东北大学学报（自然科学版）

东北大学学报(自然科学版) ›› 2005, Vol. 26 ›› Issue (9): 824-827.DOI: -

• 论著 • 上一篇    下一篇

竖炉焙烧磁选管回收率智能预报模型

严爱军;柴天佑   

  1. 东北大学教育部暨辽宁省流程工业综合自动化重点实验室;东北大学自动化研究中心 辽宁沈阳110004
  • 收稿日期:2013-06-24 修回日期:2013-06-24 出版日期:2005-09-15 发布日期:2013-06-24
  • 通讯作者: Yan, A.-J.
  • 作者简介:-
  • 基金资助:
    国家重点基础研究发展规划项目(2002CB312201);;

Intelligent prediction model of magnetic tube recovery rate for shaft furnace roasting

Yan, Ai-Jun (1); Chai, Tian-You (2)   

  1. (1) Key Laboratory of Process Industry Automation, Northeastern University, Shenyang 110004, China; (2) Research Center of Automation, Northeastern University, Shenyang 110004, China
  • Received:2013-06-24 Revised:2013-06-24 Online:2005-09-15 Published:2013-06-24
  • Contact: Yan, A.-J.
  • About author:-
  • Supported by:
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摘要： 针对竖炉焙烧过程衡量焙烧矿质量好坏的磁选管回收率化验滞后和难以建立其机理模型的问题,基于智能技术提出了由数据采集与处理模块、决策支持模块、预报模块、在线校正模块以及效果评价模块组成的磁选管回收率智能预报模型.介绍了模型的结构及各个模块的主要功能,并将建立的智能预报模型用于竖炉焙烧过程的优化控制与决策之中,为选矿厂综合自动化系统的优化控制与优化运行奠定了良好的基础,并且其维护费用低,实时性好,可靠性及精度高,取得了明显的成效.

关键词: 竖炉焙烧, 磁选管回收率(MTRR), 软测量, 神经网络, 专家系统, 智能预报模型

Abstract: As a key evaluation index for the quality of roasted iron ore, the magnetic tube recovery rate (MTRR) was usually postponed intermittently to get until it has been analyzed chemically in off-line way after each and every roasting process completed in shaft furnace. An intelligent model has been developed to predict the MTRR punctually on the basis of intelligence technology. This model consists of five modules served as for data acquisition pretreatment, decision-making, prediction, online modification and quality evaluation. Introduces the model's framework and main functions of those modules. The model has been applied to the optimal operation and control of iron ore roasting process in shaft furnace and laid down a solid foundation for the comprehensive automatic system in ore processing plant. Its obvious benefits are low maintenance cost, good real time function, and high reliability precision.

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