Measurement of particle size distribution in suspension based on artificial neural network

The inhomogeneous distribution of contact force chains (CFC) in quasi-statically sheared granular materials dominates their bulk mechanical properties. Although previous micromechanical investigations have gained significant insights into the statistical and spatial distribution of CFC, they still lack the capacity to quantitatively estimate CFC evolution in a sheared granular system. In this paper, an artificial neural network (ANN) based on discrete element method (DEM) simulation data is developed and applied to predict the anisotropy of CFC in an assembly of spherical grains undergoing a biaxial test. Five particle-scale features including particle size, coordination number, x- and y-velocity (i.e., x and y-components of the particle velocity), and spin, which all contain predictive information about the CFC, are used to establish the ANN. The results of the model prediction show that the combined features of particle size and coordination number have a dominating influence on the CFC’s estimation. An excellent model performance manifested in a close match between the rose diagrams of the CFC from the ANN predictions and DEM simulations is obtained with a mean accuracy of about 0.85. This study has shown that machine learning is a promising tool for studying the complex mechanical behaviors of granular materials.

Download Full-text

Estimating Ore Particle Size Distribution using a Deep Convolutional Neural Network

IFAC-PapersOnLine ◽

10.1016/j.ifacol.2020.12.740 ◽

2020 ◽

Vol 53 (2) ◽

pp. 12038-12043

Author(s):

Laurentz E. Olivier ◽

Michael G. Maritz ◽

Ian K. Craig

Keyword(s):

Neural Network ◽

Particle Size ◽

Particle Size Distribution ◽

Convolutional Neural Network ◽

Size Distribution ◽

Deep Convolutional Neural Network

Download Full-text

Neural network modelling to estimate particle size distribution based on other particle sections and meteorological parameters

10.5194/amt-2021-37 ◽

2021 ◽

Author(s):

Pak Lun Fung ◽

Martha Arbayani Zaidan ◽

Ola Surakhi ◽

Sasu Tarkoma ◽

Tuukka Petäjä ◽

...

Keyword(s):

Neural Network ◽

Particle Size ◽

Particle Size Distribution ◽

Size Distribution ◽

Meteorological Parameters ◽

Aerosol Size Distribution ◽

Sufficient Information ◽

Electrical Mobility ◽

Particle Sizer ◽

Mass Concentrations

Abstract. In air quality research, often only particle mass concentrations as indicators of aerosol particles are considered. However, the mass concentrations do not provide sufficient information to convey the full story of fractionated size distribution, which are able to deposit differently on respiratory system and cause various harm. Aerosol size distribution measurements rely on a variety of techniques to classify the aerosol size and measure the size distribution. From the raw data the ambient size distribution is determined utilising a suite of inversion algorithms. However, the inversion problem is quite often ill-posed and challenging to invert. Due to the instrumental insufficiency and inversion limitations, models for fractionated particle size distribution are of great significance to fill the missing gaps or negative values. The study at hand involves a merged particle size distribution, from a scanning mobility particle sizer (NanoSMPS) and an optical particle sizer (OPS) covering the aerosol size distributions from 0.01 to 0.42 μm (electrical mobility equivalent size) and 0.3 μm to 10 μm (optical equivalent size) and meteorological parameters collected at an urban background region in Amman, Jordan in the period of 1st Aug 2016–31st July 2017. We develop and evaluate feed-forward neural network (FFNN) models to estimate number concentrations at particular size bin with (1) meteorological parameters, (2) number concentration at other size bins, and (3) both of the above as input variables. Two layers with 10–15 neurons are found to be the optimal option. Lower model performance is observed at the lower edge (0.01

Download Full-text

Prediction of size distribution of iron ore granules and permeability of its bed

Journal of Mining and Metallurgy Section B Metallurgy ◽

10.2298/jmmb101213003l ◽

2011 ◽

Vol 47 (2) ◽

pp. 113-123 ◽

Cited By ~ 2

Author(s):

X. Lv ◽

C. Bai ◽

X. Huang ◽

G. Qiu

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Water Content ◽

Size Distribution ◽

Mass Fraction ◽

Raw Materials ◽

Good Prediction ◽

Moisture Capacity ◽

Granulation Process ◽

Artificial Neural

The granulation process, which is determined by many factors like properties of the mixture and the operating parameters, is of very importance for getting a good permeability of the burden in the sintering strand. The prediction of the size distribution of the granules and the permeability of its bed by the artificial neural network was studied in this paper. It was found by the experiments that the order of significance in the granulation process is water content added into the mixture, the mass fraction of the particles of 0.7-3 mm, and the moisture capacity. The water content added in the mixture and the mass fractions of the particles of 0.7-3 mm have the positive relation to the permeability of granulation, While, the moisture capacity has the negative relation to the permeability of granulation. Both the moisture capacity and the water content added were used as the inputs in the model of artificial neural network, which can give a good prediction on the permeability and mass fraction of the granules of 3-8 mm, as well as the tendency of the samples under instable raw materials conditions. These two models can be used for optimization the granulation.

Download Full-text

Retrieval of Optical Constant and Particle Size Distribution of Particulate Media Using the PSO-Based Neural Network Algorithm

New Applications of Artificial Intelligence ◽

10.5772/62446 ◽

2016 ◽

Cited By ~ 1

Author(s):

Hong Qi ◽

Ya-Tao Ren ◽

Jun-You Zhang ◽

Li-Ming Ruan ◽

He-Ping Tan

Keyword(s):

Neural Network ◽

Particle Size ◽

Particle Size Distribution ◽

Size Distribution ◽

Optical Constant ◽

Particulate Media ◽

Network Algorithm ◽

Neural Network Algorithm

Download Full-text

Prediction of the influence of processing parameters on synthesis of Al2024-B4C composite powders in a planetary mill using an artificial neural network

Science and Engineering of Composite Materials ◽

10.1515/secm-2013-0148 ◽

2014 ◽

Vol 21 (3) ◽

pp. 411-420 ◽

Cited By ~ 28

Author(s):

Temel Varol ◽

Aykut Canakci ◽

Sukru Ozsahin

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Particle Size ◽

Milling Time ◽

Planetary Mill ◽

Reinforcement Ratio ◽

Percentage Error ◽

Composite Powders ◽

Neural Network Approach ◽

Artificial Neural

AbstractIn this study, an artificial neural network approach was employed to predict the effect of B4C size, B4C content, and milling time on the particle size and particle hardness of Al2024-B4C composite powders. Al2024-B4C powder mixtures with various reinforcement weight percentages (5%, 10%, and 20% B4C), reinforcement size (49 and 5 μm), and milling times (0–10 h) were prepared by mechanical alloying process. The properties of the composite powders were analyzed using a laser particle size analyzer for the particle size and a microhardness tester for the powder microhardness. The three input parameters in the proposed artificial neural network (ANN) were the reinforcement size, reinforcement ratio, and milling time. Particle size and particle hardness of the composite powders were the outputs obtained from the proposed ANN. The mean absolute percentage error for the predicted values did not exceed 4.289% for the best prediction model. This model can be used for predicting properties of Al2024-B4C composite powders produced with different reinforcement size, reinforcement ratio, and milling times.

Download Full-text