Method for establishing DO prediction model based on optimized BP neural network

A BP neural network and prediction model technology, applied in biological neural network models, neural learning methods, neural architectures, etc., can solve problems such as failure to meet prediction requirements, failure to meet practical application requirements, results and actual output deviations, etc. , to achieve the effect of improving prediction accuracy, improving model performance, and facilitating precise control

Active Publication Date: 2017-11-17
广州博依特智能信息科技有限公司
View PDF4 Cites 22 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, when the problems to be solved are relatively complex, basic artificial neural networks (ANN) generally cannot meet the needs of practical applications, and even small network behaviors are not easy to understand, let alone large-scale multi- Layer nonlinear network struct

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Method for establishing DO prediction model based on optimized BP neural network
  • Method for establishing DO prediction model based on optimized BP neural network
  • Method for establishing DO prediction model based on optimized BP neural network

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0062] A method for establishing DO prediction model based on optimized BP neural network, including the following steps:

[0063] S1. Variable selection based on the goal of "aeration tank DO prediction".

[0064] Analyze the collected variables of the sewage treatment process related to the sewage treatment plant, and combine the actual situation of the sewage treatment plant to collect the influent F, the influent COD, the aeration volume Q, the aeration tank level LT, and the aeration tank sewage For variable data such as mud concentration MLSS, the correlation between each variable and the DO of the aeration tank is obtained through correlation analysis, and the factor variables with obvious correlation with the DO of the aeration tank are selected.

[0065] Through the correlation analysis between each variable and DO of the aeration tank from the perspective of the process and the data actually collected by the sewage treatment plant, the variables that have a significant impa...

Embodiment 2

[0115] Such as figure 1 , A method for establishing DO prediction model based on optimized BP neural network, including the following modeling and model evaluation steps:

[0116] 1. Through the control system of a sewage treatment plant to obtain the relevant data of the sewage treatment process, select all the data including 2017-03-03 07:00 to 2017-03-04 12:00 (data for this period (The lack of volume is relatively small), combined with the A2O process of sewage treatment, select the water intake F, the water COD, the aeration tank aeration Q, the aeration tank liquid level LT, and the aeration tank dissolved oxygen DO. Aeration tank sludge concentration MLSS and other variables. Using the Pearson correlation coefficient of formula (1) in Example 1, analyze the correlation between each variable and the DO of the aeration tank through correlation analysis, and select the variable that has a significant numerical correlation with the DO of the aeration tank—water intake F. Aer...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention discloses a method for establishing a DO prediction model based on an optimized BP neural network. The method comprises following steps: variables are selected on the basis of predicting aeration tank DO; data is pretreated; an initial prediction model of the aeration tank DO is established; and modeling data is used for training network models to obtain an initially built DO prediction model and then the model prediction effect is analyzed; parameter and structure optimizations on the initially built DO prediction model are performed one by one and then the initial weight and threshold of BP network are optimized by means of the mind evolutionary algorithm; after the optimized DO prediction model is obtained, the prediction precision and prediction performance of the optimized model are evaluated. The method improves the prediction accuracy of the time sequence data, improves the model performance to a certain extent and improves the prediction accuracy, and facilitates the implementation of the precise control of the aeration tank aeration in the sewage treatment plant.

Description

Technical field [0001] The invention relates to the technical field of sewage treatment, in particular to a method for establishing a DO prediction model based on an optimized BP neural network. Background technique [0002] Dissolved Oxygen (DO) plays a vital role in the wastewater treatment process of A2O process. The concentration of DO determines the degree of biochemical reaction in sewage treatment: DO is not sufficient, aerobic microorganisms cannot survive and play their role in oxidative decomposition; if the concentration of DO is too high, a large amount of DO will flow back to the anoxic tank when the sludge is returned, causing organic matter The increase in oxidation rate causes the denitrification and denitrification process to fail due to the lack of carbon source. Therefore, DO must be controlled within an appropriate range to achieve the effective goal of achieving sewage purification and reducing energy consumption. [0003] There are many factors that affect t...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G06F19/00G06N3/04G06N3/08
CPCG06N3/084G16C20/10G06N3/045
Inventor 李继庚张维蔡威满奕张燕忠刘焕彬
Owner 广州博依特智能信息科技有限公司
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap