首页|基于紫外-可见光谱法的工业废水CNN-GRU分类模型研究

基于紫外-可见光谱法的工业废水CNN-GRU分类模型研究

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工业废水分类是水污染防治和水资源管理的前提和基础,相较于生活污水,工业废水的分类研究相对滞后.水体化学需氧量(COD)是衡量水体质量的核心指标,针对现有工业废水COD分类算法中预测精度较低的问题,提出基于门控循环单元(GRU)的卷积神经网络(CNN)混合模型.该模型首先将紫外-可见光谱法测得的工业废水COD数据进行高斯滤波去噪,然后把去噪后的光谱数据输入CNN模型进行特征提取,最后通过GRU神经网络实现工业废水COD分类.实验结果显示,CNN-GRU分类模型经过200次训练后达到收敛,分类精度达到99.5%,与长短期记忆方法、GRU方法、CNN-LSTM方法相比,该混合模型的分类精度具有显著优势.
Research on CNN-GRU industrial wastewater classification model based on UV-Vis spectroscopy
The classification of industrial wastewater is a prerequisite and foundation for water pollution prevention and water resources management.However,compared to domestic sewage,research on industrial wastewater classification is relatively lagging behind.Chemical Oxygen Demand(COD)of water is a core indicator for measuring water quality.To address the problem of low prediction accuracy in existing industrial wastewater COD classification algorithms,a convolutional neural network(CNN)hybrid model based on gated recurrent units(GRU)is proposed.According to the hybrid model,the COD data of industrial wastewater measured by UV-Vis spectroscopy is subjected to Gaussian filtering and denoising at the first,then the denoised spectral data is input into the CNN model for feature extraction,and finally,COD classification of industrial wastewater is achieved using GRU neural network.The experimental results show that the CNN-GRU classification model converges after 200 times of training,with a classification accuracy of 99.5%.Compared with the long short-term memory method,the GRU method,and the CNN-LSTM method,the classification accuracy of CNN-GRU method has a significant advantage.

industrial wastewater classificationultraviolet-visible spectroscopyGaussian filter denoisingconvolutional neural network-gated recurrent unit model

缪俊锋、汤斌、陈庆、龙邹荣、叶彬强、周彦、张金富、赵明富、周密

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重庆理工大学电气与电子工程学院,重庆 400054

重庆市铜梁区生态环境监测站,重庆 402560

工业废水分类 紫外-可见光谱法 高斯滤波去噪 卷积神经网络-门控循环单元模型

国家自然科学基金重庆市自然科学基金面上项目重庆市教委科学技术研究项目重庆市高校创新研究群体项目重庆市铜梁区科技计划项目

61805029cstc2020jcyjmsxmX0879KJQN202201110CXQT21035CCF20220623

2024

10.3969/j.issn.1673-6141.2024.01.006

大气与环境光学学报
中国科学院安徽光学精密机械研究所

大气与环境光学学报

影响因子：0.285
ISSN：1673-6141
年,卷(期)：2024.19(1)
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