东北大学学报（自然科学版）

东北大学学报(自然科学版) ›› 2010, Vol. 31 ›› Issue (9): 1221-1225.DOI: -

• 论著 • 上一篇    下一篇

一类污水处理过程水质多模型在线软测量方法

丛秋梅;赵立杰;柴天佑;   

  1. 东北大学自动化研究中心;沈阳化工大学信息工程学院;东北大学流程工业综合自动化教育部重点实验室;
  • 收稿日期:2013-06-20 修回日期:2013-06-20 出版日期:2010-09-15 发布日期:2013-06-20
  • 通讯作者: -
  • 作者简介:-
  • 基金资助:
    国家重点基础研究发展计划项目((2009CB320601);;

A multi-model softsensing method of water quality in wastewater treatment process

Cong, Qiu-Mei (1); Zhao, Li-Jie (1); Chai, Tian-You (1)   

  1. (1) Automation Research Center, Northeastern University, Shenyang 110004, China; (2) School of Information Engineering, Shenyang University of Chemical Technology, Shenyang 110142, China; (3) Key Laboratory of Integrated Automation of Process Industry, Northeastern University, Shenyang 110004, China
  • Received:2013-06-20 Revised:2013-06-20 Online:2010-09-15 Published:2013-06-20
  • Contact: Cong, Q.-M.
  • About author:-
  • Supported by:
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摘要： 根据污水处理厂入水水质的特征参数进行工况区域分析,基于多模型方法建立了多工况下的水质软测量模型.其中局部模型由Hammerstein模型描述,采用误差反传类稳定学习算法学习非线性增益的多层感知器,采用递推最小二乘法学习线性部分ARX模型参数,根据样本与聚类中心之间的相近度在线修正聚类中心,基于软切换的多模型建模思路提出了出水水质COD的软测量方法.实验结果表明,在线修正聚类中心可反映工况点的动态变化;与实际运行数据进行了对比验证,表明多模型软测量方法具有较高的精度.

关键词: 污水处理过程, 多模型, 软测量, Hammerstein模型, 稳定学习

Abstract: Analyzing the varying operational conditions in accordance to the characteristic parameters of influent water quality in a wastewater treatment plant and based on the multi-model concept, an effluent water quality softsensing model was developed, where the submodel was described by Hammerstein model. With the error BP-like stable learning algorithm and the recursive least square method introduced to learn the multilayer perceptor as nonlinear gain and the ARX model as linear part of Hammerstein model, respectively, the clustering center was adjusted online according to the adjacency between the center and sample. Then, the softsensing method of effluent COD was proposed according to soft switch. The experimental results showed that the clustering centers adjusted online can reflect the varying operational conditions, and that the multi-model softsensing method can offer high accuracy in comparison with operational data.

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