Eureka AIR delivers breakthrough ideas for toughest innovation challenges, trusted by R&D personnel around the world.

Water quality biotoxicity prediction method based on artificial neural network

A technology of artificial neural network and biotoxicity, applied in the direction of neural learning method, biological neural network model, neural architecture, etc., can solve problems such as patent publications that have not yet been found, and achieve the effects of mature calculation methods, high sensitivity, and reduced detection costs

Active Publication Date: 2020-11-17
BEIHANG UNIV
View PDF8 Cites 5 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Through the search, no patent publications related to the application of the present invention have been found

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
  • Water quality biotoxicity prediction method based on artificial neural network
  • Water quality biotoxicity prediction method based on artificial neural network
  • Water quality biotoxicity prediction method based on artificial neural network

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0026] Example 1 A dual-chamber bioelectrochemical system water quality comprehensive toxicity sensor is used to detect water samples and obtain data for training a neural network model. First, a dual-chamber bioelectrochemical system was constructed. The volumes of the anode and cathode chambers were 18 mL and 32 mL, respectively, and the cathode and anode chambers were separated by a pretreated proton exchange membrane (Nafion 117, Dupont, USA). A 2.5cm×2.5cm carbon cloth (HCP330, Shanghai Hesen Electric Co., Ltd., China) was used as the anode, soaked overnight with a mixture of acetone and ethanol (v:v=50%:50%) before use to remove possible adsorption on the surface. The organic matter is cleaned with ultra-pure water and then dried at high temperature for ammonification treatment. The cathode is a 2cm×2cm platinum-loaded carbon paper (HCP120, Shanghai Hesen Electric Co., Ltd., China), and the platinum load is 0.5mg / cm 2 , use a titanium wire to connect the anode and catho...

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 water quality biotoxicity prediction method based on an artificial neural network. The method comprises the steps: detecting a water body water sample through a bioelectrochemical water quality comprehensive toxicity sensor, and collecting the detected current change data ofa normal water body water sample and a toxic water body water sample; with the current inhibitingrate at a specific time node being input and the toxicity of the water being output, selecting a perceptron neural network containing three layers, wherein the number of hidden layers of the perceptron neural network is 1, and a system structure is self-defined; learning and training the neural network by means of batch processing training types and a conjugate gradient optimization algorithm, andfinally acquiring a neural network model capable of accurately predicting the water quality biotoxicity. Based on a bioelectrochemical water quality comprehensive toxicity sensor with high detectionsensitivity and an artificial neural network model with good prediction performance, the biotoxicity of the water quality of the water body can be rapidly and accurately predicted.

Description

technical field [0001] The invention relates to the field of predicting water quality biological toxicity, in particular to the rapid prediction of water quality biological toxicity by using an artificial neural network. Background technique [0002] With the development of modern industry, the water environment ecosystem has been more and more polluted, and sudden environmental pollution incidents are common, seriously threatening ecological security and people's health. In order to strengthen the monitoring and tracking of water quality, my country has established a surface water environmental quality monitoring network, but the monitoring items are mainly physical and chemical indicators. Although physical and chemical analysis can quantify the content of a certain type or type of pollutants in water bodies, it often not only requires complex detection procedures and expensive instruments and equipment, but also cannot provide early warning for sudden water pollution even...

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): G01N27/416G06N3/04G06N3/08
CPCG01N27/416G06N3/08G06N3/045Y02A20/20
Inventor 刘红藏雨轩易越谢倍珍
Owner BEIHANG UNIV
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
Eureka Blog
Learn More
PatSnap group products

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

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com