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作 者:李洋波 蔡改贫[1,2] LI Yangbo;CAI Gaipin(School of Mechanical and Electrical Engineering,J iangxi University of Science and Technology,Ganzhou 341000,Jiangxi,China;Jiangxi Province Engineering Research Center for Mechanical and Electrical of Mining and Metallurgy,Ganzhou 341000,Jiangxi,China)
机构地区:[1]江西理工大学机电工程学院,江西赣州341000 [2]江西省矿冶机电工程技术研究中心,江西赣州341000
出 处:《有色金属(选矿部分)》2023年第3期131-143,共13页Nonferrous Metals(Mineral Processing Section)
基 金:国家自然科学基金资助项目(51464017);江西省重点研发计划项目(20181ACE50034);江西省教育厅科学技术项目(GJJ190452)。
摘 要:针对岩石破碎过程的复杂性和传统BPM模型对其仿真的局限性问题,提出一种通过EDEM-API二次开发所建立的改进的BPM模型(颗粒二次替换),从而优化仿真效果的一种改进方法。将对辊破碎机模型分别导入Recurdyn与EDEM中,分别使用传统与改进的BPM模型对物料破碎过程进行仿真模拟。通过试验,分析在辊隙1 mm、频率50 Hz的条件下,入料粒度分别为8~10、10~12、12~14、14~16 mm区间各1 kg矿石颗粒破碎后的粒级分布规律,利用MATLAB将仿真与试验所得碎后粒级进行统计。结果表明,在辊缝和电机频率一定的条件下,不同粒度的物料破碎后粒级质量分布差异较小,优化后模型碎后质量分布仿真与实际结果误差值E,相比优化前降低了3.391,辊子位移量分别为-0.08~0.06、-1.2~0.8 mm,优化后模型仿真效果更好。Due to the complexity of rock crushing process and the limitations of traditional BPM model,an improved BPM model(particle secondary replacement)established via secondary development of EDEM-API,new model can optimize the simulation effect.Roll crusher model is imported into Recurdyn&EDEM respectively,and traditional and improved BPM models simulate the material crushing process respectively.By experiments,particle size distribution law of 1kg ore particles after crushing is analyzed under the conditions of roll gap 1mm and frequency 50Hz,and the feed particle sizes are 8-10mm,10-12mm,12-14mm and 14-16mm respectively.The distribution after crushing obtained from simulation and experiment is counted by MATLAB.The results show that under the condition of certain roll gap and motor frequency,there is little difference in particle size distribution of materials with different particle sizes after crushing,the error e between the simulation and actual results of the broken mass distribution of the optimized model is reduced by 3.391 compared with that before optimization,and the roller displacement is-0.08~0.06mm&-1.2~0.8mm respectively,the after optimization is better.
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