Approximate analytical description of the grain-size distribution of a disperse product by the method of sieve analysis

AbstractThe article presents the grain size distribution of soil samples from the Precambrian basement within the purview of the textural properties, deduced transportation history and the numerical assessments using statistical parameters. The fourteen soil samples collected from the study area were subjected to sieve analysis in the laboratory for the determination of their grain size distribution. The statistical parameters’ study includes the graphic mean, skewness, sorting and kurtosis. The result of the analysis of the soil samples ranged from coarse to fine-grained samples, moderately and poorly sorted, positively and negatively skewed and the kurtosis also shows leptokurtic as the most dominant which suggests the samples poorly distributed and moderately sorted at the centre of the grain size distribution. These results also suggest the geological environment of the soil samples could be responsible for the poorly and moderately sorted exhibited by the samples deposited in the location.

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Rating of malt grist fineness with respect to the used grinding equipment

Research in Agricultural Engineering ◽

10.17221/41/2015-rae ◽

2016 ◽

Vol 62 (No. 3) ◽

pp. 141-146

Author(s):

A. Smejtková ◽

P. Vaculík ◽

M. Přikryl ◽

Z. Pastorek

Keyword(s):

Grain Size ◽

Size Distribution ◽

Grain Size Distribution ◽

Sieve Analysis ◽

Barley Malt ◽

Roller Mill ◽

Disk Mill ◽

Grinding Machines ◽

Made In ◽

Grinding Mill

Grain size distribution of grist is dependent on the type of grinding mill. The most widely used crushers used for malt grinding are roll grinding machines and dispersants are the disc mills. For rating of grist fineness grists made in the two-roller mill KVM 130/150 and dispersant the disk mill Skiold SK 2500 was used. The selected types of barley malt were processed: light malt, Munich malt, caramel malt and colouring malt. Rating of malt grist fineness was made with a help of sieve analysis using a “Pfungstadt sifter”. Conclusions from the measurements are as follows: by using the two-roller mill the coarsest grist is got from caramel malt and the finest malt from the light malt. The dispersant was processing grist at a speed of 1,500 rpm and 2,800 rpm. For each speed, the coarsest grist was obtained from caramel malt and the finest grist was obtained by crushing colouring malt.

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Image analysis for measuring stratigraphy in sand-gravel laboratory experiments

Earth Surface Dynamics Discussions ◽

10.5194/esurfd-1-973-2013 ◽

2013 ◽

Vol 1 (1) ◽

pp. 973-1018

Author(s):

C. Orrú ◽

V. Chavarrías ◽

W. S. J. Uijttewaal ◽

A. Blom

Keyword(s):

Grain Size ◽

Image Analysis ◽

Size Distribution ◽

Grain Size Distribution ◽

Laboratory Experiments ◽

Sieve Analysis ◽

Sediment Layer ◽

Conversion Model ◽

Areal Fraction ◽

Sediment Layers

Abstract. Measurements of spatial and temporal changes in the grain size distribution are crucial to improving the modelling of sediment transport and associated grain size-selective processes. We present three complementary techniques to determine such variations in the grain size distribution in sand-gravel laboratory experiments, as well as the resulting stratigraphy: (1) particle colouring, (2) removal of sediment layers, and (3) image analysis. The resulting stratigraphy measurement method has been evaluated in two sets of experiments. In both sets three grain size fractions within the range of coarse sand to fine gravel were painted in different colours. Sediment layers are removed using a wet vacuum cleaner. Subsequently areal images are taken of the surface of each layer. The areal fraction content, i.e. the relative presence of each size fraction over the bed surface, is determined using a colour segmentation algorithm which provides the areal fraction content of a specific colour (i.e., grain size) covering the bed surface. Particle colouring is not only beneficial to this type of image analysis but also observing and understanding grain size-selective processes. The stratigraphy based on areal fractions is measured with sufficient accuracy. Other advantages of the proposed stratigraphy measurement technique are: (a) rapid collection and processing of a large amount of data, (b) very high spatial density of information on the grain size distribution (so far unequalled in other methods), (c) the lack of disturbances to the bed surface, (d) only minor disturbances to the substrate due to the removal of sediment layers, and (e) the possibility to return a sediment layer at its original elevation and continue the flume experiment. The areal fractions can be converted into volumetric fractions using a conversion model. The proposed empirical conversion model is based on a comparison between the photogrammetry results and dry sieve analysis.

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Particle or Grain Size Distribution of Soils by Sieve Analysis

Manual of Geotechnical Laboratory Soil Testing ◽

10.1201/9781003200260-4 ◽

2021 ◽

pp. 45-72

Author(s):

Bashir Ahmed Mir

Keyword(s):

Grain Size ◽

Size Distribution ◽

Grain Size Distribution ◽

Sieve Analysis

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An equation to represent grain-size distribution

Canadian Geotechnical Journal ◽

10.1139/t00-015 ◽

2000 ◽

Vol 37 (4) ◽

pp. 817-827 ◽

Cited By ~ 108

Author(s):

Murray D Fredlund ◽

D G Fredlund ◽

G Ward Wilson

Keyword(s):

Grain Size ◽

Size Distribution ◽

Soil Water ◽

Grain Size Distribution ◽

Characteristic Curve ◽

Soil Classification ◽

Sieve Analysis ◽

Soil Water Characteristic Curve ◽

Starting Point ◽

Log Normal

The grain-size distribution is commonly used for soil classification; however, there is also potential to use the grain-size distribution as a basis for estimating soil behaviour. For example, much emphasis has recently been placed on the estimation of the soil-water characteristic curve. Many methods proposed in the literature use the grain-size distribution as a starting point to estimate the soil-water characteristic curve. Two mathematical forms are presented to represent grain-size distribution curves, namely, a unimodal form and a bimodal form. The proposed equations provide methods for accurately representing uniform, well-graded soils, and gap-graded soils. The five-parameter unimodal equation provides a closer fit than previous two-parameter, log-normal equations used to fit uniform and well-graded soils. The unimodal equation also improves representation of the silt- and clay-sized portions of the grain-size distribution curve.Key words: grain-size distribution, sieve analysis, hydrometer analysis, soil classification, probability density function.

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Permeability Estimation Model from Grain Size Sieve Analysis: Data of Onshore Central Niger Delta

European Journal of Engineering Research and Science ◽

10.24018/ejers.2018.3.12.503 ◽

2018 ◽

Vol 3 (12) ◽

pp. 119-125

Author(s):

Chikwendu E. Ubani ◽

Goodness O. Ani ◽

Toluope T. Womiloju

Keyword(s):

Grain Size ◽

Regression Analysis ◽

Size Distribution ◽

Grain Size Distribution ◽

Niger Delta ◽

Analysis Data ◽

Sieve Analysis ◽

Core Analysis ◽

Strong Positive Correlation ◽

Permeability Prediction

Permeability is an important property of the soil and studies have shown that grain size distribution is a controlling factor to this property. Establishing an empirical equation that shows the relationship between permeability and grain size has been previously investigated by several researchers, all of whom have been able to develop models for fast permeability prediction using grain size data. But because of the complexity of permeability and the Earth’s anisotropic nature, the confidence level of using this models is low as was seen when a comparison was carried out in this project using some of these models. The aim of this project is to develop a model using grain sieve analysis data for permeability prediction tailored to the Niger Delta region. Using statistica7 software, multiple regression analysis was performed on the grain size distribution data from sieve analysis using parameters P10, P90 and mean grain size distribution. Three models were developed for permeability ranges of less than 10mD to greater than 10000mD with R2 values of (0.857, 0.820, 0.939) showing a good data and regression fitting and R values of (0.926, 0.906, 0.969) showing strong positive correlation between variables. Permeability values obtained from routine core analysis was compared to the predicted permeability gotten from the model equation produced by the regression analysis. The models displayed good correlation with the routine core analysis values as seen on the validation charts plotted. A coloured schemed 3-D surface plot was generated to display the integrated effect of the grain size and density on permeability. The sensitivity analysis carried out showed that proper grain sorting is essential in permeability prediction.

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