An improved prediction model for COD measurements using UV-Vis spectroscopy

In the 21st century, although water quality has been improved in the last two decades, water pollution by organic contaminants has remained a non-negligible issue in China, so Chemical-Oxygen Demand (abbreviated as COD, unit: mg L −1 ) is often used as the main index to measure the degree of surface...

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Vydáno v:RSC advances Ročník 14; číslo 1; s. 193 - 25
Hlavní autoři: Guan, Li, Zhou, Yijun, Yang, Sen
Médium: Journal Article
Jazyk:angličtina
Vydáno: England Royal Society of Chemistry 02.01.2024
The Royal Society of Chemistry
Témata:
Absorption spectra
Algorithms
Chemical oxygen demand
Chemistry
Contaminants
Datasets
Discrete Wavelet Transform
Noise sensitivity
Noise tolerance
Oxygen demand
Prediction models
Spectrum analysis
Surface water
Turbidity
Ultraviolet spectroscopy
Water pollution
Water quality
Water sampling
Wavelet transforms
ISSN:2046-2069, 2046-2069
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Shrnutí:In the 21st century, although water quality has been improved in the last two decades, water pollution by organic contaminants has remained a non-negligible issue in China, so Chemical-Oxygen Demand (abbreviated as COD, unit: mg L −1 ) is often used as the main index to measure the degree of surface water pollution. UV-Vis spectroscopy, as a sensitive and rapid analytical technique, is a green detection technology suitable for automatic online COD detection equipment. However, due to the complex composition of surface water, the interference degree of the UV-Vis spectrum caused by turbidity is strongly correlated with the size, type and color of particulate matter in the solution, which results in noise sensitivity and poor generalization of the current detection model. Therefore, the main purpose of this research is to improve the traditional detection model performance by using deep learning and a spectrum preprocessing algorithm. Firstly, we used an improved noise filter based on discrete wavelet transforms to solve the noise sensitivity. Secondly, we proposed a novel COD detection network to address poor generalization. Thirdly, we collected a total of 2259 water samples' UV-Vis absorption spectra and corresponding COD as a dataset. Then, we pipelined the improved noise removal algorithm and proposed COD detection network, as a complete COD prediction model. Finally, the experiment on the dataset shows that the COD prediction model has a good performance in terms of both noise tolerance and accuracy. We have proposed and developed an improved online COD prediction measurement. The work includes designing the structure and workflow of an online automatic detection module, and proposing an improved noise removal method and a COD detection network.
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ISSN:2046-2069
2046-2069
DOI:10.1039/d3ra05472a