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中南大学学报(自然科学版)

Journal of Central South University

第36卷    第1期    总第161期    2005年2月

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文章编号:1672-7207(2005)01-0113-06
基于综合建模方法的铅锌烧结块成分预测
王雅琳1,桂卫华1,阳春华1,陈晓方1,张传福2

(1.中南大学信息科学与工程学院,湖南长沙,410083;
2.中南大学冶金科学与工程学院,湖南长沙,410083
)

摘 要: 针对铅锌烧结过程异常复杂的实际情况,提出了一种既可保证预测精度又满足配料计算对数据完备性要求的铅锌烧结块成分预测智能集成模型。该模型综合了机理与多种智能建模方法的优点,对于正常生产情况(即数据完备区),通过模糊分类/组合以及神经网络NN分段描述方法建立了成分预测的监督式分布神经网络模型;对于异常或不常用工况(即数据不完备区),通过专家经验规则修正部分假定或统计参数方式建立经验机理模型;采用串、并联形式将2种模型有机结合,并通过专家推理进行集成协调与更新修正,形成智能集成模型,实现成分可
靠、准确的在线预测。在实际生产中运用该模型,烧结块铅、锌成分预测的相对误差分别为1.51%和0.41%。

 

关键字: 铅锌烧结;成分预测;经验机理模型;监督式分布神经网络;智能集成模型

Prediction for Composition of Pb Zn
Agglomerate Based on Integrated Modeling
WANG Ya-lin1,GUI Wei-hua1,YANG Chun-hua1,CHEN Xiao-fang1,ZHANG Chuan-fu2

1.School of Information Science and Engineering, Central South University , Changsha 410083, China;2.School of Metallurgical Science and Engineering, Central South University, Changsha 410083, China

Abstract:Considering the complexity in Pb Zn sintering process, an intelligent integrated modeling is presented to assure the composition prediction precision of Pb Zn agglomerate and to meet the requirements of the data completeness by blending computation, which synthesizes the advan tages of mechanism modeling and other intelligent methods. Firstly, under normal production conditions(or on the complete data section), a supervised distributed neural networks(SDNN) is established by fuzzy classification/combination and the description method of different neural networks(NN) on different subspace; then, under abnormal/unusual production conditions(or on the incomplete data section), an expertise and mechanism based(EM) model is built, in which some hypothetical or statistical parameters are modified by empirical rules; lastly, the EM model and the SDNN are integrated in series and parallel structure, and the integrated model is coordinated and updated by use of expert reasoning to realize the on-line prediction of composition. The intelligent integrated model is applied to industrial process, and the relative errors of prediction for lead and zinc are 1.51% and 0.41%, respectively.

 

Key words: Pb Zn sintering; prediction of composition; expertise and mechanism based model;supervised distributed neural networks; intelligent integrated model

中南大学学报(自然科学版)
  ISSN 1672-7207
CN 43-1426/N
ZDXZAC
中南大学学报(英文版)
  ISSN 2095-2899
CN 43-1516/TB
JCSTFT
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