Using Artificial Neural Network to Assess Chlorine in Supply Systems

A water distribution system (WDS) is based in a network of interconnected hydraulic components to transport the water directly to the customers. Water must be treated in a Water Treatment Plant (WTP) to provide safe drinking water to consumers, free from pathogenic and other undesirable organisms. The disinfection is an important aspect in achieving safe drinking water and preventing the spread of waterborne diseases. Chlorine is the most commonly used disinfectant in conventional water treatment processes because of its low cost, its capacity to deactivate bacteria, and because it ensures residual concentrations in WDS to prevent microbiological contamination. Chlorine is measured at the output of the WTP and also in several considered points within the WDS to control the water quality in the system. Simulation and modeling methods help to predict in an effective way the chlorine concentration in the WDS. The purpose of the book is to assess chlorine concentration in some strategic points within the WDS by using the historical measured data of some water quality parameters that influence chlorine.

Autorentext

Born in Fonseca, La Guajira, Colombia, Gustavo Andres Cuesta is a PhD researcher who successfully finalized his thesis of Using Artificial Neural Network Models to Assess Water Quality in Water Distribution Networks in Brno University of Technology, Czech Republic.