The presented thesis investigates the identification of burst locations in water distribution systems (WDS) by analysis of field and simulation experimental data. This required the development of a new hybrid method of burst detection and sizing, and also a burst location identification algorithm. Generally, existing practice relies on a combination of some simple procedure and experience of the involved staff and cannot be easily automated. The practical methods are based on direct manifestation of burst on the surface or on systematically surveying suspected areas e.g. by using listening sticks, such methods are very time consuming. The proposed burst location algorithm is based on comparing data by means of statistical analysis of field data with results of water network simulation. An extended network hydraulic simulator is used to model pressure dependent leakage terms. The presence of a burst changes the flow pattern and also pressure at network nodes which may be used to estimate the burst size and its location. The influence of such random factors as demand flows and background leakage on the process of burst detection is also considered. The field data is from a generalised fixed area and variable area (FAVOR) test where inlet pressure is being stepped up and down and the following variables are measured: inlet flow, inlet pressure (head) and pressure at a number of selected sensitive nodes. The method has three stages and uses two different models, one is inlet flow model (IFM) to represent the total inlet flow and another is the extended hydraulic model to simulate different burst locations. Initially the presence of a potential burst is investigated. If this is confirmed precise values of the demand, background leakage flow and burst flow in IFM are subsequently estimated. They are used to identify the burst site at the third stage of the method. The method can be easily adapted for practical use. It requires data from experiments carried out at night between 1am and 5am and involves placing typically about 20 temporary loggers to collect the measurements during this period. It also requires the availability of a hydraulic model which normally is in the possession of a water company. The program has been implemented in the Matlab package and is easy to use. The current methodology is tuned to identify a single burst but can be generalised to identify locations of multiple bursts.

Previous efforts to apply risk analysis for water distribution systems (WDS) have not typically included explicit hydraulic simulations in their methodologies. A risk classification scheme is here employed for identifying vulnerable WDS components subject to an intentional water contamination event. A Monte Carlo simulation is conducted including uncertain stochastic diurnal demand patterns, seasonal demand, initial storage tank levels, time of day of contamination initiation, duration of contamination event, and contaminant quantity. An investigation is conducted on exposure sensitivities to the stochastic inputs and on mitigation measures for contaminant exposure reduction. Mitigation measures include topological modifications to the existing pipe network, valve installation, and an emergency purging system. Findings show that reasonable uncertainties in model inputs produce high variability in exposure levels. It is also shown that exposure level distributions experience noticeable sensitivities to population clusters within the contaminant spread area. The significant uncertainty in exposure patterns leads to greater resources needed for more effective mitigation.

Threat management of water distribution systems is essential for protecting consumers. In a contamination event, different strategies may be implemented to protect public health, including flushing the system through opening hydrants or isolating the contaminant by manipulating valves. To select the most effective options for responding to a contamination threat, the location and loading profile of the source of the contaminant should be considered. These characteristics can be identified by utilizing water quality data from sensors that have been strategically placed in a water distribution system. A simulation-optimization approach is described here to solve the inverse problem of source characterization, by coupling an evolutionary computation-based search with a water distribution system model. The solution of this problem may reveal, however, that a set of non-unique sources exists, where sources with significantly different locations and loading patterns produce similar concentration profiles at sensors. The problem of non-uniqueness should be addressed to prevent the misidentification of a contaminant source and improve response planning. This paper aims to address the problem of non-uniqueness through the use of Niched Co-Evolution Strategies (NCES). NCES is an evolutionary algorithm designed to identify a specified number of alternative solutions that are maximally different in their decision vectors, which are source characteristics for the water distribution problem. NCES is applied to determine the extent of non-uniqueness in source characterization for a virtual city, Mesopolis, with a population of approximately 150,000 residents. Results indicate that NCES successfully identifies non-uniqueness in source characterization and provides alternative sources of contamination. The solutions found by NCES assist in making decisions about response actions. Once alternative sources are identified, each source can be modeled to determine where the vulnerable areas of the system are, indicating the areas where response actions should be implemented.

This study compared four different water main failure prediction models: a statistically simple model, a statistically complex model, a statistically complex model with modifications termed the 2019 model, and an age-based model. The statistically complex models compute the probability of failure based on age, size, internal pressure, length of pipe in corrosive soil, land use, and material of the. These two values are then used to prioritize a water main rehabilitation program to effectively use the municipality’s funds. The 2019 model calculates the probability of failure and consequence of failure differently than the statistically complex model by considering corrosive soil data instead of assuming all the pipes are in highly corrosive soil and average daily traffic volume data instead of using street classifications. The statistically simple model only uses the pipe age and material for probability of failure. The age-based model relies purely on the age of the pipe to determine its probability of failure. Consequences of failure are determined by the proximity of the pipe to highly trafficked streets, critical services, pipe replacement cost, and the flow capacity of the pipe. Risk of failure score is the product of the consequence of failure score and probability of failure score. Pipes are then ranked based on risk of failure scores to allow municipalities to determine their pipe rehabilitation schedule. The results showed that the statistically complex models were preferred because results varied between all four models. The 2019 model is preferred for long-term analysis because it can better account for future traffic growth using the average daily traffic volume. Corrosive soil data did not have a significant impact on the results, which can be attributed to the relatively small regression parameter for corrosive soil. The age-based model is not recommended because results of this study shows it places a significantly high number of pipes in the high and critical risk categories compared to the other models that account for more factors. This could result in the unnecessary replacement of pipes leading to an inefficient allocation of funds. Keywords: Risk of Failure, Consequence of Failure, Probability of Failure

Failure of pipes in water distribution systems is a common occurrence especially in large cities. The failure of a pipe results in: loss of water; property damage; interruption of service; decreased system performance; and the financial cost of restoring the failed pipe. The cost of replacement and rehabilitation in the United States is estimated at 23 plus billion dollars. It is virtually impossible to replace all vulnerable pipes at the same time. As a result, there is a need for methods that can help in progressive system rehabilitation and replacement subject to budgetary constraints. If delaying is considered a good strategy due to the time value of money then, the timing of preventive maintenance becomes a crucial element for system maintenance and operation. The central under pinning element in the decision process for scheduling preventive maintenance is the deteriorating nature of a pipe under a given surrounding. By planning to replace pipes before they fail, proper planning can be put in place for securing of finances and labor force needed to rehabilitate the pipes. With this approach, service interruptions are minimized as the loss of service time is limited to the time used in replacing the pipe. In this research, a mechanistic model for assessing the stage of deterioration of an underground pipe is developed. The developed model consists of three sub-models namely, the Pipe Load Model (PLM), the Pipe Deterioration Model (PDM), and the Pipe Break Model (PBM). The PLM simulates the loads and stresses exerted on a buried water main. These loads include the earth load, traffic load, internal pressure, expansive soil loads, thermal, and frost loads. The PDM simulates the deterioration of the pipe due to corrosion resulting from the physical characteristics of the pipe environment. The pipe deterioration effect is modeled in two stages. First, the thinning of the pipe wall is modeled using a corrosion model. Second, the localized pit growth is used to determine the residual strength of the pipe based on the fracture toughness and the initial design strength of the pipe. The PBM assesses the vulnerability of a pipe at any time in terms of a critical safety factor. The safety factor is defined as the ratio of residual strength to applied stress. For a conservative estimate the multiplier effect due to thermal and frost loads are considered. For a chosen analysis period, say 50 years, the pipes with safety factors less than the critical safety factor are selected and ordered by their rank. Aided by the prioritized list of failure prone pipes, utilities can organize a replacement schedule that minimizes cost over time. Additionally a physically based regression model for determining the optimal replacement time of pipe is also presented. A methodology for assessing the consequences of accelerated and delayed replacement is also provided. The methodologies developed in this dissertation will enable utilities to formulate future budgetary needs compatible with the intended level of service. An application of the model and results are included in the dissertation. / Ph. D.

The main aim of the study was to analyse the impact of physico-chemical parameters on drinking water quality, biofilm formation and antibiotic resistant bacteria in the drinking water distribution system in Mafikeng, North West Province, South Africa. Another objective was to isolate and characterise Pseudomonas and Aeromonas species from drinking water distribution system and detect the virulence gene determinants in the isolates by PCR analysis. The physico-chemical data obtained were subjected to statistical analysis using Excel 2007 (Microsoft) and SPSS (version 14.0) programmes. Pearson’s correlation product of the moment was used to determine the correlation between EC, TDS, pH and temperature. The two tailed test of significance (p<0.05) was used in order to determine the significance of the result. Antibiotic susceptibility tests were performed using Kirby-Bauer disk diffusion method. Cluster analysis based on the antibiotic inhibition zone diameter data of different organisms isolated from different sites was determined and was expressed as dendograms using Wards algorithm and Euclidean distance of Statistica version 7. Specific PCR was used to determine the identities of presumptive Pseudomonas and Aeromonas species through amplification of the gyrB, toxA and the ecfX gene fragments. Virulence gene determinants for the confirmed Pseudomonas and Aeromonas species were detected by amplifying the exoA, exoS and exoT genes and the aerA and hylH gene fragments, respectively. A Gene Genius Bio imaging system (Syngene, Synoptics; UK) was used to capture the image using GeneSnap (version 3.07.01) software (Syngene, Synoptics; UK) to determine the relative size of amplicons. Physico-chemical parameters were monitored from three drinking water sources three times a week and bacteriological quality was monitored weekly for four months from raw and treated drinking water. Water samples were analysed for pH, temperature, total dissolved solids (TDS) and electric conductivity (EC). Bacterial consortia from drinking water samples were isolated using selective media and enumerated. The results revealed a good chemical quality of water. However, the microbial quality of the water is not acceptable for human consumption due to the presence of Pseudomonas, Aeromonas, faecal coliforms (FC), total coliforms (TC) and Heterotrophic bacteria. The results showed that the drinking water is slightly alkaline with pH value ranging between7.7 to 8.32. What is of concern was the microbial quality of the water. Pseudomonas sp., faecal coliforms (FC), total coliforms (TC) and heterotrophic bacteria were present in some of the treated water samples. The most significant finding of this study is that all drinking water samples were positive for Pseudomonas sp.(>100/100ml), but also that when one considers the TDS it demonstrates that water from the Modimola Dam has an impact on the quality of the mixed water. The prevalence and antibiotic resistance profiles of planktonic and biofilm bacteria isolated from drinking water were determined. The susceptibility of these isolates was tested against 11 antibiotics of clinical interest and the multiple antibiotic resistance (MAR) patterns were compiled. The most prevalent antibiotic resistance phenotype observed was KF-AP-C-E-OT-K-TM-A. All isolates from all samples were susceptible to ciprofloxacin. However, all faecal coliforms and Pseudomonas spp. were susceptible to neomycin and streptomycin. On the contrary all organisms tested were resistant to erythromycin (100%) trimethoprim and amoxycillin. Cluster analysis based on inhibition zone diameter data could not differentiate the various isolated into sample types. The highest prevalence of antibiotic resistant isolates was observed in Modimola Dam and Molopo eye. Biofilms were investigated in both raw water and treated drinking water sources for the presence of faecal coliforms, total coliforms, Pseudomonas spp., Aeromonas spp. and heterotrophic bacteria based on conventional microbiology and molecular methods. Drinking water biofilms were grown twice and the biofilm developing device containing copper and galvanized steel coupons were utilized. The Mini Tap filter, a home water treatment device which can be used at a single faucet, under constant flow was used during the second collection of treated water samples from cold water taps. Scanning electron micrograph revealed the existence of biofilms in all the sites investigated and the highest density was obtained on galvanized steel coupons. Isolates were tested against the antibiotics ampicillin (10μg), cephalothin (5μg), streptomycin (10μg), erythromycin (15μg), chloramphenicol (30μg), neomycin (30 μg), amoxycillin (10 μg), ciprofloxacin (5 μg), trimethoprim (25μg), kanamycin (30μg), and oxytetracycline (30μg). The multiple antibiotic resistance profiles and the presence of virulence related genes were determined. Various types of drug resistance and presence of virulence genes were observed. The most prevalent resistance phenotype observed was KF-AP-C-E-OT-TM-A. In conclusion, the results indicated the occurrence of faecal indicator bacteria in the drinking water destined for human consumption. Faecal indicator bacteria are the major contributors of poor drinking water quality and may harbour opportunistic pathogens. This highlighted survival of organisms to treatment procedures and the possible regrowth as biofilms in plumbing materials. The detection of large proportion of MAR Aeromonas and Pseudomonas species which possessed virulent genes was a cause of concern as these could pose health risks to humans. The data obtained herein may be useful in assessing the health risks associated with the consumption of contaminated water. / PhD (Microbiology), North-West University, Potchefstroom Campus, 2014

The main aim of the study was to analyse the impact of physico-chemical parameters on drinking water quality, biofilm formation and antibiotic resistant bacteria in the drinking water distribution system in Mafikeng, North West Province, South Africa. Another objective was to isolate and characterise Pseudomonas and Aeromonas species from drinking water distribution system and detect the virulence gene determinants in the isolates by PCR analysis. The physico-chemical data obtained were subjected to statistical analysis using Excel 2007 (Microsoft) and SPSS (version 14.0) programmes. Pearson’s correlation product of the moment was used to determine the correlation between EC, TDS, pH and temperature. The two tailed test of significance (p<0.05) was used in order to determine the significance of the result. Antibiotic susceptibility tests were performed using Kirby-Bauer disk diffusion method. Cluster analysis based on the antibiotic inhibition zone diameter data of different organisms isolated from different sites was determined and was expressed as dendograms using Wards algorithm and Euclidean distance of Statistica version 7. Specific PCR was used to determine the identities of presumptive Pseudomonas and Aeromonas species through amplification of the gyrB, toxA and the ecfX gene fragments. Virulence gene determinants for the confirmed Pseudomonas and Aeromonas species were detected by amplifying the exoA, exoS and exoT genes and the aerA and hylH gene fragments, respectively. A Gene Genius Bio imaging system (Syngene, Synoptics; UK) was used to capture the image using GeneSnap (version 3.07.01) software (Syngene, Synoptics; UK) to determine the relative size of amplicons. Physico-chemical parameters were monitored from three drinking water sources three times a week and bacteriological quality was monitored weekly for four months from raw and treated drinking water. Water samples were analysed for pH, temperature, total dissolved solids (TDS) and electric conductivity (EC). Bacterial consortia from drinking water samples were isolated using selective media and enumerated. The results revealed a good chemical quality of water. However, the microbial quality of the water is not acceptable for human consumption due to the presence of Pseudomonas, Aeromonas, faecal coliforms (FC), total coliforms (TC) and Heterotrophic bacteria. The results showed that the drinking water is slightly alkaline with pH value ranging between7.7 to 8.32. What is of concern was the microbial quality of the water. Pseudomonas sp., faecal coliforms (FC), total coliforms (TC) and heterotrophic bacteria were present in some of the treated water samples. The most significant finding of this study is that all drinking water samples were positive for Pseudomonas sp.(>100/100ml), but also that when one considers the TDS it demonstrates that water from the Modimola Dam has an impact on the quality of the mixed water. The prevalence and antibiotic resistance profiles of planktonic and biofilm bacteria isolated from drinking water were determined. The susceptibility of these isolates was tested against 11 antibiotics of clinical interest and the multiple antibiotic resistance (MAR) patterns were compiled. The most prevalent antibiotic resistance phenotype observed was KF-AP-C-E-OT-K-TM-A. All isolates from all samples were susceptible to ciprofloxacin. However, all faecal coliforms and Pseudomonas spp. were susceptible to neomycin and streptomycin. On the contrary all organisms tested were resistant to erythromycin (100%) trimethoprim and amoxycillin. Cluster analysis based on inhibition zone diameter data could not differentiate the various isolated into sample types. The highest prevalence of antibiotic resistant isolates was observed in Modimola Dam and Molopo eye. Biofilms were investigated in both raw water and treated drinking water sources for the presence of faecal coliforms, total coliforms, Pseudomonas spp., Aeromonas spp. and heterotrophic bacteria based on conventional microbiology and molecular methods. Drinking water biofilms were grown twice and the biofilm developing device containing copper and galvanized steel coupons were utilized. The Mini Tap filter, a home water treatment device which can be used at a single faucet, under constant flow was used during the second collection of treated water samples from cold water taps. Scanning electron micrograph revealed the existence of biofilms in all the sites investigated and the highest density was obtained on galvanized steel coupons. Isolates were tested against the antibiotics ampicillin (10μg), cephalothin (5μg), streptomycin (10μg), erythromycin (15μg), chloramphenicol (30μg), neomycin (30 μg), amoxycillin (10 μg), ciprofloxacin (5 μg), trimethoprim (25μg), kanamycin (30μg), and oxytetracycline (30μg). The multiple antibiotic resistance profiles and the presence of virulence related genes were determined. Various types of drug resistance and presence of virulence genes were observed. The most prevalent resistance phenotype observed was KF-AP-C-E-OT-TM-A. In conclusion, the results indicated the occurrence of faecal indicator bacteria in the drinking water destined for human consumption. Faecal indicator bacteria are the major contributors of poor drinking water quality and may harbour opportunistic pathogens. This highlighted survival of organisms to treatment procedures and the possible regrowth as biofilms in plumbing materials. The detection of large proportion of MAR Aeromonas and Pseudomonas species which possessed virulent genes was a cause of concern as these could pose health risks to humans. The data obtained herein may be useful in assessing the health risks associated with the consumption of contaminated water. / PhD (Microbiology), North-West University, Potchefstroom Campus, 2014

Water distribution system utilities must be able to maintain a system’s assets (i.e., pumps, tanks, water mains, etc.) in good working condition in order to provide adequate water quantity and quality to its customers. Various asset management approaches are employed by utilities in order to make optimal decisions regarding the renewal of system components. Part of a good asset management approach is performing a comprehensive risk analysis which consists of considering all potential ways in which the system may fail, the likelihood failure of for each scenario, and the consequences of said failure. This study investigates a water distribution system’s risk of failure due to both acute transient events (e.g., pump trip) and standard pressure fluctuations due to daily system operations. Such an analysis may be useful in optimal decision making such as asset monitoring, scheduling of condition assessments or system renewal projects, policy implementation, and investment priorities in order to keep the utility’s total costs at a minimum. It may also be useful as a precautionary measure to help prevent catastrophic failures such as large main blowouts for which the utility would incur substantial costs, both direct and indirect. As part of this thesis, a database of water distribution system models is used to analyze the effects of an acute transient event for different system configurations. The database was created at the University of Kentucky and has been made available to the research community to test newly developed algorithms for various studies including optimal system operations and optimal system design.

The water industry is a heavy consumer of electricity to pump water. Electricity generated with fossil fuel sources produce greenhouse gas (GHG) emissions that contribute to climate change. Carbon taxation and economic discounting in project planning are promising policies to reduce GHG emissions. The aim of this research is to develop novel single- and multi-objective optimization frameworks that incorporate a new gene-coding scheme and pipe ageing models (pipe roughness growth model, a pipe leakage model, and a pipe break model) to examine the impacts of a carbon tax and low discount rates on energy use, GHG emissions, and design/operation/rehabilitation decisions in water systems. Chapter 3 presents a new algorithm that optimizes the operation of pumps and reservoirs in water transmission systems. The algorithm was applied to the KamalSaleh transmission system near Arak, Iran. The results suggest that a carbon tax combined with a low discount rate produces small reductions in energy use and GHG emissions linked to pumping given the high static head of the KamalSaleh system. Chapter 4 presents a new algorithm that optimizes the design and expansion of water distribution networks. The algorithm was applied to the real-world Fairfield water network in Amherstview, Ontario, Canada. The results suggest that a carbon tax combined with a low discount rate does not significantly decrease energy use and GHG emissions because the Fairfield system had adequate installed hydraulic capacity. Chapters 5 and 6 present a new algorithm that optimizes the optimal rehabilitation type and timing of water mains in water distribution networks. In Chapter 5, the algorithm is applied to the Fairfield network to examine the impact of asset management strategies (quantity and infrastructure adjacency discounts) on system costs. The results suggest that applying discounts decreased capital and operational costs and favored pipe lining over pipe replacement and duplication. In Chapter 6, the water main rehabilitation optimization algorithm is applied to the Fairfield network to examine the impact of a carbon tax and low discount rates on energy use and GHG emissions. The results suggest that adopting a low discount rate and levying a carbon tax had a small impact in reducing energy use and GHG emissions and a significant impact in reducing leakage and pipe breaks in the Fairfield system. Further, a low discount rate and a carbon tax encouraged early investment in water main rehabilitation to reduce continuing leakage, pipe repair, energy, and GHG costs. / Thesis (Ph.D, Civil Engineering) -- Queen's University, 2013-04-21 13:58:08.302

The operational management of Water Distribution Systems (WDS), particularly under failure conditions when the behaviour of a WDS is not well understood, is a challenging problem. The research presented in this thesis describes the development of a methodology for risk-based diagnostics of failures in WDS and its application in a near real-time Decision Support System (DSS) for WDS’ operation. In this thesis, the use of evidential reasoning to estimate the likely location of a burst pipe within a WDS by combining outputs of several models is investigated. A novel Dempster-Shafer model is developed, which fuses evidence provided by a pipe burst prediction model, a customer contact model and a hydraulic model to increase confidence in correctly locating a burst pipe. A new impact model, based on a pressure driven hydraulic solver coupled with a Geographic Information System (GIS) to capture the adverse effects of failures from an operational perspective, is created. A set of Key Performance Indicators used to quantify impact, are aggregated according to the preferences of a Decision Maker (DM) using the Multi-Attribute Value Theory. The potential of distributed computing to deliver a near real-time performance of computationally expensive impact assessment is explored. A novel methodology to prioritise alarms (i.e., detected abnormal flow events) in a WDS is proposed. The relative significance of an alarm is expressed using a measure of an overall risk represented by a set of all potential incidents (e.g., pipe bursts), which might have caused it. The DM’s attitude towards risk is taken into account during the aggregation process. The implementation of the main constituents of the proposed risk-based pipe burst diagnostics methodology, which forms a key component of the aforementioned DSS prototype, are tested on a number of real life and semi-real case studies. The methodology has the potential to enable more informed decisions to be made in the near real-time failure management in WDS.