Sewage pump station water level prediction method base on neural network

A neural network and sewage pumping station technology, applied in the field of automation, can solve problems such as pipeline overflow caused by unforeseen floods, and achieve the effects of strong practicality, high prediction accuracy, and improved prediction accuracy

Inactive Publication Date: 2010-09-01
SERVICE CENT OF COMMLIZATION OF RES FINDINGS HAIAN COUNTY
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Problems solved by technology

However, in the existing urban drainage pipe network system, the flow and water level of sewage flowing through each pipe network and pumping station can only be estimated by human experience. Theref

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  • Sewage pump station water level prediction method base on neural network
  • Sewage pump station water level prediction method base on neural network
  • Sewage pump station water level prediction method base on neural network

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Embodiment Construction

[0030] Taking the pumping station of the magnetic tape factory of the first sewage line in Hangzhou as an example, the specific implementation method of water level prediction modeling for sewage pumping stations based on BP neural network is carried out.

[0031] Step (1) Prediction model variable selection

[0032] (a) Mechanism analysis. The output of the model is the liquid level of the forebay of the pumping station. Given the cross-sectional area of ​​the forebay, the change of the forebay water storage can be obtained by multiplying the cross-sectional area of ​​the forebay by the water level of the forebay of the pumping station. According to the law of flow balance, the water storage in the forebay of the pumping station = the inflow of the forebay of the pumping station - the sewage discharge of the pumping station, where the inflow is mainly the sum of the discharge of the upstream pumping station and the inflow of local sewage. The sewage discharge of each pump st...

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Abstract

The invention discloses a sewage pump station water level prediction method base on neural network. The method comprises the steps of: firstly selecting prediction model variables including input variables and output variables, wherein input variables include an upstream pump station lifting capacity, forebay liquid level variable quantity, pump station discharge rate and switch pump liquid level control setting quantity and the output variables include a pump station forebay water level; secondly normalizing the data; then setting up a BP neural network framework and training the BP neural network; and finally carrying out denormalization on the data of the neural network. The method has higher prediction accuracy compared with the traditional steady flow computing method.

Description

technical field [0001] The invention belongs to the technical field of automation, and in particular relates to a method for predicting the water level of a rain-sewage mixed-flow pipe network pumping station in an urban drainage system based on a neural network. Background technique [0002] With the rapid development of cities, urban drainage has become one of the bottlenecks restricting the rapid development of cities. However, in the existing urban drainage pipe network system, the flow and water level of sewage flowing through each pipe network and pumping station can only be estimated by human experience. Therefore, the system cannot predict the occurrence of floods, pipeline overflows, let alone dispatch pumping stations. The unit is turned on to achieve the purpose of saving energy in the pumping station. [0003] The existing urban drainage system data acquisition and monitoring system (SCADA system) has accumulated a large amount of pumping station operation data,...

Claims

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Application Information

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IPC IPC(8): G05B13/02
Inventor 徐哲左燕薛安克周晓慧何必仕邬玲懿
Owner SERVICE CENT OF COMMLIZATION OF RES FINDINGS HAIAN COUNTY
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