Application of back-propagation neural network modeling for free residual chlorine, total trihalomethanes and trihalomethanes speciation

Abstract

The application of back-propagation neural networks (BPNNs) was assessed for modeling residual chlorine decay, total THM concentrations (TTHMs), and three individual THM species (CHCl3, CHBrCl2, and CHBr2Cl) in water that was chlorinated under laboratory-scale conditions. Data for modeling chlorine decay and TTHM were generated in chlorination experiments carried out with water collected in water utilities of the Quebec City region, whereas data for THM species were provided by the US Geological Survey for the Mississippi River and its tributaries. The BPNN models were compared with conventional models developed with exactly the same data. Results showed that the ability of BPNN to model residual chlorine decay is, in general terms, comparable to the ability of kinetic first- and second-order models. For TTHMs and THM speciation, however, the ability of BPNN was clearly higher in comparison with multivariate regression models, in particular when brominated disinfection by-products (DBPs) (CHBrCl2 and CHBr2Cl) were modeled. The successful application of BPNN presented in this study opens the door to other potential applications of BPNN for field-scale data concerning THMs as well as for other relevant disinfection by-products. Key words: back-propagation neural networks, drinking water, chlorination, residual chlorine decay, trihalomethanes, prediction.