系统仿真学报 ›› 2015, Vol. 27 ›› Issue (4): 689-696.

• 仿真建模与仿真算法及数值仿真 • 上一篇    下一篇

球磨机存煤量和钢球动能信息融合的神经网络模型

白焰, 何芳   

  1. 华北电力大学控制与计算机工程学院, 北京 102206
  • 收稿日期:2014-03-27 修回日期:2014-08-20 发布日期:2020-08-20
  • 作者简介:白焰(1954-),男,辽宁沈阳人,博士,教授,研究方向为集成分布式智能系统、现场总线控制系统和火电厂大机组智能控制;何芳(1986-),女,吉林通化人,博士生,研究方向为火力发电厂智能控制和钢球磨煤机。
  • 基金资助:
    国家自然科学基金(青年科学基金)(61304041),中央高校基本科研业务费专项资金(2014XS36)

Neural Network Model of Information Fusion for Coal Storage and Kinetic Energy of Ball Mill

Bai Yan, He Fang   

  1. School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China
  • Received:2014-03-27 Revised:2014-08-20 Published:2020-08-20

摘要: 分析球磨机制粉系统动态数学模型,进行基于离散元素法的PFC3D球磨机运行过程仿真实验,得到一定球径的定量优化的工作参数配置下不同煤质、煤量和钢球运动的关联数据。采用组合自适应学习算法,建立球磨机存煤量和钢球动能信息融合的神经网络模型,从能量角度预测磨筒存煤量。结果表明:钢球实时动能与存煤量、磨煤效率关系密切,神经网络信息融合模型具有良好的预测效果,初步证实基于钢球动能的球磨机存煤量控制方法的可行性。

关键词: 球磨机, 存煤量, PFC3D, 能量, 信息融合, 神经网络

Abstract: A dynamic mathematical model of coal pulverizing system was analyzed. Simulation experiments on mill operation process were conducted by PFC3D software platform based on discrete element method. The associated data between different coal quality, coal storage and balls' motion were obtained under certain quantitative optimized operating parameters configuration. Neural network model of information fusion for coal storage and kinetic energy of ball mill was established by using an adaptive combination learning algorithm. Coal storage in mill cylinder was predicted from the energy point of view. The results indicate that there is a close relationship between coal storage, pulverizing efficiency and balls' real-time kinetic energy. The neural network information fusion model has good predictive power to coal storage. The coal storage control method based on balls' kinetic energy is therefore feasible for optimized operation of the coal pulverizing system.

Key words: ball mill, coal storage, PFC3D, energy, information fusion, neural network

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