A review of intelligent ore sorting technology and equipment development

Under the background of increasingly scarce ore worldwide and increasingly fierce market competition,developing the mining industry could be strongly restricted.Intelligent ore sorting equipment not only improves ore use and enhances the economic benefits of enterprises but also increases the ore grade and lessens the grinding cost and tailings production.However,long-term research on intelligent ore sorting equipment found that the factors affecting sorting efficiency mainly include ore information identification technology,equipment sorting actuator,and information processing algorithm.The high precision,strong anti-interference capability,and high speed of these factors guarantee the separation efficiency of intelligent ore sorting equipment.Color ore sorter,X-ray ore transmission sorter,dual-energy X-ray transmission ore sorter,X-ray fluorescence ore sorter,and near-infrared ore sorter have been successfully developed in accordance with the different characteristics of minerals while ensuring the accuracy of equipment sorting and improving the equipment sorting efficiency.With the continuous improvement of mine automation level,the application of online element rapid analysis technology with high speed,high precision,and strong anti-interference capability in intelligent ore sorting equipment will become an inevitable trend of equipment development in the future.Laser-induced breakdown spectroscopy,transientγneutron activation analysis,online Fourier transform infrared spectroscopy,and nuclear magnetic resonance techniques will promote the development of ore sorting equipment.In addition,the improvement and joint application of additional high-speed and high-precision operation algorithms(such as peak area,principal component analysis,artificial neural network,partial least squares,and Monte Carlo library least squares methods)are an essential part of the development of intelligent ore sorting equipment in the future.

3D large-scale SPH modeling of vehicle wading with GPU acceleration

Vehicle wading is a complex fluid-structure interaction(FSI) problem and has attracted great attention recently from the automotive industry, especially for electric vehicles. As a meshless Lagrangian particle method, smoothed particle hydrodynamics(SPH) is one of the most suitable candidates for simulations of vehicle wading due to its inherent advantages in modeling free surface flows, splash, and moving interfaces. Nevertheless, the inevitable neighbor query for the nearest adjacent particles among the support domain leads to considerable computational cost and thus limits its application in 3D large-scale simulations. In this work, a GPU-based SPH method is developed with an adaptive spatial sort technology for simulations of vehicle wading. In addition, a fast, easy-to-implement particle generator is presented for isotropic initialization of the complex vehicle geometry with optimal interpolation properties. A comparative study of vehicle wading on a puddle between the GPUbased SPH with two pieces of commercial software is used to verify the capability of the GPU-based SPH method in terms of convergence analysis, kinematic characteristics, and computing performance. Finally, different conditions of vehicle speeds, water depths, and puddle widths are tested to investigate the vehicle wading numerically. The results demonstrate that the adaptive spatial sort technology can significantly improve the computing performance of the GPU-based SPH method and meanwhile promotes the GPU-based SPH method to be a competitive tool for the study of 3D large-scale FSI problems including vehicle wading. Some helpful findings of the critical vehicle speed, water depth as well as boundary wall effect are also reported in this work.