Modularized neural network-based effluent BOD sensor anomaly detection method

A neural network and anomaly detection technology, applied in the field of artificial intelligence, can solve problems such as lag, heavy load, vibration, etc.

Pending Publication Date: 2021-05-18
BEIJING UNIV OF TECH
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Problems solved by technology

The current standard method for BOD measurement is the dilution and inoculation method, but the procedure is cumbersome, the measurement cycle is long, there is a serious hysteresis, and it cannot reflect the change of BOD in the water body
In recent years, a large number of random BOD sensors have been put into applic

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  • Modularized neural network-based effluent BOD sensor anomaly detection method
  • Modularized neural network-based effluent BOD sensor anomaly detection method
  • Modularized neural network-based effluent BOD sensor anomaly detection method

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Embodiment Construction

[0094] The invention provides a method for detecting the abnormality of the effluent BOD sensor based on a modular neural network. Through the online soft measurement detection of the output of the effluent BOD sensor through the neural network, the detection accuracy and timeliness of the abnormality of the effluent BOD sensor in the sewage treatment process are improved. The real-time monitoring level of BOD in the effluent of urban sewage treatment plants has been improved to ensure the normal operation of the sewage treatment process:

[0095] The example of the present invention adopts the water quality analysis data of a sewage plant in 2006, which contains 2688 groups of data from dry days and rainy days altogether, and 10 water quality variables, including (1) water inflow; (2) effluent So (Oxygen) concentration; (3) Concentration of Sno (Nitrate and nitrite nitrogen) in effluent; (4) Concentration of Snh (NH4++NH3 nitrogen) in influent; (5) Concentration of Snh (NH4++N...

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Abstract

The invention discloses an effluent BOD sensor anomaly detection method based on a modular neural network, relates to the field of artificial intelligence, and is directly applied to the field of sewage treatment. In order to solve the problems that in the current sewage treatment process, an effluent BOD sensor drifts and abrupt abnormity cannot be detected in real time under complex working conditions, samples are automatically classified and input according to the working conditions by adopting a density-based clustering algorithm; extracting an effluent BOD auxiliary variable as an input variable of each sub-network in the modular network by using a mutual information-based method; designing a self-organizing RBF neural network based on error correction as a sub-network, and training the network through an improved Levenberg-Marquardt (LM) algorithm to improve the training speed. The result shows that the abnormity detection method is compact in structure, abnormity of the effluent BOD sensor in the sewage treatment process can be rapidly and accurately detected, and technical guarantee is provided for safe and stable operation of sewage treatment.

Description

Technical field: [0001] The invention relates to the field of artificial intelligence, is directly applied to the field of abnormal detection of BOD sensors in sewage treatment, and in particular relates to a method for detecting abnormalities of effluent BOD sensors based on a modular neural network. Background technique: [0002] Biochemical oxygen demand (Biochemical Oxygen Demand, BOD) is an important parameter reflecting the degree of organic pollution of water bodies, an important index for evaluating sewage water quality and an important control parameter for sewage treatment process. The detection and measurement of BOD is of great significance to sewage treatment . The current standard method for BOD measurement is the dilution and inoculation method, but the procedure is cumbersome, the measurement cycle is long, there is a serious hysteresis, and it cannot reflect the change of BOD in the water body. In recent years, a large number of random BOD sensors have been...

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Application Information

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IPC IPC(8): G06K9/62G06N3/04G01N33/18
CPCG06N3/04G01N33/1806G06F18/2321G06F18/214
Inventor 李文静张竣凯
Owner BEIJING UNIV OF TECH
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