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A Prediction Method of Water Quality Biotoxicity Based on Artificial Neural Network

A technology of artificial neural network and biotoxicity, which is applied in neural learning methods, biological neural network models, neural architectures, etc., can solve problems such as patent publications that have not yet been discovered, and achieve mature calculation methods, high sensitivity, and high prediction accuracy Effect

Active Publication Date: 2022-07-12
BEIHANG UNIV
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Problems solved by technology

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  • A Prediction Method of Water Quality Biotoxicity Based on Artificial Neural Network
  • A Prediction Method of Water Quality Biotoxicity Based on Artificial Neural Network
  • A Prediction Method of Water Quality Biotoxicity Based on Artificial Neural Network

Examples

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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 two-chamber bioelectrochemical system was constructed. The volume of the anode and cathode compartments were 18 mL and 32 mL, respectively. The cathode and anode compartments were separated by a pretreated proton exchange membrane (Nafion 117, Dupont, USA). A piece of 2.5cm×2.5cm carbon cloth (HCP330, Shanghai Hesen Electric Co., Ltd., China) was selected for the anode, and 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 washed with ultrapure water, dried, and then subjected to high-temperature ammonia treatment. The cathode is a piece of platinum-loaded carbon paper (HCP120, Shanghai Hesen Electric Co., Ltd., China) of 2cm×2cm, with a platinum loading of 0.5mg / cm 2 , use titanium wire ...

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Abstract

The invention relates to a water quality biological toxicity prediction method based on artificial neural network. The bioelectrochemical water quality comprehensive toxicity sensor is used to detect water samples, and the current change data of normal water samples and toxic water samples are detected by collecting the sensors. The node current inhibition rate is used as the input, and whether the water quality is toxic or not is used as the output; a perceptron neural network with 3 layers is selected, the number of hidden layers is 1, and the architecture is customized. Batch training type and conjugate gradient optimization algorithm are used to carry out The learning and training of the neural network finally obtains a neural network model that can accurately predict the biological toxicity of water quality. Based on the bioelectrochemical water quality comprehensive toxicity sensor with high detection sensitivity and the artificial neural network model with good prediction performance, the rapid and accurate prediction of the biological toxicity of water quality is realized.

Description

technical field [0001] The invention relates to the field of predicting the biological toxicity of water quality, in particular to the rapid prediction of the biological toxicity of water quality by using an artificial neural network. Background technique [0002] With the development of modern industry, the water environment ecosystem has been polluted more and more, and sudden environmental pollution incidents are not uncommon, 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, it often not only requires complex testing procedures and expensive equipment, but also cannot provide early warning of sudden water pollution events. B...

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Application Information

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Patent Type & Authority Patents(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