基于偏最小二乘随机配置网络的污水水质指标估计

赵立杰; 王佳; 黄明忠; 王国刚

doi:10.11949/0438-1157.20200604

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基于偏最小二乘随机配置网络的污水水质指标估计

过程系统工程 | 更新时间：2026-07-24

- 基于偏最小二乘随机配置网络的污水水质指标估计
- Estimation of effluent quality index based on partial least squares stochastic configuration networks
- 化工学报 2020年71卷第12期页码：5672-5680
- 作者机构：
  
  沈阳化工大学信息工程学院，辽宁沈阳 110142
- 作者简介：
  
  赵立杰（1972—），女，博士，教授，zlj_lunlun@163.com
  黄明忠（1975—），男，硕士，副教授，syhmz75111@syuct.edu.cn
- 基金信息：
  
  国家重点研发计划项目(2018YFB1700200)
- DOI：10.11949/0438-1157.20200604
  中图分类号： TN 911.7
- 收稿：2020-05-18，
  
  修回：2020-08-31，
  
  网络首发：2020-12-17，
  
  纸质出版：2020-12-05
- 稿件说明：
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赵立杰, 王佳, 黄明忠, 王国刚. 基于偏最小二乘随机配置网络的污水水质指标估计[J]. 化工学报, 2020, 71(12): 5672-5680

ZHAO Lijie, WANG Jia, HUANG Mingzhong, WANG Guogang. Estimation of effluent quality index based on partial least squares stochastic configuration networks[J]. CIESC Journal, 2020, 71(12): 5672-5680
赵立杰, 王佳, 黄明忠, 王国刚. 基于偏最小二乘随机配置网络的污水水质指标估计[J]. 化工学报, 2020, 71(12): 5672-5680 DOI： 10.11949/0438-1157.20200604.

ZHAO Lijie, WANG Jia, HUANG Mingzhong, WANG Guogang. Estimation of effluent quality index based on partial least squares stochastic configuration networks[J]. CIESC Journal, 2020, 71(12): 5672-5680 DOI： 10.11949/0438-1157.20200604.

摘要

准确、可靠地测量污水处理厂的出水水质指标是成功控制和优化污水处理厂的关键。由于现有的离线化验方法存在操作繁复、测量滞后的问题，难以实现水质的实时控制。为了提高估计的准确性和可靠性，提出了一种偏最小二乘的随机配置网络方法（PLS-SCN）。为了克服输入数据高维度和多重共线性导致的预测风险，将偏最小二乘（PLS）方法嵌入到随机配置网络（SCN）框架中，以代替经典的普通最小二乘（OLS）方法。PLS-SCN方法从隐含层输出中提取影响水质指标的主要潜在变量，通过正交投影运算来增强泛化性能。某城市污水处理厂水质指标仿真结果表明，PLS-SCN网络具有良好的输入输出关系，性能优于传统SCN和PLS方法，能够快速、可靠地估计污水水质的质量。

Abstract

Accurate and reliable measurement of the effluent quality indicators of wastewater treatment plants is the key to successful control and optimization of wastewater treatment plants. Due to the complexity of the operation and the delay of laboratory analysis

it is difficult to achieve real-time control of effluent quality. In order to improve the accuracy and reliability of the estimation

this paper proposes a method of stochastic configuration network based on partial least squares (PLS-SCN). In order to overcome the forecast risk caused by high dimensionality and multicollinearity of the input data

the partial least squares(PLS) is embedded into the stochastic configuration network(SCN) framework replacing the classic ordinary least squares (OLS). The PLS-SCN method extracts the main latent variables that affect the effluent quality from the output of the hidden layer

and enhances the generalization performance through orthogonal projection operations. The simulation results of the effluent quality index of a municipal sewage treatment plant show that the PLS-SCN network has a good input and output relationship

and its performance is better than traditional SCN and PLS

and it can quickly and reliably estimate the sewage quality.

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