Abstract
Chlorine dioxide (ClO2) is a widely used sterilizer and a disinfectant across a multitude of industries. When using ClO2, it is imperative to measure the ClO2 concentration to abide by the safety regulations. This study presents a novel, soft sensor method based on Fourier transform infrared spectroscopy (FTIR) spectroscopy for measurement of ClO2 concentration in different water samples varying from milli Q to wastewater. Six distinct artificial neural network models were constructed and evaluated based on three overarching statistical standards to select the optimal model. The OPLS-RF model outperformed all other models with R2, RMSE, and NRMSE values of 0.945, 0.24, and 0.063, respectively. The developed model demonstrated limit of detection and limit of quantification values of 0.1 and 0.25 ppm, respectively, for water. Furthermore, the model also exhibited good reproducibility and precision as measured by the BCMSEP (0.064). The soft sensor-based method presented in the study offers significant advantages in terms of simplicity and speedy detection. In summary, the study presents development of a soft sensor that is capable of predicting the trace content of chlorine dioxide ranging between 0.1 to 5 ppm in a water sample by connecting FTIR with an OPLS-RF model.
Published Version
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