[1]郝勇生,于向军,赵刚,等.基于改进粒子群算法的球磨机运行优化[J].东南大学学报(自然科学版),2008,38(3):419-423.[doi:10.3969/j.issn.1001-0505.2008.03.011] 　Hao Yongsheng,Yu Xiangjun,Zhao Gang,et al.Optimization for ball mill operation based on improved particle swarm optimization algorithm[J].Journal of Southeast University (Natural Science Edition),2008,38(3):419-423.[doi:10.3969/j.issn.1001-0505.2008.03.011] 点击复制 基于改进粒子群算法的球磨机运行优化() 分享到： var jiathis_config = { data_track_clickback: true };

38

2008年第3期

419-423

2008-05-20

文章信息/Info

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

1 东南大学能源与环境学院, 南京 210096; 2 南京南瑞继保电气有限公司, 南京 211106
Author(s):
1 School of Energy and Environmental, Southeast University, Nanjing 210096, China
2 Nanjing Nari-relays Electric Co.,Ltd, Nanjing 211106, China

Keywords:

TK39
DOI:
10.3969/j.issn.1001-0505.2008.03.011

Abstract:
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.

参考文献/References:

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