On soft sensor of chemical oxygen demand by SOM-RBF neural network

Xiaoqin LIAN; Liwei WANG; Sa AN; Wei WEI; Zaiwen LIU

doi:10.11949/0438-1157.20190122

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On soft sensor of chemical oxygen demand by SOM-RBF neural network

更新时间：2026-03-25

- On soft sensor of chemical oxygen demand by SOM-RBF neural network
- CIESC Journal Vol. 70, Issue 9, Pages: 3465-3472(2019)
- 作者机构：
  
  1. 北京工商大学计算机与信息工程学院,北京,100048
  2. 食品安全大数据技术北京市重点实验室,北京,100048
- 作者简介：
- 基金信息：
- DOI：10.11949/0438-1157.20190122
  CLC： TP183
- Online First：05 September 2019，
  
  Published：05 September 2019
- 稿件说明：
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Xiaoqin LIAN, Liwei WANG, Sa AN, et al. On soft sensor of chemical oxygen demand by SOM-RBF neural network[J]. CIESC Journal, 2019, 70(9): 3465-3472.
DOI：

Xiaoqin LIAN, Liwei WANG, Sa AN, et al. On soft sensor of chemical oxygen demand by SOM-RBF neural network[J]. CIESC Journal, 2019, 70(9): 3465-3472. DOI： 10.11949/0438-1157.20190122.

摘要

污水处理是一个复杂的非线性过程，化学需氧量（chemical oxygen demand，COD）是评价污水处理效果的关键指标之一。COD的传统测量方法耗时长、成本高，基于传统神经网络的软测量方法提高了COD参数的测量速度但精度较差。针对这些问题，设计一种结合自组织特征映射 (self-organizing map

SOM)和径向基函数(radial basis function

RBF)神经网络的COD参数软测量方法。该方法利用SOM网络聚类数据样本，根据所得聚类结果确定RBF网络的隐层节点数及节点的数据中心，综合提高RBF网络的收敛速度和拟合精度。利用污水处理厂部分水样数据建立COD软测量模型，模型仿真和硬件在线测试结果表明，相对于传统的BP、RBF等网络，基于SOM-RBF神经网络的COD软测量方法测量时间短、预测精度较高，具有较为广阔的应用前景。

Abstract

Sewage treatment is a complex nonlinear process

and chemical oxygen demand (COD) is one of the key indicators for evaluating the effectiveness of wastewater treatment. It is costly and time-consuming to get COD by traditional chemical approaches. By neural networks

it is faster

but it is not accurate enough. To address them

a soft sensor approach

which is based on the combination of self-organizing map (SOM) and radial basis function (RBF) neural network

is designed. SOM is taken to cluster data samples. The number of hidden layer nodes and the center vector of the nodes are determined by clustering results. By such disposal

the rate of convergence and fitting precision have been improved. Part data of water samples from a sewage treatment plant are taken to establish the soft sensor model of COD. Test results provided by numerical model and hardware show that

compared with the traditional BP

RBF and other networks

the soft sensor model of COD designed in this paper has short measurement time and relatively high prediction accuracy. It may be a promising soft sensor approach in applications.

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