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Method for predicting effluent COD concentration in A2O sewage treatment process

A sewage treatment and concentration technology, applied in neural learning methods, biological neural network models, etc., can solve problems such as lagging of biochemical reactions

Inactive Publication Date: 2016-09-28
SHENZHEN KITEWAY AUTOMATION ENG
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Problems solved by technology

However, the A2O sewage treatment process is a complex nonlinear system with a large lag, and its effluent water quality is affected by COD (Chemical Oxygen Demand) and related sewage pH, influent pollutant concentration, sludge concentration, and dissolved oxygen. Influenced by various factors such as concentration, the biochemical reaction process has hysteresis characteristics

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  • Method for predicting effluent COD concentration in A2O sewage treatment process
  • Method for predicting effluent COD concentration in A2O sewage treatment process
  • Method for predicting effluent COD concentration in A2O sewage treatment process

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[0045] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0046] figure 1It shows the flow of a method for predicting the COD concentration of effluent in the A2O sewage treatment process provided by an embodiment of the present invention. The method is implemented based on BP neural network modeling. The BP neural network is selected because the neural network can be used in the modeling process. The BP neural network is a model directly established based on the input / output data of the object, which does not require complex mathematical formula derivation and empirical knowledge of the object, and does not need to delve into the mechanism of action betw...

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Abstract

The present invention relates to the field of sewage treatment, and is realized based on BP neural network modeling. By establishing the output layer variable of the neural network, the effluent COD concentration, and selecting the input layer variable, the transfer function of the hidden layer and the transfer function of the output layer of the neural network, A BP neural network model is established; sample data of the input layer variables and output layer variables of the neural network are selected, abnormal data processing is performed on the sample data, and the BP neural network model is used for training and prediction to obtain the COD concentration of the effluent. This method can quickly and accurately predict the effluent COD concentration during A2O wastewater treatment, providing reliable control conditions for A2O wastewater treatment.

Description

technical field [0001] The invention relates to the field of sewage treatment, in particular to a method for predicting the COD concentration of effluent in the A2O sewage treatment process. Background technique [0002] The A2O method, also known as the AAO method, is the abbreviation of the first letter of the English Anaerobic-Anoxic-Oxic (anaerobic-anoxic-aerobic method). It is a commonly used secondary sewage treatment process and can be used for secondary sewage treatment or Tertiary sewage treatment has good nitrogen and phosphorus removal effects. However, the A2O sewage treatment process is a complex nonlinear system with a large lag, and its effluent water quality is affected by COD (Chemical Oxygen Demand) and related sewage pH, influent pollutant concentration, sludge concentration, and dissolved oxygen. Affected by various factors such as concentration, the biochemical reaction process has hysteresis characteristics. Among them, chemical oxygen demand (COD) is...

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Application Information

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IPC IPC(8): G06N3/08
CPCG06N3/088
Inventor 唐思明武延坤
Owner SHENZHEN KITEWAY AUTOMATION ENG
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