Simultaneous measurement of chemical oxygen demand and turbidity in water based on broad optical spectra using backpropagation neural network

Abstract

How to overcome the measurement interference between chemical oxygen demand (COD) and turbidity in absorption spectra of water has attracted lots of attention recently. Previous studies focused on eliminating the influence of turbidity, which could not measure COD and turbidity simultaneously. In this paper, we proposed a new method based on ultraviolet-visible-near infrared (UV-Vis-NIR) absorption measurements using a backpropagation neural network (BPNN). The BPNN model was established within the Tensorflow framework. And the absorbance of the wavelength ranges of 240–1040 nm and the actual values of COD and turbidity in the training set were defined as input and output, respectively. Then the BPNN model was trained with training sets of different sizes. And the original absorbance in the test set without any compensation was used to test the performance of the trained BPNN, which indicated that the larger the sample size, the better the adjustment effect of the model. The optimal training sample size was 2400. The correlation coefficient of the model is greater than 0.9998, and the RMSE between the predicted value and the actual value of COD and turbidity is 0.19 mg/L and 0.34 NTU, respectively. Our results show that the method based on BPNN not only can eliminate the influence of turbidity on the accuracy of COD but also realize the accurate measurement of turbidity itself. Furthermore, this method provides a potential strategy for the simultaneous and rapid measurement of other water quality parameters by using the absorption spectrum.