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

被引：0
作者：
Lian X. [1 ,2 ]
Wang L. [1 ,2 ]
An S. [1 ,2 ]
Wei W. [1 ,2 ]
Liu Z. [1 ,2 ]
机构：
[1] School of Computer and Information Engineering, Beijing Technology and Business University, Beijing
[2] Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing
来源：
Huagong Xuebao/CIESC Journal | 2019年 / 70卷 / 09期
关键词：
Chemical oxygen demand; Model; Neural network; Prediction; Radial basis function; Self-organizing map; Soft sensor;
D O I：
10.11949/0438-1157.20190122
中图分类号：
学科分类号：
摘要：
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. © All Right Reserved.
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页码：3465 / 3472
页数：7
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