首页|近红外光谱的水体污染指标COD定量预测模型

近红外光谱的水体污染指标COD定量预测模型

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针对传统化学需氧量(chemical oxygen demand,COD)检测存在检测成本高、耗时、易造成二次污染,以及现有检测模型泛化性较差等不足,难以满足水环境实时监测需求的问题,提出基于近红外光谱技术的COD快速无损定量预测模型.实验结果表明,本模型在污水COD光谱数据集上的预测性能,相较于传统机器学习算法和现有其他深度学习算法更优.测试的决定系数(R2)和均方根误差(ERMS)分别达到0.992 1和27.47 mg·L-1,模型卷积层的输出特征可解释性强,能有效表征关键波长点.该预测模型为实际水体COD含量快速检测提供一种新的方法.
Quantitative prediction model of COD water pollution index based on near-infrared spectroscopy
The traditional chemical oxygen demand(COD)detection method is known for its high cost,time-consuming process,and potential for secondary pollution.Moreover,existing detection models often lack generalization,making it difficult to meet the demands of real-time water environ-ment monitoring.In this study,we propose a rapid and non-destructive quantitative prediction model for COD based on near-infrared spectroscopy.Experimental results show that the prediction perform-ance of this model on the sewage COD spectrum dataset is better than that of traditional machine learn-ing algorithms and other existing deep learning algorithms.The model achieved a high coefficient of determination of 0.992 1 and a low root mean square error of 27.47 mg·L-1.The output features of the model's convolutional layer are interpretable and effectively capture the key wavelength points.This research provides a new method for the rapid detection of COD in practical water samples.

chemical oxygen demand(COD)quantitative prediction modelnear-infrared spectroscopywater environmentreal time monitoringone-dimensional convolutional neural network

范日高、王武、郑芝芳、柴琴琴

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福州大学电气工程与自动化学院,福建福州 350108

广电计量检测(福州)有限公司,福建福州 350003

化学需氧量 定量预测模型 近红外光谱 水环境 实时监测 一维卷积神经网络

福建省自然科学基金资助项目福州市科技重大资助项目

2021J016362021ZD282

2024

10.7631/issn.1000-2243.23013

福州大学学报(自然科学版)
福州大学

福州大学学报(自然科学版)

CSTPCD北大核心
影响因子：0.35
ISSN：1000-2243
年,卷(期)：2024.52(2)
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