基于改进粒子群算法的球磨机运行优化

为了降低制粉系统球磨机的能耗率,对球磨机进行了运行优化的研究.在运行优化过程中,为了获得运行优化的目标模型,运用支持向量回归机对制粉出力进行了软测量建模,实现了制粉出力的在线软计算,得到了制粉单耗的计算模型.在此基础上,将混沌遍历的思想引入粒子群优化算法,提出了一种新的混沌遍历粒子群算法,该改进粒子群算法具有较快的搜索速度及全局收敛的特点.将该改进粒子群算法用于球磨机运行目标的优化从而获得最佳运行参数值.研究结果表明,运用所建立的运行优化目标模型及改进的优化算法可以获得球磨机的最佳运行优化参数,该研究具有重要的工程应用价值.

Optimization for ball mill operation based on improved particle swarm optimization algorithm

To achieve a minimal unit power consumption and maximal output of ball mill in power plant,some research about optimization of the mill running parameters was done.During the optimizing process,the soft sensor model for monitoring pulverizing-capacity of the mill on line was established based on support vector regression(SVR)algorithm as well as the consumption model for pulverizing coal.Based on this work,the chaos theory was applied to improve the PSO(particles swarm optimization)algorithm in order to cope with the problems such as low-search speed and local optimization.Finally,the advanced PSO algorithm was used to optimize these models obtained in this paper to achieve the optimizing running parameters of pulverizing process.The results indicate that the optimizing parameters can be obtained through these models and advanced PSO algorithm.This study is useful for engineering application.