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论文标题： (基于紫外-可见分光光度法和导数神经网络算法的排水类型在线识别系统)

期刊：

作者：Qiyun Zhu, April Gu, Dan Li, Tianmu Zhang, Lunhong Xiang, Miao He

发表时间：13 Apr 2021

DOI：

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原文链接：

文章出版：Front. Environ. Sci. Eng. 2021, 15 (6): 136

原文信息

题目：

作者：

Qiyun Zhu¹, April Gu², Dan Li³, Tianmu Zhang¹, Lunhong Xiang¹, Miao He ()¹

作者单位：

1 School of Environment, Tsinghua University, Beijing 100084, China

2 Department of Civil and Environmental Engineering, Cornell University, Ithaca, NY 14853, USA

3 Department of Environmental Science and Engineering, Fudan University, Shanghai 200438, China

关键词：

Drainage online recognition (排水在线识别系统); UV-vis spectra (紫外-可见分光光度法); Derivative spectrum (导数光谱); Convolutional neural network (卷积神经网络)

文章亮点

• 采用紫外-可见分光光度法对排水类型进行在线识别。

• 收集并评估了4种排水类型的紫外-可见光谱。

• 建立了多元导数混合输入卷积神经网络。

• 评价比较了不同网络结构和输入内容的影响。

文章简介

城市排水管网系统十分复杂，污水来源具有多样性，优化污水收集系统对水污染控制和提高污水处理厂的运行效率具有重要意义。目前全国各地正在加速推进城市排水管网的升级改造，以提高其分类收集能力。因此，如何建立和完善有效的在线监测和识别系统是一个亟待解决的关键问题。在众多的水质监测技术中，基于紫外-可见分光光度法的探头，由于其体积小、检测速度快，对于排水在线分类具有很高的应用潜力。然而，探头的光学分辨率、动态范围和信噪比等性能参数较弱，且污水浊度高会提高噪声水平，因此有必要提取排水光谱中的形状特征进行分类。本研究结合紫外-可见分光光度法和导数神经网络算法，建立了一种在线识别排水类型的系统。

实验表明，结合高效的采样和清洗系统，紫外-可见分光光度法可以采集排水管网里水体的紫外可见吸收光谱，利用导数对光谱形状特征进行放大，建立基于卷积神经网络的非线性分类算法对污水进行分类。通过将原始光谱、一阶导数光谱和两者的组合设置为三个不同的输入，并且将带卷积层和不带卷积层的人工神经网络设置为两种不同的网络结构，来比较不同输入和网络结构对分类精度的影响。结果表明，导数混合输入卷积神经网络对生活污水、混合雨水、雨水和工业污水的分类效果最佳。工业废水识别率为100%，生活污水与雨水混合系统识别率均在90%以上。

图1 摘要图

编者点评

随着排水系统截污纳管和提质增效的逐步深化，必需要建立可靠的排水管理系统。本研究建立了一个基于紫外-可见分光光度法和导数神经网络算法的排水管网在线监测系统，对于评价城市排水管网中雨水与污水的分离效果，识别工业污水并早期预警，监测入河排污口出水质量具有重要意义，甚至对排水管网的问题诊断、改造和管理具有潜在的指导意义。

编者简介

卢琦，女，24岁，湖南大学环境明升体育app与工程学院2019级环境明升体育app与工程专业硕士生，导师为王冬波教授，研究方向为污泥资源化利用。

摘要

Optimizing sewage collection is important for water pollution control and wastewater treatment plants quality and efficiency improvement. Currently, the urban drainage pipeline network is upgrading to improve its classification and collection ability. However, there is a lack of efficient online monitoring and identification technology. UV-visible absorption spectrum probe is considered as a potential monitoring method due to its small size, reagent-free and fast detection. Because the performance parameters of probe like optic resolution, dynamic interval and signal-to-noise ratio are weak and high turbidity of sewage raises the noise level, it is necessary to extract shape features from the turbidity disturbed drainage spectrum for classification purposes. In this study, drainage network samples were online collected and tested, and four types were labeled according to sample sites and environment situation. Derivative spectrum were adopted to amplify the shape features, while convolutional neural network algorithm was established to conduct nonlinear spectrum classification. Influence of input and network structure on classification accuracy was compared. Original spectrum, first-order derivative spectrum and a combination of both were set to be three different inputs. Artificial neural network with or without convolutional layer were set be two different network structures. The results revealed a convolutional neural network combined with inputs of first and zero-order derivatives was proposed to have the best classification effect on domestic sewage, mixed rainwater, rainwater and industrial sewage. The recognition rate of industrial wastewater was 100%, and the recognition rate of domestic sewage and rainwater mixing system were over 90%.

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