Pneumatic jig: effect of airflow, time and pulse rates on solid particle separation

Aziz, Mohd Aizudin Abd; Isa, Khairuddin Md; Miles, N.J.; Rashid, Radzuwan Ab

doi:10.1007/s13762-018-1648-4

Cited by 13 publications

(7 citation statements)

References 14 publications

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“…These issues motivated the study of recovery behavior of jig by means of modeling and simulation tools. As pointed out by Dong et al [4], there are several benefits to modeling jig: plant design and optimization, experimental design, testing research hypotheses, design and evaluation of control strategies, forecasting, analysis of plant-wide performance, and education [4], [28].…”

Section: Related Workmentioning

confidence: 99%

Estimation of Recovery Percentage in Gravimetric Concentration Processes using an Artificial Neural Network Model

Alarcón¹,

Rivera-M²,

Golondrino³

2022

IJACSA

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The concentrate process is the most sensitive in mineral processing plants (MPP), and the optimization of the process based on intelligent computational models (machine learning for recovery percentage modelling) can offer significant savings for the plant. Recent theoretical developments have revealed that many of the parameters commonly assumed as constants in gravity concentration modelling have a dynamic nature; however, there still lacks a universal way to model these factors accurately. This paper aims to understand the model effect of operational parameters of a jig (gravimetric concentrator) on the recovery percentage of the interest mineral (gold) through empirical modeling. The recovery percentage of mineral particles in a vibrated bed of big particles is studied by experimental data. The data used for the modelling were from experimental test in a pilot-scale jig supplemented by a twomonth field sampling campaign for collecting 151 tests varying the most significant parameters (amplitude and frequency of pulsation, water flow, height of the artificial porous bed, and particle size). It is found the recovery percentage (%R) decreases with increasing pulsation amplitude (A) and frequency (F) when the size ratio of small to large particles (d/D) is smaller than 0.148. An empirical model was developed through machine learning techniques, specifically an artificial neural network (ANN) model was built and trained to predict the jig recovery percentage as a function of operation parameters and is then used to validate the recovery as a function of vibration conditions. The performance of the ANN model was compared with a new 65 experimental data of the recovery percentage.Results showed that the model (R 2 = 0.9172 and RMSE = 0.105) was accurate and therefore could be efficiently applied to predict the recovery percentage in a jig device.

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Section: Related Workmentioning

confidence: 99%

Estimation of Recovery Percentage in Gravimetric Concentration Processes using an Artificial Neural Network Model

Alarcón¹,

Rivera-M²,

Golondrino³

2022

IJACSA

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“…However, vibration leads to intense movement of particles in the layer, which contributes to unwanted abrasion of the particle surface. Work [16] shows that the pulsating supply of air flow to the layer suspended on the gas distribution grate increases the efficiency of separation. However, the authors of [16] note the importance of selecting the optimal range of pulsation frequency.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Design of a shelf pneumatic classifier for separating a polydisperse mixture of granulated superphosphate

Yukhymenko

Ostroha

Bocko

2022

EEJET

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The object of this study is a polydisperse mixture of granular superphosphate. It is emphasized that existing technologies of granulation and processing of granules lead to the formation of dusty fractions of small particles. The content of small fractions in the finished product should be minimal and, in connection with this problem, the task is to remove small particles from the mixture. The purpose of the current experimental study is to classify a polydisperse mixture of granular superphosphate in a pneumatic classifier. The device includes an inclined perforated shelf with an unloading space between its end and the wall of the apparatus. It has been experimentally revealed that the maximum efficiency of extraction into the entrainment of small particles is achieved with a width of the discharge space equal to 0.5 of the length of the cross-sectional side of the apparatus; the degree of perforation of the shelf is 5 %; the angle of inclination of the shelf is 25–30°; the speed of the gas flow in the free section of the apparatus is 3.7 m/s. It is shown that the degree of extraction into the entrainment of a small fraction less than 1 mm in size reaches 70–75 %, the content of the small fraction in the carry-over is 96–98 %, and the large fraction is less than 5 %. By processing experimental data, an empirical equation was built that makes it possible to determine the concentration of particles in the gas stream for individual fractions of the material. It is shown that due to the implementation of an active aerodynamic weighing mode, the shelf pneumatic classifier works at specific loads for air flow rate less than the typical designs of fluidized bed separators. It is noted that the effective operation of the shelf pneumatic classifier in the production of granular mineral fertilizers is ensured at a productivity of no more than 10–12 t/h. With greater productivity, there is a need to install several devices in the technological line

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“…The Euler-Lagrange model (ELM) have a wide use in present-day engineering [3], [7], [21]- [25]. In this contribution, special attention is paid to calculate the water flow in an Euler coordinates where the particles are moved individually in Lagrangian frame.…”

Section: Euler Lagrange Model Descriptionmentioning

confidence: 99%

“…They include feed water rate, amplitude and frequency of oscillating flow, etc. [6], [7] When oscillating flow reach low amplitude levels of water velocity (ul<0.001 m/s), undesired concentration occurs [6], [7]. On the other hand, high amplitude levels of water velocity (ul>0.01 m/s) contribute to the acceleration of mineral particles and may inhibit the particle settling.…”

Section: Introductionmentioning

confidence: 99%

“…These issues motivated the study of dynamic behavior of GCPs by means of modeling and simulation tools. As pointed out by Dong et al [8], There are several benefits to modeling GCPs: plant design and optimization, experimental design, testing research hypotheses, design and evaluation of control strategies, forecasting, analysis of plant-wide performance, and education [7], [8].…”

Section: Introductionmentioning

confidence: 99%

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Effect of hydrodynamic forces on mineral particles trajectories in gravimetric concentrator type JIG

Ospina

Manco

2018

Rev. politec.

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Hydrodynamic interaction is a sensitive process for gravity concentration equipment. Because of the nonlinearity and complexity of interaction dynamics due the solid particles and water, reliable mathematical models are needed to perform plant width design (PWD)-oriented tasks. To this end, in this paper we present a study of particle motion in a water oscillating flow subjected to a sinusoidal profile on a jig device, which is a high yield and high recovery gravimetric concentrator device widely used in minerals processing. A mathematical Eulerian-Lagrangian model (ELM) is used where fluid motion is calculated by solving the Navier-Stokes and continuity equations by a widely used numerical procedure call Semi-Implicit Method for Pressure Linked Equations algorithm (SIMPLE). The motion of individual particles is obtained by a forces balance applying the Newton’s second law of motion through the action of forces imposed by the water and gravity. Liquid-solid interactions forces are calculated by the mathematical Eulerian-Lagrangian model extended to a particle suspension having a wide size and density distribution. The calculation and comparison of Basset, pressure gradient and virtual mass forces with other forces (drag and buoyancy) acting on particle trajectories in water oscillating flows were carried out under turbulent regimen flow. It was found that Basset, pressure gradient and virtual mass forces have a significant effect on the particle’s trajectories affecting their subsequent stratification. Furthermore, the conditions under which these forces can be neglected in the jig’s hydrodynamic model were studied. The study demonstrates significant differences in the particle trajectories for various size and density distribution.

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Pneumatic jig: effect of airflow, time and pulse rates on solid particle separation

Cited by 13 publications

References 14 publications

Estimation of Recovery Percentage in Gravimetric Concentration Processes using an Artificial Neural Network Model

Estimation of Recovery Percentage in Gravimetric Concentration Processes using an Artificial Neural Network Model

Design of a shelf pneumatic classifier for separating a polydisperse mixture of granulated superphosphate

Effect of hydrodynamic forces on mineral particles trajectories in gravimetric concentrator type JIG

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