系统仿真学报 ›› 2018, Vol. 30 ›› Issue (2): 414-421.doi: 10.16182/j.issn1004731x.joss.201802006

• 仿真建模理论与方法 • 上一篇    下一篇

铝土矿连续球磨过程粒度分布预测模型

马天雨1, 王雅琳2, 沈坤1, 刘金平1   

  1. 1.湖南师范大学物理与信息科学学院,湖南 长沙 410006;
    2.中南大学信息科学与工程学院,湖南 长沙 410083
  • 收稿日期:2016-01-29 出版日期:2018-02-08 发布日期:2019-01-02
  • 作者简介:马天雨(1978-),男,甘肃白银,博士,讲师,研究方向为复杂工业过程建模及优化控制。
  • 基金资助:
    国家自科科学基金(61273187, 61501183),教改专项(121000),湖南省自然科学基金(2015JJ6070, 2016JJ6097)

Prediction Model of Particle Size Distribution in Bauxite Continuous Ball Milling Process

Ma Tianyu1, Wang Yalin2, Shen Kun1, Liu Jinping1   

  1. 1.College of Physics and Information Science, Hunan Normal University, Changsha 410006, China;
    2.College of Information Science and Engineering, Central South University, Changsha 410083, China
  • Received:2016-01-29 Online:2018-02-08 Published:2019-01-02

摘要: 针对球磨过程粒度分布难以在线检测的问题,基于数据驱动方法和PBM(Population Balance Model)模型框架,建立铝土矿连续球磨过程粒度分布预测模型。根据铝土矿分批磨矿特性数据改进PBM破碎速率模型结构;考虑不同粒级物料的停留时间分布特性,改进停留时间分布密度函数;由分批实验获得的铝土矿磨矿特性数据和连续球磨过程采样数据反算寻优模型关键参数。工业试验数据验证结果表明所建模型精度高,满足实际生产需要。

关键词: 球磨过程, 粒级质量平衡, 停留时间分布, 数据驱动

Abstract: As it is difficult to detect the particle size distribution of ball milling process on line, a prediction model of particle size distribution in bauxite continuous ball-milling process is proposed, which is based on data-driven method and population balance model (PBM) frame. The break-rate model structure of PBM is improved according to the characteristic data of batch grinding test of bauxite. The residual time distribution density function is improved by considering the characteristics of residence time distribution for different particle sizes. The key parameters of the model are optimized by the data of batch-test and continuous ball-milling process using back-calculation method. The industrial test data verification results show that the model accuracy meets the needs of practical production.

Key words: ball milling process, population balance model, residual time distribution, data-driven

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