CFD-DEM investigation of particle dispersion degree on a novel vibrating screen

Li, Yibo; Fan, Haojun; Yang, Xu; Cui, Tao; Su, Yuan; Qiao, Mengmeng; Han, Shaoyun; Qian, Jun; Zheng, Zhaohui

doi:10.1016/j.powtec.2022.117497

Cited by 23 publications

(6 citation statements)

References 23 publications

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“…In order to obtain the required dispersion coefficient, the steel U-shaped oscillation groove is divided into a limited number of unit samples, each with the same sample size. Data statistics and analysis are conducted on the volume of a certain ore particle in each unit sample to obtain the degree of separation of the ore particles [18][19].…”

Section: Dispersion Coefficient Of Ore Particlesmentioning

confidence: 99%

Study on the segregation effect of pyrite and brass ore particles under combined pendulum vibration

Zhang,

Juanatas,

Niguidula

et al. 2024

J. Phys.: Conf. Ser.

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This article uses a combined pendulum mode to study the segregation behavior of spherical particles of pyrite and brass ore with different diameters using the discrete element method. The numerical simulation program includes extensive oscillation tests on binary mixed particles of pyrite and brass ore spherical particles in different steel U-shaped oscillation grooves, different oscillation frequencies, oscillation angles, and particle sizes. The dispersion coefficient γ calculated from the Lacey mixing index M is used to characterize the segregation behavior of binary mixed particles and track the evolution of segregation. Numerical simulation shows that the oscillation frequency is 8 Hz, the oscillation amplitude is 14°, and the dispersion coefficient γ of the first group with protrusions reaches 0.8, which is about 19.4% higher than the dispersion coefficient 0.67 of the first group with light grooves. The dispersion coefficient γ of the second group with added protrusions reached 0.69, which is about 16.95% higher than the dispersion coefficient 0.59 of the second group when using a light groove. The addition of protrusions improved the particle layering effect. The oscillation amplitude is taken as 14°, and the oscillation frequencies are taken as 6Hz, 8Hz, 10Hz, and 12Hz, respectively. As the oscillation frequency increases, collisions between particles become more intense, and the dispersion coefficient γ between particles increases to a certain extent when it changes from 6Hz to 8Hz. Then, as the oscillation frequency increases, the dispersion coefficient γ decreases in severe fluctuations, and the particle stratification effect actually deteriorates. The oscillation frequency is taken as 8Hz, and the oscillation amplitude is taken as 10°, 14°, and 18°, respectively. After the oscillation amplitude increases to 14°, the dispersion coefficient γ of the particles remains basically stable, and changing the oscillation amplitude has almost no effect on the particle stratification effect. The oscillation frequency is 8Hz, and the oscillation amplitude is 14°. When adding protrusions to the steel U-shaped oscillation groove, the average relative errors of both experimental schemes are less than 1%, and the correlation coefficients are close to 1. The data points are very close to the 45° line. It indicates that the experimental data and simulation values have high consistency, and the discrete element method is feasible and reliable as a method for studying mineral particulate matter. The in-depth study of the separation and stratification behavior of ore particles under combined oscillation is of great scientific significance for the development of new combined vibration equipment.

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Section: Dispersion Coefficient Of Ore Particlesmentioning

confidence: 99%

Study on the segregation effect of pyrite and brass ore particles under combined pendulum vibration

Zhang,

Juanatas,

Niguidula

et al. 2024

J. Phys.: Conf. Ser.

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“…A vibrating screen is a commonly used piece of equipment for particle separation in bulk materials, which is widely used in mining, agriculture, and food fields [1][2][3]. The vibrating screen is welcomed by the majority of front-line workers because of its simple design, wear-resistant and durable, and the vibrating screen's performance is critical to the quality of product manufacture; many researchers used a variety of ways to carry out parametric research on the vibrating screen, and achieved a series of scientific research results.…”

Section: Introductionmentioning

confidence: 99%

Parameterization of Multi-Angle Shaker Based on PSO-BP Neural Network

et al. 2023

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It was possible to conduct a study on the shape and parameterization of the vibrating screen so as to explore the relationship between detailed vibrating screen motion parameters and particle group distribution under different screen surface states. The motion characteristics of particle groups in various scenes were investigated, screening performance of vibrating screen with complex parameters was studied, interaction between motion parameters of screen surface and motion of material groups in multi-component mixed particle groups was analyzed, segregation distribution law of multi-component mixed material groups was revealed, and this study presents simulation findings based on the discrete element program EDEM. The ensemble learning approach was used to examine the optimized model screen. It was revealed that the screen’s amplitude, vibration frequency, vibration direction angle, swing frequency, swing angle, and change rate of screen surface inclination all had a major impact on its performance. As a result, the vibrating screen’s running state was described by various parameter combinations, and the trend changes of several factors that affected the performance of the screen were examined. The investigation revealed that the particle swarm optimization backpropagation (PSO-BP) neural network model outperformed the backpropagation (BP) neural network model alone in terms of prediction. It had lower root mean square error (RMSE), mean square relative error (MSRE), mean absolute error (MAE), and mean absolute relative error (MARE) than the BP neural network model, but a larger R2. This model’s greatest absolute error was 0.0772, and its maximum relative error was 0.0241. The regression coefficient R value of 0.9859, which displayed the model’s strong performance and high prediction accuracy, showed that the PSO-BP model was feasible and helpful for parameter optimization design of vibrating screens.

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“…The sieving efficiency of the cleaning device was highest when the air velocity was 11.2 m/s. Li et al [27] studied the influence of the new sieve's trapezoidal angle on the airflow. It was found that the airflow velocity increases gradually with an increase in the trapezoidal angle.…”

Section: Introductionmentioning

confidence: 99%

Design and Motion Process of Air-Sieve Castor Cleaning Device Based on Discrete Element Method

et al. 2023

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In order to improve the cleaning effect of the castor shelling mixture, an air-sieve castor cleaning device was developed. The structure of the cleaning device was optimized by experiment and simulation based on the discrete element method. The results show that the sieving efficiency of the square distribution is 98.23% and the loss rate is 2.39%, both of which are better than the triangular distribution. The sieving efficiency in the condition of a 14 mm sieve hole diameter is 98.23%. For the conditions of 15 mm and 16 mm sieve hole diameters, the sieving efficiencies are 96.64% and 96.69%, respectively. Therefore, a sieve hole diameter of 14 mm is more beneficial for the sieving of castor capsules. The sieving efficiency is 94.0–99.5%, and the loss rate is 0.5–3.0% under 5°–9° sieve inclination. The cleaning effect is better through comprehensive analysis when the sieve surface inclination is 8°. The parameters of maximum sieving efficiency combination, minimum loss rate combination, and optimal cleaning effect were obtained by optimizing the operating parameters of the cleaning device. The operating parameters were adjusted, and then the three combinations of parameters were tested. It is found that the impurity content of castor seeds is within 1%, which meets the requirements of the device design. This study can provide a reference for developing high-efficiency castor cleaning devices.

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CFD-DEM investigation of particle dispersion degree on a novel vibrating screen

Cited by 23 publications

References 23 publications

Study on the segregation effect of pyrite and brass ore particles under combined pendulum vibration

Study on the segregation effect of pyrite and brass ore particles under combined pendulum vibration

Parameterization of Multi-Angle Shaker Based on PSO-BP Neural Network

Design and Motion Process of Air-Sieve Castor Cleaning Device Based on Discrete Element Method

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