Soft-sensor method for ion concentration of electrochemical wastewater treatment based on STA-K-means clustering

Meihua QIN; Hongqiu ZHU; Yonggang LI; Junming CHEN; Fengxue ZHANG; Wenting LI

doi:10.11949/0438-1157.20190279

您当前的位置：

首页 >

文章列表页 >

Soft-sensor method for ion concentration of electrochemical wastewater treatment based on STA-K-means clustering

更新时间：2026-03-25

- Soft-sensor method for ion concentration of electrochemical wastewater treatment based on STA-K-means clustering
- CIESC Journal Vol. 70, Issue 9, Pages: 3458-3464(2019)
- 作者机构：
  
  中南大学自动化学院,湖南,长沙,410083
- 作者简介：
- 基金信息：
- DOI：10.11949/0438-1157.20190279
  CLC： TP274
- Online First：05 September 2019，
  
  Published：05 September 2019
- 稿件说明：
移动端阅览
Meihua QIN, Hongqiu ZHU, Yonggang LI, et al. Soft-sensor method for ion concentration of electrochemical wastewater treatment based on STA-K-means clustering[J]. CIESC Journal, 2019, 70(9): 3458-3464.
DOI：

Meihua QIN, Hongqiu ZHU, Yonggang LI, et al. Soft-sensor method for ion concentration of electrochemical wastewater treatment based on STA-K-means clustering[J]. CIESC Journal, 2019, 70(9): 3458-3464. DOI： 10.11949/0438-1157.20190279.

摘要

针对电化学废水处理过程出口离子浓度无法在线检测的问题，提出了一种基于状态转移的K均值聚类算法的软测量建模方法。在分析内部反应机理的基础上，结合物料平衡和吸附动力学定理建立电化学过程的机理模型；由于单一的软测量模型难以满足实际的精度要求，提出一种基于状态转移的K均值聚类算法将原始数据集进行聚类，应用状态转移算法对K均值算法的初始聚类中心进行优化，同时，引入离群值矩阵动态迭代同时实现数据聚类和异常值检测；最后，对聚类后的不同训练子集分别建立子模型，综合各子模型得到基于多模型切换方法的软测量模型。通过某废水处理厂的现场数据进行实例验证，结果证明了所建立的电化学废水处理过程离子浓度软测量模型合理有效。

Abstract

Aiming at the problem that the ion concentration cannot be detected online during the electrochemical wastewater treatment process

a soft-sensor modeling method based on K-means clustering algorithm of state transition is proposed. On the basis of analyzing the mechanism in the electrochemical process

the mechanism model of the electrochemical process was established according to the material balance and adsorption kinetics. Since single model cannot meet the requirements of accuracy

a K-means clustering algorithm based on state transition is proposed to cluster the original data set. This algorithm uses the state transfer algorithm to optimize the initial clustering center of the K-means algorithm

and introduce a matrix to realize clustering and outlier detection simultaneously during iterative process. Then

after clustering

the sub-models are established with the training subsets respectively

and the soft measurement model based on the multi-model switching method is obtained by synthesizing the sub-models.Finally

via field data verification of a wastewater treatment plant

the results show that the soft measurement model of ion concentration in electrochemical wastewater treatment process is reasonable and effective.

关键词

Keywords

references

Views

172

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Kinetics and modeling of NKC-9-catalyzed ring-opening polymerization of cyclosiloxanes

Study on the thermodynamics and kinetics of the direct synthesis of triacetoneamine from acetone and ammonia

Substance flow analysis method based on molecular-level model for catalytic reforming process

The CO reduction characteristics of magnetite oxygen carriers in chemical looping hydrogen production systems

Preparation of polyimide-reinforced lignocellulosic nanofibril aerogel and its oil-water separation performance

Related Author

ZHU Wenhao

LUO Qi

ZHANG Yinxu

LI Jinjin

XI Zhenhao

ZHAO Ling

YANG Yong

SUN Weizhen

Related Institution

State Key Laboratory of Chemical Engineering and Low-carbon Technology, School of Chemical Engineering, East China University of Science and Technology

Hoshine Silicon (Shanshan) Industry Co., Ltd.

State Key Laboratory of Chemical Engineering and Low-carbon Technology, East China University of Science and Technology

Department of Chemical Engineering, Tsinghua University

Sinopec (Dalian) Research Institute of Petroleum and Petrochemicals Co., Ltd.

⁰