Research on optimization of working performance of shearer drum
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摘要: 在实际生产中，截割破碎过程是多作用耦合的结果，离散元法（DEM）与多体动力学（MBD）双向耦合技术可实现煤机设备与煤壁的信息交互，符合实际生产情况，具有较大的优越性。为提高采煤机滚筒的工作性能，基于DEM−MBD双向耦合机理，结合力学性能试验和模拟试验得到实际工况参数，采用仿真软件EDEM和RecurDyn建立了采煤机滚筒截割煤壁的双向耦合模型，对仿真过程中滚筒所受的转矩和截割力进行分析，证明耦合效果和截割效果较好。设计了单因素试验和正交试验，分析了滚筒运行参数对工作性能的影响规律，并利用SPSS软件得到滚筒转速、截割深度、牵引速度对截割比能耗、装煤率、载荷波动系数的影响程度，通过现场试验验证了模型的可行性。构建了以滚筒转速、截割深度、牵引速度为决策变量，以截割比能耗、装煤率和载荷波动系数为目标的多目标优化模型，利用改进多目标灰狼（MOGWO）算法和优劣解距离法（TOPSIS）对模型进行求解，得出当滚筒转速为31.12 r/min、截割深度为639.4 mm、牵引速度为5.58 m/min时，采煤机滚筒的工作性能最优，此时截割比能耗为0.467 7 kW·h/m3，装煤率为43.01%，载荷波动系数为0.327 8。Abstract: In actual production, the cutting and crushing process is the result of multi action coupling. The bidirectional coupling technology of discrete element method (DEM) and multi bodydynamics (MBD) can achieve information exchange between coal mining equipment and coal wall. It is in line with actual production situations and has significant advantages. In order to improve the working performance of the shearer drum, based on the DEM-MBD bidirectional coupling mechanism, combined with mechanical performance experiments and simulation experiments to obtain actual operating parameters, a bidirectional coupling model of the shearer drum cutting coal wall is established using simulation software EDEM and RecurDyn. The torque and cutting force experienced by the drum during the simulation process are analyzed, and it is proved that the coupling effect and cutting effect are good. Single factor experiments and orthogonal experiments are designed to analyze the influence of drum operating parameters on working performance. SPSS software is used to obtain the degree of influence of drum speed, cutting depth, and traction speed on cutting specific energy consumption, coal loading rate, and load fluctuation coefficient. The feasibility of the model is verified through on-site experiments. A multi-objective optimization model is constructed with drum speed, cutting depth, and traction speed as decision variables, and cutting specific energy consumption, coal loading rate, and load fluctuation coefficient as objectives. The improved multi-objective gray wolf optimization (MOGWO) algorithm and technique for order preference by similarity to ideal solution (TOPSIS) method are used to solve the model. It is found that when the drum speed is 31.12 r/min, the cutting depth is 639.4 mm, and the traction speed is 5.58 m/min, the working performance of the shearer drum is optimal. At this time, the cutting specific energy consumption is 0.467 7 kW·h/m3, the coal loading rate is 43.01%, and the load fluctuation coefficient is 0.327 8.
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图 2 煤岩物理力学性能试验过程
Figure 2. Experimental process of coal rock physical and mechanical properties
图 10 不同滚筒运行参数与工作性能关系
Figure 10. Relationship between different drum parameters and performance
图 13 不同滚筒转速下颗粒速度云图及曲线
Figure 13. Particle velocity clouds and curves at different drum speeds
图 19 不同算法生成的 Pareto解集分布
Figure 19. Distribution of Pareto solution sets generated by different algorithms
表 1 煤岩物理力学参数
Table 1. Coal rock physical and mechanical parameters
参数 煤 夹矸 泊松比 0.28 0.24 密度/（kg·m−3） 1400 2300 弹性模量/GPa 1.73 17.81 抗拉强度/MPa 1.35 2.24 抗压强度/MPa 11.25 28.6 坚固性系数 0.7 2.5 
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表 2 不同滚筒运行参数下的仿真结果
Table 2. Simulation results of different drum parameters
方案 n/
（r·min−1）d/
mmv/
（m·min−1）HW/
（kW·h·m−3）Q/% δ 1−1 30 500 6 0.451 39 0.361 6 1−2 40 500 6 0.502 41 0.402 9 1−3 50 500 6 0.577 40 0.424 4 1−4 60 500 6 0.629 38 0.435 0 1−5 70 500 6 0.666 35 0.449 1 2−1 40 450 6 0.504 42 0.416 8 2−2 40 500 6 0.502 41 0.402 9 2−3 40 550 6 0.503 39 0.386 8 2−4 40 600 6 0.492 36 0.364 4 2−5 40 650 6 0.489 35 0.357 5 3−1 40 500 2 0.697 33 0.367 3 3−2 40 500 3 0.627 38 0.389 8 3−3 40 500 4 0.550 40 0.395 9 3−4 40 500 5 0.535 42 0.400 3 3−5 40 500 6 0.502 41 0.402 9
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表 3 正交试验组合与结果
Table 3. Orthogonal test combinations and results
方案 因素 HW/
（kW·h·m−3）Q/% δ n/
（r·min−1）d/
mmv/
（m·min−1）1 30 450 2 0.643 31 0.342 8 2 30 550 4 0.573 36 0.342 0 3 30 650 6 0.455 33 0.321 2 4 50 450 4 0.613 41 0.421 5 5 50 550 6 0.575 35 0.409 0 6 50 650 2 0.720 26 0.351 4 7 70 450 6 0.661 38 0.460 1 8 70 550 2 0.792 27 0.392 0 9 70 650 4 0.688 31 0.381 2
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表 4 相关性水平分析
Table 4. Correlation level analysis
相关性 HW/
（kW·h·m−3）Q/% δ n/（r·min−1） A 0.685** −0.291 0.646** B 0.000 0.168 0.001 d/mm A −0.026 −0.581** −0.585** B 0.903 0.003 0.003 v/（m·min−1） A −0.719** 0.630** 0.375 B 0.000 0.001 0.071
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