scholarly journals Image Analysis Technology in the Detection of Particle Size Distribution and the Activity Effect of Low-Silicon Copper Tailings

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Yuxiang Zhao ◽  
Xinzhong Liu ◽  
Biwen Liu ◽  
Qian Zhang ◽  
Dongdong Huan ◽  
...  
Keyword(s):  
Particle Size ◽  
Particle Size Distribution ◽  
Size Distribution ◽  
Image Data ◽  
Mechanical Force ◽  
Current Status ◽  
Model Parameters ◽  
Data Set ◽  
Copper Tailings ◽  
Sem Image

To speed up the comprehensive utilization and treatment of copper tailings, the digital image processing technology is proposed in this study to detect the low-silicon copper tailings (LSCT) using a scanning electron microscope (SEM), and the particle size distribution (PSD) and the activity of LSCT are analysed under the action of mechanical force. Firstly, the current status and application of copper tailings are introduced, and the influence of the particle size of LSCT on its practical application performance is explained. Secondly, the LSCT SEM image target recognition model is designed based on the convolutional neural network (CNN), and the model parameters and the reference CNN are selected. Finally, the experimental process is designed, a SEM image data set of LSCT is prepared, the model is trained through the training set, and the image recognition test is performed on the produced data set. The experimental results show that when the number of iterations of the CNN is 10, the accuracy of model recognition can be guaranteed. After the action of mechanical force, the PSD of LSCT is mainly concentrated at 1 μm~100 μm; that around 1.4 μm~10 μm is the largest, and the PSD of LSCT around 1.4 μm increases with the increase of action time of mechanical force, but the PSD of the LSCT begins to increase when the grinding time exceeds 150 minutes, and the activity of LSCT reaches the maximum (75.545%) at 150 minutes. The average accuracy of SEM image detection of the model is 86.97%, and the model based on DenseNet shows better recognition accuracy than other models. This study provides a reference for analysing the PSD of LSCT.

scholarly journals Aerosol particle size distribution in the stratosphere retrieved from SCIAMACHY limb measurements

2018 ◽  
Vol 11 (4) ◽  
pp. 2085-2100 ◽  
Author(s):  
Elizaveta Malinina ◽  
Alexei Rozanov ◽  
Vladimir Rozanov ◽  
Patricia Liebing ◽  
Heinrich Bovensmann ◽  
...  
Keyword(s):  
Particle Size ◽  
Particle Size Distribution ◽  
Size Distribution ◽  
Aerosol Particle ◽  
Volcanic Eruptions ◽  
Stratospheric Aerosol ◽  
Solar Light ◽  
Data Set ◽  
Aerosol Particle Size ◽  
Distribution Parameters

Abstract. Information about aerosols in the Earth's atmosphere is of a great importance in the scientific community. While tropospheric aerosol influences the radiative balance of the troposphere and affects human health, stratospheric aerosol plays an important role in atmospheric chemistry and climate change. In particular, information about the amount and distribution of stratospheric aerosols is required to initialize climate models, as well as validate aerosol microphysics models and investigate geoengineering. In addition, good knowledge of stratospheric aerosol loading is needed to increase the retrieval accuracy of key trace gases (e.g. ozone or water vapour) when interpreting remote sensing measurements of the scattered solar light. The most commonly used characteristics to describe stratospheric aerosols are the aerosol extinction coefficient and Ångström coefficient. However, the use of particle size distribution parameters along with the aerosol number density is a more optimal approach. In this paper we present a new retrieval algorithm to obtain the particle size distribution of stratospheric aerosol from space-borne observations of the scattered solar light in the limb-viewing geometry. While the mode radius and width of the aerosol particle size distribution are retrieved, the aerosol particle number density profile remains unchanged. The latter is justified by a lower sensitivity of the limb-scattering measurements to changes in this parameter. To our knowledge this is the first data set providing two parameters of the particle size distribution of stratospheric aerosol from space-borne measurements of scattered solar light. Typically, the mode radius and w can be retrieved with an uncertainty of less than 20 %. The algorithm was successfully applied to the tropical region (20° N–20° S) for 10 years (2002–2012) of SCIAMACHY observations in limb-viewing geometry, establishing a unique data set. Analysis of this new climatology for the particle size distribution parameters showed clear increases in the mode radius after the tropical volcanic eruptions, whereas no distinct behaviour of the absolute distribution width could be identified. A tape recorder, which describes the time lag as the perturbation propagates to higher altitudes, was identified for both parameters after the volcanic eruptions. A quasi-biannual oscillation (QBO) pattern at upper altitudes (28–32 km) is prominent in the anomalies of the analysed parameters. A comparison of the aerosol effective radii derived from SCIAMACHY and SAGE II data was performed. The average difference is found to be around 30 % at the lower altitudes, decreasing with increasing height to almost zero around 30 km. The data sample available for the comparison is, however, relatively small.

scholarly journals Aerosol particle size distribution in the stratosphere retrieved from SCIAMACHY limb measurements

2017 ◽  
Author(s):  
Elizaveta Malinina ◽  
Alexei Rozanov ◽  
Vladimir Rozanov ◽  
Patricia Liebing ◽  
Heinrich Bovensmann ◽  
...  
Keyword(s):  
Particle Size ◽  
Particle Size Distribution ◽  
Size Distribution ◽  
Aerosol Particle ◽  
Volcanic Eruptions ◽  
Stratospheric Aerosol ◽  
Solar Light ◽  
Data Set ◽  
Distribution Width ◽  
Aerosol Particle Size

Abstract. Information about aerosols in the Earth’s atmosphere is of a great importance in the scientific community. While tropospheric aerosol influences the radiative balance of the troposphere and affects human health, stratospheric aerosol plays an important role in atmospheric chemistry and climate change. In particular, information about the amount and distribution of stratospheric aerosols is required to initialize climate models, as well as validate aerosol microphysics models and investigate geoengineering. In addition, good knowledge of stratospheric aerosol loading is needed to increase the retrieval accuracy for the key trace gases (e.g. ozone or water vapor), when interpreting remote sensing measurements of the scattered solar light. There are several parameters which are commonly used to describe stratospheric aerosols, such as the aerosol extinction coefficient and Ångström coefficient. However, the use of particle size distribution parameters coupled with the aerosol number density is an unambiguous and more optimal approach. In this manuscript we present a new retrieval algorithm to obtain the particle size distribution of the stratospheric aerosol from space borne observations of the scattered solar light in the limb viewing geometry. While the mode radius and width of the aerosol particle size distribution are retrieved, the aerosol particle number density remains unchanged. The latter is justified by a lower sensitivity of the limb-scattering measurements to changes in this parameter. To our knowledge this the first data set providing two parameters of the particle size distribution of the stratospheric aerosol from space borne measurements of the scattered solar light. Generally, the mode radius and absolute distribution width can be retrieved with the uncertainty of less than 20 %. The algorithm was successfully applied to the tropical region (20° N–20° S) for 10 years (2002–2012) of SCIAMACHY observations in limb viewing geometry, establishing a unique data set. Analysis of this new climatology for the particle size distribution parameters showed clear increases of the mode radius after the tropical volcanic eruptions, whereas no distinct behaviour oft the absolute distribution width could be identified. A tape recorder, which describes the time lag as the perturbation propagates to higher altitudes, was identified for both parameters after the volcanic eruptions. A Quasi Biannual Oscillation (QBO) pattern at upper altitudes (28–32 km) is prominent in the anomalies of the analyzed parameters. A comparison of the aerosol effective radii derived from SCIAMACHY and SAGE II data was performed. The average difference is found to be around 30 % at the lower altitudes decreasing with increasing height to almost zero around 30 km. The data sample available for the comparison is, however, relatively small.

scholarly journals Characterization of the Relationship between the Loess Moisture and Image Grayscale Value

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 7983
Author(s):  
Qingbing Liu ◽  
Jinge Wang ◽  
Hongwei Zheng ◽  
Tie Hu ◽  
Jie Zheng
Keyword(s):  
Particle Size ◽  
Particle Size Distribution ◽  
Water Content ◽  
Size Distribution ◽  
Dry Density ◽  
Model Parameters ◽  
Light Reflection ◽  
Rate Of Increase ◽  
Three Stages ◽  
The Relationship

This paper presents a model for estimating the moisture of loess from an image grayscale value. A series of well-controlled air-dry tests were performed on saturated Malan loess, and the moisture content of the loess sample during the desiccation process was automatically recorded while the soil images were continually captured using a photogrammetric device equipped with a CMOS image sensor. By converting the red, green, and blue (RGB) image into a grayscale one, the relationship between the water content and grayscale value, referred to as the water content–gray value characteristic curve (WGCC), was obtained; the impacts of dry density, particle size distribution, and illuminance on WGCC were investigated. It is shown that the grayscale value increases as the water content decreases; based on the rate of increase of grayscale value, the WGCC can be segmented into three stages: slow-rise, rapid-rise, and asymptotically stable stages. The influences that dry density and particle size distribution have on WGCC are dependent on light reflection and transmission, and this dependence is closely related to soil water types and their relative proportion. Besides, the WGCC for a given soil sample is unique if normalized with illuminance. The mechanism behind the three stages of WGCC is discussed in terms of visible light reflection. A mathematical model was proposed to describe WGCC, and the physical meaning of the model parameters was interpreted. The proposed model is validated independently using another six different types of loess samples and is shown to match well the experimental data. The results of this study can provide a reference for the development of non-contact soil moisture monitoring methods as well as relevant sensors and instruments.

scholarly journals Improvement of a 1D Population Balance Model for Twin-Screw Wet Granulation by Using Identifiability Analysis

Pharmaceutics ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 692
Author(s):  
Ana Alejandra Barrera Jiménez ◽  
Daan Van Hauwermeiren ◽  
Michiel Peeters ◽  
Thomas De Beer ◽  
Ingmar Nopens
Keyword(s):  
Particle Size ◽  
Particle Size Distribution ◽  
Size Distribution ◽  
Population Balance ◽  
Wet Granulation ◽  
Process Conditions ◽  
Model Parameters ◽  
Modeling Tools ◽  
Identifiability Analysis ◽  
Twin Screw

Recently, the pharmaceutical industry has undergone changes in the production of solid oral dosages from traditional inefficient and expensive batch production to continuous manufacturing. The latest advancements include increased use of continuous twin-screw wet granulation and application of advanced modeling tools such as Population Balance Models (PBMs). However, improved understanding of the physical process within the granulator and improvement of current population balance models are necessary for the continuous production process to be successful in practice. In this study, an existing compartmental one-dimensional PBM of a twin-screw granulation process was improved by altering the original aggregation kernel in the wetting zone as a result of an identifiability analysis. In addition, a strategy was successfully applied to reduce the number of model parameters to be calibrated in both the wetting zone and kneading zones. It was found that the new aggregation kernel in the wetting zone is capable of reproducing the particle size distribution that is experimentally observed at different process conditions as well as different types of formulations, varying in hydrophilicity and API concentration. Finally, it was observed that model parameters could be linked not only to the material properties but also to the liquid to solid ratio, paving the way to create a generic PBM to predict the particle size distribution of a new formulation.

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 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 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.

scholarly journals Intercomparison and evaluation of global aerosol microphysical properties among AeroCom models of a range of complexity

2014 ◽  
Vol 14 (9) ◽  
pp. 4679-4713 ◽  
Author(s):  
G. W. Mann ◽  
K. S. Carslaw ◽  
C. L. Reddington ◽  
K. J. Pringle ◽  
M. Schulz ◽  
...  
Keyword(s):  
Particle Size ◽  
Particle Size Distribution ◽  
Size Distribution ◽  
Climate Models ◽  
Cloud Condensation Nuclei ◽  
Global Climate Models ◽  
The Arctic ◽  
Data Set ◽  
Global Variation ◽  
Aitken Mode

Abstract. Many of the next generation of global climate models will include aerosol schemes which explicitly simulate the microphysical processes that determine the particle size distribution. These models enable aerosol optical properties and cloud condensation nuclei (CCN) concentrations to be determined by fundamental aerosol processes, which should lead to a more physically based simulation of aerosol direct and indirect radiative forcings. This study examines the global variation in particle size distribution simulated by 12 global aerosol microphysics models to quantify model diversity and to identify any common biases against observations. Evaluation against size distribution measurements from a new European network of aerosol supersites shows that the mean model agrees quite well with the observations at many sites on the annual mean, but there are some seasonal biases common to many sites. In particular, at many of these European sites, the accumulation mode number concentration is biased low during winter and Aitken mode concentrations tend to be overestimated in winter and underestimated in summer. At high northern latitudes, the models strongly underpredict Aitken and accumulation particle concentrations compared to the measurements, consistent with previous studies that have highlighted the poor performance of global aerosol models in the Arctic. In the marine boundary layer, the models capture the observed meridional variation in the size distribution, which is dominated by the Aitken mode at high latitudes, with an increasing concentration of accumulation particles with decreasing latitude. Considering vertical profiles, the models reproduce the observed peak in total particle concentrations in the upper troposphere due to new particle formation, although modelled peak concentrations tend to be biased high over Europe. Overall, the multi-model-mean data set simulates the global variation of the particle size distribution with a good degree of skill, suggesting that most of the individual global aerosol microphysics models are performing well, although the large model diversity indicates that some models are in poor agreement with the observations. Further work is required to better constrain size-resolved primary and secondary particle number sources, and an improved understanding of nucleation and growth (e.g. the role of nitrate and secondary organics) will improve the fidelity of simulated particle size distributions.

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