distribution models showed that these functions are characterized as being sufficiently accurate, as the estimation error does not exceed 3.0% in any of the cases. In the particular case of the Swebrec function, reproducibility for all the products is high. Furthermore, its estimation error does not exceed 2.7% in any of the cases, with a correlation coefficient of the ratio between experimental and simulated data greater than 0.99.

Determination of Particle-Size Distribution and Concentration of Cigarette Smoke by a Light-Scattering Method

1977 ◽  
Vol 9 (3) ◽  
Author(s):  
T. Okada ◽  
Y. Ishizu ◽  
K. Matsunuma
Keyword(s):  
Particle Size ◽  
Particle Size Distribution ◽  
Size Distribution ◽  
Cigarette Smoke ◽  
Scattered Light ◽  
Geometric Mean ◽  
Number Concentration ◽  
Scattering Method ◽  
Working Standard

AbstractA new method for determining particle-size distribution of cigarette smoke particles was developed by simultaneous measurement of scattered light at three angles for a fixed wavelength. A theoretical chart useful for this purpose, which was made of the relative intensities of scattered light at the angles 45° and 135° to that at the angle 90°, was calculated on the basis of the Mie theory. The number concentration was determined from the Rayleigh ratio using the working standard method. The measurements were rapidly performed, without change of particle size during measuring time, with a device for dilution. The geometric mean diameter, the logarithmic standard deviation and the number concentration of mainstream smoke were found to be about 0.18 um, 0.4 and 3 X 10

scholarly journals Pedotransfer functions to estimate water retention parameters of soils in northeastern Brazil

2013 ◽  
Vol 37 (2) ◽  
pp. 379-391 ◽  
Author(s):  
Alexandre Hugo Cezar Barros ◽  
Quirijn de Jong van Lier ◽  
Aline de Holanda Nunes Maia ◽  
Fábio Vale Scarpare
Keyword(s):  
Particle Size ◽  
Organic Matter ◽  
Particle Size Distribution ◽  
Water Content ◽  
Size Distribution ◽  
Water Retention ◽  
Northeastern Brazil ◽  
Pedotransfer Functions ◽  
Residual Water ◽  
Specific Pressure

Pedotransfer functions (PTF) were developed to estimate the parameters (α, n, θr and θs) of the van Genuchten model (1980) to describe soil water retention curves. The data came from various sources, mainly from studies conducted by universities in Northeast Brazil, by the Brazilian Agricultural Research Corporation (Embrapa) and by a corporation for the development of the São Francisco and Parnaíba river basins (Codevasf), totaling 786 retention curves, which were divided into two data sets: 85 % for the development of PTFs, and 15 % for testing and validation, considered independent data. Aside from the development of general PTFs for all soils together, specific PTFs were developed for the soil classes Ultisols, Oxisols, Entisols, and Alfisols by multiple regression techniques, using a stepwise procedure (forward and backward) to select the best predictors. Two types of PTFs were developed: the first included all predictors (soil density, proportions of sand, silt, clay, and organic matter), and the second only the proportions of sand, silt and clay. The evaluation of adequacy of the PTFs was based on the correlation coefficient (R) and Willmott index (d). To evaluate the PTF for the moisture content at specific pressure heads, we used the root mean square error (RMSE). The PTF-predicted retention curve is relatively poor, except for the residual water content. The inclusion of organic matter as a PTF predictor improved the prediction of parameter a of van Genuchten. The performance of soil-class-specific PTFs was not better than of the general PTF. Except for the water content of saturated soil estimated by particle size distribution, the tested models for water content prediction at specific pressure heads proved satisfactory. Predictions of water content at pressure heads more negative than -0.6 m, using a PTF considering particle size distribution, are only slightly lower than those obtained by PTFs including bulk density and organic matter content.

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