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.
关键词
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
Key words
chemical oxygen demand(COD)/quantitative prediction model/near-infrared spectroscopy/water environment/real time monitoring/one-dimensional convolutional neural network
维普
万方数据