Inversion prediction of COD in wastewater based on hyperspectral technology

COD is an important detection index in the field of water environmental treatment. At present, COD detection methods and instruments have shortcomings such as long detection time, complicated detection process and high consumption of chemical agent. In this paper, a rapid detection method for COD index of wastewater based on hyperspectral technology and machine learning is developed, and a complete non-contact detection scheme is formed. In the method, the characteristic spectral data of standard COD in the sample spectral curve are extracted by successive projections algorithm (SPA) and genetic algorithm (GA) to establish the regression model of the index. The model is applied to the rapid detection of COD index in the textile desizing wastewater treatment process, and the accuracy of the model in real environment is comprehensively evaluated by root mean square error (RMSE), relative analysis error (RPD) and determination coefficient (R2). Our results indicate that the model has high prediction stability and strong generalization ability (the RMSE is 40.4489 mg/L, RPD is 9.37, and R2 is 0.97) and is a green method for COD detection in wastewater.