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Sludge bulking prediction method based on peak self-organizing radial basis function neural network

A technology based on neural networks and neural networks, applied in neural learning methods, biological neural network models, etc., can solve problems such as difficulty in establishing sludge bulking mechanism models, large measurement errors, complex mechanism and dynamic characteristics, etc.

Active Publication Date: 2014-07-23
BEIJING UNIV OF TECH
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Problems solved by technology

However, the traditional method has large measurement errors, takes a long time, and is cumbersome to operate
At the same time, due to the complex mechanism and dynamic characteristics of the sludge bulking process, and the various causative factors are highly nonlinear and strongly coupled, it is difficult to establish a mechanism model for sludge bulking

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  • Sludge bulking prediction method based on peak self-organizing radial basis function neural network
  • Sludge bulking prediction method based on peak self-organizing radial basis function neural network
  • Sludge bulking prediction method based on peak self-organizing radial basis function neural network

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Embodiment Construction

[0068] The present invention obtains an intelligent prediction method for the sludge volume index SVI value, which is a key parameter of sludge bulking. The method is based on the peak self-organizing radial basis neural network, and by simultaneously adjusting the structure and parameters of the peak self-organizing radial basis neural network, Improve the adaptive ability and predictive performance of the neural network, establish the soft sensor model of the sludge volume index SVI, and realize the online detection of sludge bulking;

[0069] The experimental data comes from the annual water quality analysis daily report of a sewage treatment plant in Beijing in 2011. After excluding abnormal experimental samples, there are 345 sets of available data. All 345 sets of data samples are divided into two parts: 225 sets of data are used as training samples, and the remaining 120 sets of data are used as training samples. group data as a test sample;

[0070] The present inventi...

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Abstract

The invention discloses an SVI prediction method based on a peak self-organizing radial basis function neural network, not only belong to the field of control science and engineering, but also belongs to the field of environmental science and engineering. In order to solve the problems that the sludge bulking kinetics characteristic is complex and key parameters are difficult to measure in the sewage disposal process, the method achieves accurate prediction of sludge bulking. As the prediction method adjusts the structure and connection weight of the radial basis function neural network at the same time, the information processing capacity of the neural network is improved, and the prediction precision of the SVI is improved; an experimental result shows that the intelligent prediction method can accurately predict the SVI and promote the efficient and stable operation of the sewage disposal process.

Description

technical field [0001] The invention utilizes the radial basis neural network based on the peak self-organization to predict the sludge volume index SVI, which is a key index of sludge bulking, and realizes the real-time detection of sludge bulking. The real-time prediction of sludge volume index (SVI) is an important link to realize the control of sludge bulking and an important basis for the normal operation of the sewage treatment process. It belongs to the field of control science and engineering, and belongs to the field of environmental science and engineering. Background technique [0002] With the growth of the national economy and the enhancement of public awareness of environmental protection, sewage treatment automation technology has ushered in unprecedented opportunities for development. The National Water Pollution Prevention Law proposes to increase the reuse rate of water, encourage scientific and technological research and the popularization and application ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06N3/08
Inventor 韩红桂王丽丹李颖乔俊飞
Owner BEIJING UNIV OF TECH
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