Method for quick predicting total bacterial count of potable water network based on BP (Back Propagation) neural network

A technology of BP neural network and the total number of bacteria, which is applied in the direction of biological neural network model, measurement/inspection of microorganisms, biochemical equipment and methods, etc., can solve the problems of long data correction process, difficult direct application, and long time to obtain parameter values, etc. , to achieve the effect of simple measurement, reduced quantity and improved prediction quality

Inactive Publication Date: 2010-07-14
TIANJIN UNIV
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Problems solved by technology

[0003] The technical problem to be solved by the present invention is to provide a method that can solve the problems of the traditional prediction model, such as long data correction process, difficult direct application, long parameter value acquisition time, accurate and fast prediction of the total number of bacteria in the drinking water pipe network A rapid prediction method for the total number of bacteria in drinking water pipe network based on BP neural network

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  • Method for quick predicting total bacterial count of potable water network based on BP (Back Propagation) neural network
  • Method for quick predicting total bacterial count of potable water network based on BP (Back Propagation) neural network
  • Method for quick predicting total bacterial count of potable water network based on BP (Back Propagation) neural network

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[0020] The method for quickly predicting the total number of bacteria in the drinking water pipe network based on the BP neural network of the present invention will be described in detail below in conjunction with examples.

[0021] The method for quickly predicting the total number of bacteria in drinking water pipe network based on BP neural network of the present invention comprises the steps:

[0022] (1) Obtain the total number of bacteria in the tested drinking water pipe network and the values ​​of other relevant water quality indicators that have an impact on it as the detection data.

[0023] Obtaining other water qualities that have an impact on the total number of bacteria in the embodiments of the present invention is to sample once a day at 3 sampling points of the selected actual water supply pipe network, with the same sampling time period each time, continuously monitoring 35 times, and measuring freedom Free Chlorine (fCh), Total Chlorine (tCh), Turbidity (Tu...

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Abstract

The invention discloses a method for quick predicting total bacterial count of a potable water network based on BP (Back Propagation) neural network, comprising the following steps of (1) acquiring the total bacterial count of the tested potable water network and other related water quality index values influencing the count as detection data; (2) establishing an error-based backpropagation neural network; (3) training and testing the neural network; and (4) predict the total bacterial count of the potable water network by utilizing the neural network passed the test. The invention can establish a prediction model of the total bacterial count of the potable water network by 6 water quality indexes having high relativity with the total bacterial count and being quick tested and obtained by instruments only through limited tests, accurately and quickly predict the total bacterial count in the potable water network through computer simulation tests and scientific prediction, provide reliable information for water supply enterprises and guarantee bacteriological water quality security of the water supply network.

Description

technical field [0001] The invention relates to a method for predicting the total number of bacteria in a drinking water pipe network, in particular to a method for predicting the total number of bacteria in drinking water based on a BP neural network capable of accurately and rapidly predicting the total number of bacteria in the drinking water pipe network. Background technique [0002] Bacteriological water quality safety of drinking water pipe network is a common concern of water supply enterprises and users. When the factory water is transmitted through the water distribution system, complex physical, chemical and biological effects will occur in the pipeline, resulting in changes in water quality. At present, although researchers have paid more and more attention to simulating the growth of microorganisms, establishing a related model of microbial growth to replace the traditional method of counting microorganisms that is time-consuming and has poor accuracy. However,...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): C12Q1/06G06N3/02
Inventor 吴卿赵新华郑毅田一梅
Owner TIANJIN UNIV
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