摘要
以重锤式破碎机多柔体动力学与离散元(MFBD-DEM)双向耦合模型为优化对象,选取入料量a、入料高度b、入料粒径c和转子转速d为优化参数,设置反击板磨损量、出料粒径小于20 mm的百分占比、排料口排料量和单位能量下排料量(能耗)为优化目标设计正交实验,对结果进行方差分析和响应面法分析,再逐一求解各优化目标下的响应方程后创建多目标优化模型,最后利用粒子群算法进行求解。结果显示:当a=589 t/h、b=2 998 mm、c=195 mm、d=500 r/min时,磨损量降低12.8%,粒径占比提高1.9%,排料量提高7.92%,单位能量下排料量提高9.4%,优化后重锤式破碎性能得到明显的改善。
Taking the multi flexible body dynamics and discrete element method(MFBD-DEM)bidirectional coupling model of the heavy hammer crusher as the optimization object,the feeding amount a,feeding height b,f eeding particle size c,and rotor speed d are selected as the optimization parameters.The wear of the impact plate,the percentage of discharge particle size less than 20 mm,the discharge amount at the discharge port,and the discharge amount per unit energy(energy consumption)are set as the optimization objectives to design orthogonal experiments.The results are analyzed by variance and response surface methodology,and the response equations under each optimization objective are solved one by one to create a multi-objective optimization model.Finally,particle swarm optimization algorithm is used to solve the problem.The results showed that when a=589 t/h,b=2998 mm,c=195 mm,and d=500 r/min,t he wear rate decreased by 12.8%,t he particle size ratio increased by 1.9%,the discharge rate increased by 7.92%,and the discharge rate per unit energy increased by 9.4%.After optimization,the performance of heavy hammer crushing was significantly improved.
作者
闫军
杨文静
张成杰
王鑫
吴桐
YAN Jun;YANG Wenjing;ZHANG Chengjie;WANG Xin;WU Tong(School of Mechanical Engineering,Inner Mongolia University of Technology,Huhehot 010051,Inner Mongolia,China;Huadian Inner Mongolia Energy Co.,Ltd.,Hohhot 010051,Inner Mongolia,China)
出处
《中国工程机械学报》
北大核心
2025年第5期828-833,共6页
Chinese Journal of Construction Machinery
基金
国家自然科学基金资助项目(51865044)
内蒙古自治区自然科学基金资助项目(2018LH05039)。
关键词
重锤式破碎机
多柔体动力学与离散元(MFBD-DEM)双向耦合
响应面分析
多目标优化
heavy hammer crusher
multi flexible body dynamics-discrete element method(MFBDDEM)bidirectional coupling
response surface analysis
multi-objective optimization
