摘要
双激振电机双轨迹振动筛具有良好的筛分性能和钻井液处理能力,已应用于实际工程中的净化处理系统。在实际工程运用中发现,双激振电机双轨迹振动筛的激振电机安装位置的改变会引起振动筛摆角的变化,从而影响振动筛筛面上每个点的运动轨迹的振幅和倾角。因此为了两种运动轨迹接近于各自的平动状态并达到振幅与倾角的均衡性,通过构建双激振电机振动系统的动力学模型求解出振动系统的稳态解,推导稳态解中摆角各设计参数与电机安装位置的函数关系,利用粒子群算法对振动筛电机安装位置参数进行多目标设计优化,求解出同时满足振动筛两种轨迹改变的最佳电机安装位置。双轨迹振动筛经过优化设计后,运动轨迹接近于各自平动状态,并且振幅和倾角更具有均衡性,更好的发挥双电机双轨迹振动筛的优点,满足了实际工程中的不同工况需求。
The double-track vibrating screen with double exciting motors has good performance and drilling fluid treatment ability,and has been applied to the purification treatment system in practical engineering.In the actual engineering application discovery,the change of the installation position parameters of the excitation motor causes the change of the swinging angle of the vibrating screen,thereby affecting the amplitude and inclination of the motion trajectory of each point on the screen surface.Therefore,in order to achieve the equalization of the amplitude and inclination of the two trajectories,the dynamic model of the vibration system of the double-excited motor is constructed to solve the steady-state solution of the vibration system.The relationship between the motor installation position parameters and the design parameters in the swing angle is derived.The particle swarm optimization algorithm is used to optimize the multi-objective design of the vibrating screen motor installation position parameters,and the optimal motor installation position of the vibrating screen is solved.After optimization,the trajectory amplitude and inclination of the double-track vibrating screen are more consistent and stable.The vibrating screen better plays the role of screening and meets engineering applications.
作者
侯勇俊
郭馨悦
方潘
杜明俊
HOU Yong-jun;GUO Xin-yue;FANG Pan;DU Ming-jun(School of Mechanical and Electrical Engineering,Southwest Petroleum University,Sichuan Chengdu 610000,China)
出处
《机械设计与制造》
北大核心
2022年第10期257-261,共5页
Machinery Design & Manufacture
基金
国家自然科学基金项目(51705437)
四川省科技厅重点项目(2018RZ0101)。
关键词
振动筛
多目标优化
运动轨迹
粒子群算法
均衡性
Vibrating Screen
Multi-Objective Optimization
Motion Track
Chaotic Particle Swarm Optimization Algorithm
Equalization