Soft-sensing method and system for effluent COD of a wastewater treatment system

A waste water treatment system and soft measurement technology, applied in neural learning methods, testing water, biological neural network models, etc., can solve problems such as expensive online measuring instruments, difficult maintenance, and long time for effluent COD measurement

Inactive Publication Date: 2011-11-30
SOUTH CHINA UNIV OF TECH
View PDF7 Cites 27 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] In order to solve the problems of long measurement time of effluent COD in the wastewater treatment system, expensive on-line measuring instruments and difficult maintenance, the present invention combines fuzzy logic and artificial neural network to construct a fuzzy neural network, and uses an adaptive fuzzy C-means clustering algorithm to optimize fuzzy Number of Neural Network Rules

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
  • Soft-sensing method and system for effluent COD of a wastewater treatment system
  • Soft-sensing method and system for effluent COD of a wastewater treatment system
  • Soft-sensing method and system for effluent COD of a wastewater treatment system

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0056] A soft sensing method for effluent COD of a waste water treatment system based on adaptive fuzzy C-means clustering fuzzy neural network, comprising the following steps:

[0057] (1) Determination of the hydraulic retention time HRT entering the A / A / O wastewater treatment system, the pH value of the influent, the DO of the aerobic tank and the reflux ratio r of the mixed solution, and the actual COD value of the effluent flowing out of the wastewater treatment system;

[0058] (2) collect the above-mentioned measured data sample, adopt self-adaptive fuzzy C mean value clustering algorithm, carry out cluster analysis to data, set up COD fuzzy neural network model in MATLAB according to the number of clusters obtained, model is trained, until the error meets the requirements;

[0059] (3) Embed the trained fuzzy neural network model in the industrial computer, and use the configuration software MCGS to build a human-computer interaction interface;

[0060] (4) Use OPC te...

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 relates to a method and system for soft measurement of effluent COD in a wastewater treatment system, comprising the following steps: (1) measuring HRT, pH value of influent water, DO of an aerobic pool, reflux ratio r of a mixed solution, and actual effluent COD value; 2) Collect the above-mentioned data samples, use the adaptive fuzzy C-means clustering algorithm to cluster the data, establish a COD fuzzy neural network model according to the number of clusters obtained, and train the model until the error meets the requirements; (4 ) Embed the trained fuzzy neural network model into the industrial computer, and realize the data communication between the MATLAB software and the configuration software MCGS through OPC technology, so as to realize the online real-time monitoring of COD in the effluent of the wastewater treatment system. The invention can solve the problems of high price, difficult maintenance and lagging measurement of the COD measuring instrument for the soft measurement of the COD of the effluent of the wastewater treatment system, and truly realize the online real-time monitoring of the effluent quality.

Description

technical field [0001] The invention relates to the technical field of wastewater treatment, in particular to a soft measurement method and system for COD of effluent from a wastewater treatment system. Background technique [0002] With the continuous improvement of modern industrial process requirements for control, calculation, energy efficiency and operational reliability, various measurement requirements are increasing. The connotation and extension of modern process testing have been greatly deepened and expanded compared with the past. On the one hand, only obtaining information on conventional process parameters such as flow rate, temperature, and pressure can no longer meet the requirements of process operation and control, and it is necessary to obtain measurement information on detection parameters closely related to process operation and control, such as composition and physical properties. On the other hand, the accuracy requirements of instrument measurement a...

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
Patent Type & Authority Applications(China)
IPC IPC(8): G01N33/18G06N3/08
Inventor 万金泉胡康马邕文王艳黄明智黄乾
Owner SOUTH CHINA UNIV OF TECH
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