| Summary: | Main objective of this research is to optimize booster chlorination to ensure high quality water in the distribution network (DN). Focus of this research is primarily small to medium sized DNs that lack continuous monitoring, where chlorination is the predominant disinfection practice to maintain acceptable drinking water quality. In this research, new methods and strategies have been proposed to help in selecting location and dosages for booster chlorination, which protects against microbiological contamination and biofilm growth but also limit formation of harmful disinfectant by-products (DBPs), and chlorine related taste & odors issues.
This research developed index and risk based schemes to optimize water quality in DNs. Three indices have been proposed: 1) non-compliance potential index (NCP index), 2) modified Canadian Council of Ministries of the Environment Water Quality index (Modified CCME WQI), and 3) intrusion risk potential (IRP). NCP index has been developed to evaluate regulatory violations of DBPs using Bayesian Belief Network. The modified CCME WQI, an extension of commonly accepted CCME WQI, has been developed to evaluate Stage 1 and Stage 2 DBP rules. Risk based index (IRP) has been developed to identify potential intrusion points based on pollutant source, water main characteristics, soil properties, operational and land use factors and population served. Three optimization schemes and algorithms have been proposed to determine the number of booster locations and corresponding dosage levels. Modified CCME WQI has been used to select optimal dosage for booster chlorination using response surface optimization. It uses temporal series data for free residual chlorine (FRC) and converts into an index by maximizing water quality. Later another optimization algorithm has been developed to locate booster stations using FRC and total trihalomethane time series data. This algorithm is called maximum covering location problem, which has been developed using EPANET–MSX programmer’s toolkit integrated with Matlab coding. Third optimization algorithm has been developed to minimize the impacts of contaminant intrusion using booster chlorination. This scheme uses multi-objective genetic algorithm to select both location and dosages for booster chlorination. Proposed methods and strategies have been demonstrated using case studies on City of Kelowna and Quebec City DNs. === Applied Science, Faculty of === Engineering, School of (Okanagan) === Graduate
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