东北大学学报（自然科学版）

东北大学学报(自然科学版) ›› 2007, Vol. 28 ›› Issue (7): 913-916.DOI: -

• 论著 •    下一篇

竖炉焙烧过程生产质量监控系统

吴峰华;岳恒;柴天佑;   

  1. 东北大学流程工业综合自动化教育部重点实验室;东北大学流程工业综合自动化教育部重点实验室;东北大学流程工业综合自动化教育部重点实验室 辽宁沈阳110004;辽宁沈阳110004;辽宁沈阳110004;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:2007-07-15 发布日期:2013-06-24
  • 通讯作者: Wu, F.-H.
  • 作者简介:-
  • 基金资助:
    国家重点基础研究规划项目(2002CB312201);;

Product quality monitoring system for roasting process of shaft furnace

Wu, Feng-Hua (1); Yue, Heng (1); Chai, Tian-You (1)   

  1. (1) Key Laboratory of Inregrated Automation of Process Industry, Northeastern University, Shenyang 110004, China; (2) Research Center of Automation, Northeastern University, Shenyang 110004, China
  • Received:2013-06-24 Revised:2013-06-24 Online:2007-07-15 Published:2013-06-24
  • Contact: Wu, F.-H.
  • About author:-
  • Supported by:
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摘要： 针对竖炉焙烧过程的质量指标磁选管回收率难以实时在线测量问题,基于RBF神经网络与专家系统提出了由磁选管回收率预报模型和生产质量诊断模型构成的竖炉焙烧质量监控系统.经过现场检验,该系统能够准确实时地预报磁选管回收率,并能够对生产质量进行诊断,提出合理的参数调整方法以避免不合格产品的出现.磁选管回收率提高2%,产品合格率提高了50%,有效地保证了竖炉焙烧过程的生产质量.

关键词: RBF神经网络, 专家规则, 磁选管回收率, 生产质量监控, 模型

Abstract: In the hematite ore roasting process of a shaft furnace, the product quality index, namely the magnetic tube recovery rate (MTRR), is difficult to be measured on-line or in real time. Therefore, a quality monitoring system is developed on the basis of RBF neural network and expert system, involving a MTRR prediction model and a product quality diagnosis model. Practical applications show that the proposed system can timely predict the MTRR with great precision and well diagnose the product quality. Furthermore, the way to adjust relevant parameters is suggested for the process to avoid the unqualified products. As a result, the MTRR is increased by 2% with the qualified product improved 50%, thus the product quality of the roasting process of shaft furnace can be efficiently guaranteed.

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