Benchmarking empirical models for THMs formation in drinking water systems: An application for decision support in Barcelona, Spain

Drinking water treatment plants (DWTPs) face changes in raw water quality, which affect the formation of disinfection by-products. Several empirical modelling approaches have been reported in the literature, but most of them have been developed with lab-scale data, which may not be representative of real water systems. Therefore, the application of these models for real-time operation of DWTPs might be limited. At the present study, multiple linear regression (MLR) and multi-layer perceptrons (MLP) were benchmarked using field-scale data for predicting the THMs formation in a case-study DWTP in Barcelona, Spain. After fitting the studied models, MLR exhibited good fit with the validation data set (R2 = 0.88 and MAE = 4.0 μg·L−1) and described the most plausible input-output relationships with field-scale data. The MLR predictive model was incorporated into an environmental decision support system (EDSS) for assessing the THMs formation at two critical points of the distribution network. A Monte Carlo scheme was applied for quantifying uncertainty of model predictions at these points, considering low and high water quality scenarios and different degrees of treatment by an electrodialysis reversal process. The results show that the use of the proposed EDSS can help in real operation of complex drinking water systems, which face important changes in water quality throughout the year.

Additional Info

Search articles

Title

Year

Authors

Laboratory of Chemical and Enviromental Engineering

Institut de Medi Ambient
Universitat de Girona
Campus Montilivi
17003 Girona

Parc Científic i Tecnològic de la UdG
Edifici Jaume Casademont, Porta B
Pic de Peguera, 15
17003 Girona
Tel. +34 972 41 98 59
info@lequia.udg.cat

 

Search

Keyword

Social Media

Follow us on ...

Facebook Twitter Youtube Linkedin

NOTE! This site uses cookies and similar technologies. If you not change browser settings, you agree to it. Cookie Policy