东北大学学报（自然科学版）

东北大学学报:自然科学版 ›› 2014, Vol. 35 ›› Issue (3): 314-317.DOI: 10.12068/j.issn.1005-3026.2014.03.003

• 信息与控制 • 上一篇    下一篇

基于模型迁移方法的精炼炉钢水终点硫含量预报

吕伍1,2，毛志忠1,2，袁平1,2，贾明兴1,2   

  1. (1.东北大学 信息科学与工程学院， 辽宁 沈阳110819； 2.东北大学 流程工业综合自动化国家重点实验室， 辽宁 沈阳110819)
  • 收稿日期:2013-03-15 修回日期:2013-03-15 出版日期:2014-03-15 发布日期:2013-11-22
  • 通讯作者: 吕伍
  • 作者简介:吕伍(1985-)，男，辽宁辽阳人，东北大学博士后研究人员;毛志忠(1962-)，男，山东莱州人，东北大学教授，博士生导师;贾明兴(1972-)，男，辽宁凌源人，东北大学教授.
  • 基金资助:
    中央高校基本科研业务费专项资金资助项目(N110604011).

Ladle Furnace End Point Sulphur Content Prediction Model Based on Model Migration Method〓

LYU Wu1,2， MAO Zhizhong1,2， YUAN Ping1,2， JIA Mingxing1,2   

  1. 1. School of Information Science & Engineering， Northeastern University， Shenyang 110819， China; 2. Key Laboratory of Integrated Automation for Process Industries， Ministry of Education， Northeastern University， Shenyang 110819， China.
  • Received:2013-03-15 Revised:2013-03-15 Online:2014-03-15 Published:2013-11-22
  • Contact: LYU Wu
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摘要： 针对精炼炉(LF)钢水脱硫过程的非线性、强动态和多工况问题，提出了基于局部模型迁移方法的钢水硫含量终点预报模型.首先建立简化的机理模型捕捉主要的过程特性，然后采用模型迁移方法补偿机理简化和工况变化引起的预报误差.针对传统模型迁移方法不能处理过程非线性问题，提出局部模型迁移方法，在输入空间内建立多个局部迁移模型，通过融合算法组合成全局迁移模型来自适应校正机理模型的偏差.由于充分利用了机理模型的优势，相比现有的智能预报模型，该方法具有良好的预报精度.最后，通过现场实际数据验证了所提方法的有效性.

关键词: 精炼炉, 硫含量预报, 模型迁移, 聚类分析, 模糊TS

Abstract: Because of the modeling problems of the ladle furnace (LF) desulfurization process that are nonlinear， intensive dynamic and characterized of multiple conditions， end point sulphur content prediction model was proposed based on local model migration algorithm， where a simplified principle model was first established to capture the main process behavior and fine corrected by local model migration method to compensate the remain prediction error caused by mechanism simplification process and condition changes. A new local model migration algorithm was developed to automatically rectify the process nonlinearity deviation. The new method works by integrating several local migration models that are established in several local regions of the input space. The presented predictor shows better performance with respect to existing intelligent predictors due to the full exploitation of first principles， which is validated by the practical data.

Key words: ladle furnace, end point sulphur content prediction model, model migration, clustering analysis, fuzzy TS

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