Wet ball mill that has been extensively used to comminute the raw materials in various industries possesses the advantages of large production capacity,high grinding efficiency,low investment and so on.Wet mill is a t...Wet ball mill that has been extensively used to comminute the raw materials in various industries possesses the advantages of large production capacity,high grinding efficiency,low investment and so on.Wet mill is a typical gas-liquid-solid three-phase system,and the interaction between these three phases is quite complex,resulting in the difficulty to simulate the wet milling process.As a consequence,a numerical model by coupling computational fluid dynamics(CFD),discrete element method(DEM)and volume of fluid(VOF)is developed to accurately simulate the wet milling process.A novel scheme is also devised and incorporated into CFD-DEM-VOF model to treat the interphase coupling when the particles and CFD cells have the comparable size that will be encountered in simulating the wet milling process.The accuracy of the established CFD-DEM-VOF coupling model is then validated by several test cases,including the single particle sedimentation in air-liquid domain,water entry of particle assembly and three-phase flows in a lab-scale wet mill and an industrial-scale wet mill.Furthermore,the effects of CFD cell size(2.5,5 and 10 mm)and mill rotation speed(10,30 and 50 r/min)on the prediction accuracy are also explored by the test case of lab-scale mill.The results indicate that using the quite coarse CFD cells can deteriorate the simulation accuracy,and increasing the mill rotation speed will enhance this influence.Nevertheless,adopting the very fine CFD cells(e.g.,2.5 mm in this work)in our model is not necessary in terms of the accuracy in simulating the particle behaviors,and the reliable prediction of particle behaviors can still be obtained while using the relatively large CFD cells(e.g.,5 mm in this paper).展开更多
Ore particle size is a fundamental parameter in mineral processing,directly affecting grinding efficiency,equipment performance,and downstream product quality.Traditional manual and mechanical measurement methods are ...Ore particle size is a fundamental parameter in mineral processing,directly affecting grinding efficiency,equipment performance,and downstream product quality.Traditional manual and mechanical measurement methods are time-consuming,low in accuracy,and unsuitable for continuous monitoring.Recently,deep learning has emerged as a promising solution for automated ore size detection.This review systematically introduces deep learning methods for ore particle analysis,with a focus on two major paradigms:object detection(including anchor-based and anchor-free models)and image segmentation(including semantic segmentation,instance segmentation,and boundary regression).The performance of each method is compared across varying ore stacking scenarios,such as heavy occlusion,irregular particle shapes,and dusty environments,with an emphasis on their respective strengths and limitations.In addition,the review identifies major technical,equipment-related,and data-centric challenges that impede industrial deployment.These challenges include the development of robust algorithms,ensuring reliable real-time operation under adverse conditions,and securing high-quality annotated datasets.Recent advancements are examined,including weak supervision,few-shot learning,and multimodal fusion of RGB(Red,Green,Blue),depth,and infrared data.To enable intelligent and scalable ore particle size monitoring systems,future efforts should focus on building accurate,efficient,and generalizable models supported by self-supervised pretraining and sensor integration.展开更多
基金supported by National Natural Science Foundation of China(No.22078283)State Key Laboratory of Intelligent Optimized Manufacturing in Mining&Metallurgy Process(No.BGRIMM-KZSKL-2022-3).
文摘Wet ball mill that has been extensively used to comminute the raw materials in various industries possesses the advantages of large production capacity,high grinding efficiency,low investment and so on.Wet mill is a typical gas-liquid-solid three-phase system,and the interaction between these three phases is quite complex,resulting in the difficulty to simulate the wet milling process.As a consequence,a numerical model by coupling computational fluid dynamics(CFD),discrete element method(DEM)and volume of fluid(VOF)is developed to accurately simulate the wet milling process.A novel scheme is also devised and incorporated into CFD-DEM-VOF model to treat the interphase coupling when the particles and CFD cells have the comparable size that will be encountered in simulating the wet milling process.The accuracy of the established CFD-DEM-VOF coupling model is then validated by several test cases,including the single particle sedimentation in air-liquid domain,water entry of particle assembly and three-phase flows in a lab-scale wet mill and an industrial-scale wet mill.Furthermore,the effects of CFD cell size(2.5,5 and 10 mm)and mill rotation speed(10,30 and 50 r/min)on the prediction accuracy are also explored by the test case of lab-scale mill.The results indicate that using the quite coarse CFD cells can deteriorate the simulation accuracy,and increasing the mill rotation speed will enhance this influence.Nevertheless,adopting the very fine CFD cells(e.g.,2.5 mm in this work)in our model is not necessary in terms of the accuracy in simulating the particle behaviors,and the reliable prediction of particle behaviors can still be obtained while using the relatively large CFD cells(e.g.,5 mm in this paper).
基金supported by National Key R&D Program of China under Grant 2021YFC2902702,2021YFC2902704pilot project of BGRIMM Technology Group under Grant 02-2407youth science and technology innovation fund of BGRIMM Technology Group under Grant 04-2509.
文摘Ore particle size is a fundamental parameter in mineral processing,directly affecting grinding efficiency,equipment performance,and downstream product quality.Traditional manual and mechanical measurement methods are time-consuming,low in accuracy,and unsuitable for continuous monitoring.Recently,deep learning has emerged as a promising solution for automated ore size detection.This review systematically introduces deep learning methods for ore particle analysis,with a focus on two major paradigms:object detection(including anchor-based and anchor-free models)and image segmentation(including semantic segmentation,instance segmentation,and boundary regression).The performance of each method is compared across varying ore stacking scenarios,such as heavy occlusion,irregular particle shapes,and dusty environments,with an emphasis on their respective strengths and limitations.In addition,the review identifies major technical,equipment-related,and data-centric challenges that impede industrial deployment.These challenges include the development of robust algorithms,ensuring reliable real-time operation under adverse conditions,and securing high-quality annotated datasets.Recent advancements are examined,including weak supervision,few-shot learning,and multimodal fusion of RGB(Red,Green,Blue),depth,and infrared data.To enable intelligent and scalable ore particle size monitoring systems,future efforts should focus on building accurate,efficient,and generalizable models supported by self-supervised pretraining and sensor integration.
