摘要
采用粒子群算法和Matlab语言,以磨粉机的分流辊与快辊的水平距离及垂直距离、分流辊的转速及直径、快慢辊的斜置角度作为设计变量,以物料的喂料轨迹和物料的入磨速度作为多目标函数进行优化设计。仿真结果与原设计相比较,落点距轧点从39mm减小到1.4021mm,入磨速度也从1.795m/s提高到了2.0962m/s,有效地提高了磨粉机的工作效率。与基本型遗传算法优化结果相比,粒子群算法效果要好于基本型遗传算法。
Set feeding trajectory and infeeding speed as multiple target functions and take horizontal distance and vertical distance between feeding roller and fast roller, rotational speed and diameter of feeding roller and inclined angle between the fast roller and slow roller as design parameters, Particle Swarm algorithms and MATLAB were used in the optimization design of roller mill. Compared with the original design, distance between point of fall and roller point decreased from 39mm to 1.4022mm, infeeding speed increased from 1.795m/s to 2.0960m/s. The simulation results showed that the Particle Swarm algorithms excelled genetic algorithms and roller mill can work more efficiently.
出处
《农机化研究》
北大核心
2006年第8期115-118,共4页
Journal of Agricultural Mechanization Research
关键词
食品工业
磨粉机
优化设计
粒子群算法
喂料轨迹
入磨速度
food industry
roller mill
optimization simulation
particle swarm algorithms
feeding trajectory
infeeding speed
