Impact energy of particles in ball mills based on DEM simulations and data-driven approach

Jayasundara, Chandana T; Zhu, Hai Ping

doi:10.1016/j.powtec.2021.09.063

Cited by 22 publications

(8 citation statements)

References 28 publications

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“…The more-charged cables caused a greater allocation of collision intensity, resulting in a decline in Y sep . This phenomenon can also be supported by the discrete element method simulation on the different charging ball mill ratio 39 . Despite the minor influence, the increase in cable diameter negatively affects the separation.…”

Section: Resultsmentioning

confidence: 61%

Neural network based prediction of the efficacy of ball milling to separate cable waste materials

Han

Kumagai

et al. 2023

Commun Eng

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Material recycling technologies are essential for achieving a circular economy while reducing greenhouse gas emissions. However, most of them remain in laboratory development. Machine learning (ML) can promote industrial application while maximising yield and environmental performance. Herein, an asynchronous-parallel recurrent neural network was developed to predict the dynamic behaviour when separating copper and poly(vinyl chloride) components from the cable waste. The model was trained with six datasets (treatment conditions) at 3600 epochs. High accuracy was confirmed based on a mean-square error of 0.0015–0.0145 between the prediction and experimental results. The quantitative relationship between the input features and the separation yield was identified using sensitivity analysis. The charged weight of cables and impact energy were determined as the critical factors affecting the separation efficiency. The ML framework can be widely applied to recycling technologies to reveal the process mechanism and establish a quantitative relationship between process variables and treatment outputs.

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Section: Resultsmentioning

confidence: 61%

Neural network based prediction of the efficacy of ball milling to separate cable waste materials

Han

Kumagai

et al. 2023

Commun Eng

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“…They also revealed that the estimated milling rates of the particles under various conditions were consistent with the experimental results. Jayasundara and Zhu [10] used DEM simulations and data driven approach to simulate the impact energy of particles in ball mills under different operating conditions. The obtained mode was validated by comparing the simulated results with the experimental data.…”

Section: Introductionmentioning

confidence: 99%

Effect of Milling Parameters on DEM Modeling of a Planetary Ball Mill

Mhadhbi¹

2023

AMPC

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The effects of the milling parameters involving shape of powder particles, rotation speed, and ball-to-powder diameter (BPDR) on DEM modeling in the planetary ball mill were investigated. BPDR was varied from 1 to 10. The results revealed that the size and shape of the powder particles do not give a significant change in simulation results when BPDR attains maximum value of 10. The increasing of BPDR leads to the increase of simulation time and size. Hence, the effect of change of the powder particle shape on the calculated data size is not significant. The results also revealed that the increasing rotation speed increases impact energy between powder particles.

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“…The discrete element method (DEM) has become an excellent tool to model and analyze granular materials. Jayasundara and Zhu [1] used discrete element method to simulate the impact energy of particles in a ball mill. The model was then validated by comparing the simulation results with the experimental data.…”

Section: Introductionmentioning

confidence: 99%

Calibration of DEM Parameters to Simulate a Planetary Ball Mill

Mhadhbi¹

2022

AMPC

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Planetary ball mill is a powerful tool, which has been used for milling various materials for size reduction. The discrete element method (DEM) was used to simulate the dynamics of particle processes in a planetary ball mill. This work includes the calibration of DEM parameters to simulate a planetary ball mill using EDEM Altair 2021.2 software, which provides both faster workflows and results. The iterative input parameters changed to a close correlation between the simulation and experimental results are attained. The results showed that the standard tests could be used to generate various experimental reference values for the calibration. The numerical modeling results agree with the experimental, indicating that the calibrated parameters are accurate.

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Impact energy of particles in ball mills based on DEM simulations and data-driven approach

Cited by 22 publications

References 28 publications

Neural network based prediction of the efficacy of ball milling to separate cable waste materials

Neural network based prediction of the efficacy of ball milling to separate cable waste materials

Effect of Milling Parameters on DEM Modeling of a Planetary Ball Mill

Calibration of DEM Parameters to Simulate a Planetary Ball Mill

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