Chemometric methods to process online spectrometry for quality monitoring of different water matrices
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Water quality monitoring during the purification and sanitation processes in Drinking Water Treatment Plants (DWTP), Wastewater Treatment Plants (WWTP) and Water Reclamation Plants (ERA) is a necessary step to obtain a wide overview of water treatment processes, to monitor pollutants that are in the spotlight of the public administrations and to provide reliable water quality to the consumers. Obtaining high quality information through sensors and probes installed on line is highly increasing.
The aim of this investigation is to provide the spectro::lyser® probe more capabilities for contaminant detection and prediction, both in drinking and wastewater.
By using the Ultraviolet Visible (UV–Vis) spectrum, coupled with advanced statistical methods and chemical studies different mathematical models were developed. This allows to predict the trihalomethanes formation potential (THM FP) during water sanitation in DWTP, the concentration of a selected hydrocarbons mixture (toluene, m-xylene and p-xylene) in urban wastewaters at the WWTP’s influent, and the concentration of coagulant added during the coagulation-flocculation process in ERA.
The results showed that applying new mathematical models to monitor these kinds of contaminants is mandatory. In the model developed to predict the THM FP, it was concluded that Artificial Neural Networks, ANN, is the algorithm with the greatest capabilities of prediction, giving the best correlation and error results (R2 = 0.92, RMSE = 0.77). On the other hand, in the model developed to predict the presence of a controlled hydrocarbon mixture in wastewater, the Multiple Linear Regression algorithm, MLR, algorithm obtained very good results (R2MLR1 = 0.82; RMSEMLR1 = 0.22; R2MLR2 = 0.87; RMSEMLR2 = 0.21, R2MLR3 = 0.79, RMSEMLR3 = 0.24). To predict the optimal concentration of coagulant to add during the coagulation-flocculation process ANN gave the best results (R2 = 0.86, RSE = 0.02).
The developed algorithms are specific to each particular Water Treatment Plant studied, and can be used as a tool to provide a quick and efficient response when necessary
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