A simulation model analyzes cost effectiveness of remote facility monitoring for risk reduction in rural water supply systems by performing a break-even analysis that compares operating costs with manual and remote monitoring. Water system operating cost includes the value of water loss (i.e., realized risk) resulting from operating excursions which are inversely related to mechanical reliability. Reliability is controlled by facility monitoring that identifies excursions enabling operators to implement mitigating measures. Cost effectiveness refers to the cost relationship among operating alternatives that reveals changed economic conditions at different operating rates inherent in the inverse relationship between fixed and variable costs. Break-even analysis describes cost effectiveness by identifying the operating rate above which the more capital intensive alternative will result in lower operating cost. Evidence indicates that increased monitoring frequency associated with remote monitoring can reduce water system operating cost by improving reliability, but whether remote monitoring is cost effective depends upon system-specific factors. The lack of a documented tool for evaluating this type of cost effectiveness led to the project objective of developing a model that performs break-even analysis by simulating water system operating costs as functions of system size (delivery rate). When the spreadsheet-based static deterministic parametric simulation model is run for the Tazewell County, Virginia water system based upon 1998 data, break even is predicted at approximately fifty-five percent of annual capacity (116,338,000 gallons) with operating cost of $1,043,400. Maximum annual operating cost reduction from a $317,600 investment provides payback in nine years. / Master of Science

In this study, a model is developed to assess risk to a municipal water supply system under the influence of population growth and climate change. To incorporate the uncertainly in water use, a model which combines time series Monte Carlo simulations and a deterministic artificial neural network (ANN) is developed to simulate the daily water demand under climate variability. The model is then expanded in two directions. One direction is to estimate the effects of demand management programs and system expansion on the reliability, resiliency, and vulnerability of the water supply system. Another direction is to capture the possible impacts of climate change on the risk of a water supply system. Twenty-six scenarios generated from different combinations of demand management programs, system expansions and Global Climate Model (GCM) scenarios were set to illustrate the risk indices: reliability, resiliency, and vulnerability. To illustrate the effects of a change of precipitation frequency and a higher population growth, twenty-five additional scenarios were evaluated.

In this study, a model is developed to assess risk to a municipal water supply system under the influence of population growth and climate change. To incorporate the uncertainly in water use, a model which combines time series Monte Carlo simulations and a deterministic artificial neural network (ANN) is developed to simulate the daily water demand under climate variability. The model is then expanded in two directions. One direction is to estimate the effects of demand management programs and system expansion on the reliability, resiliency, and vulnerability of the water supply system. Another direction is to capture the possible impacts of climate change on the risk of a water supply system. Twenty-six scenarios generated from different combinations of demand management programs, system expansions and Global Climate Model (GCM) scenarios were set to illustrate the risk indices: reliability, resiliency, and vulnerability. To illustrate the effects of a change of precipitation frequency and a higher population growth, twenty-five additional scenarios were evaluated.

Hospital-acquired Legionnaires¡¦ Disease (LD) is a bacterial pneumonia caused by the genus of Legionella. It is an opportunistic pathogen with the characteristic of widespread distribution in the environment. Its source of infection associates with potable water systems. Proactively culturing hospital water supply for Legionella as a strategy for prevention of nosocomial LD has been widely adopted in other countries. Nosocomial LDs has been hardly reported in Taiwan. In addition, environmental cultures of Legionella in potable water systems in hospitals have not been systematically implemented. Thus, the purpose of the research is to confirm if LD presents in the hospital in Taiwan, and developing risk management strategy in hospital-acquired LD. To practice one-year prospective surveillance program for LD, we choose a military hospital in Southern Taiwan, collecting the specimens from the nosocomial and community-acquired pneumonia patients for legionella investigations. In the meanwhile, we collect water samples for hospital epidemiological investigation every 3 months. Isolated Legionella pneumophila is serotyped and analyzed by pulsed-field gel electrophoresis. From Nov 1, 2006 to Oct 30, 2007, within 54 cases of nosocomial and 300 cases of community-acquired pneumonia, only one case of nosocomial LD was found. Environmental investigations detected L. pneumophila in 17(20.7%) of the 84 water samples, of which 82.4% (14/17) belonged to serogroup 1. The result demonstrated the infection source of the only positive case of nosocominal pneumonia is the potable water supply system of another hospital. In conclusion: 1. The infection source of nosocomial LD is the potable water supply system of the hospital. 2. The positive rate of distal outlets for L. pneumophila is a reasonable and reliable indicator in risk management for nosocomial LD. 3. Uncovered cases of nosocomial LD will be found in prospective clinical surveillance for LD. Suggestions: 1. Routine water-quality monitoring should be added in environmental water culture for L. pneumophila in the institution, such as hospital, nursing home, hotel, restaurant, SPA, swimming pool, hot spring, school, army, etc. 2. We advise that government health department carries out national surveillance for hospital water environment in determining the risk of hospital-acquired LD. 3. Education and training program need to be provided for medical staffs in the diagnostic skills of nosocomial LD to avoid misdiagnosing and delaying the treatment.

Orientador: Edevar Luvizotto Junior / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Civil, Arquitetura e Urbanismo / Made available in DSpace on 2018-08-11T18:08:54Z (GMT). No. of bitstreams: 1 Silva_NeusaAparecidaSales_M.pdf: 3550514 bytes, checksum: 2963e1b4e84aea2fc6b498b7ef92c3dc (MD5) Previous issue date: 2003 / Resumo: A gestão de uma empresa de abastecimento de água fundamenta-se em um conjunto de dados gerais, sobre os quais se geram informações (dados tratados) através das quais são tomadas decisões de ações efetivas. As informações se traduzem normalmente em indicativos puros, tais como número de consumidores, extensão da rede, volume produzido, volume consumido, etc. As devidas relações destes indicativos ¿puros¿ podem fornecer valiosas informações de diagnostico do sistema. Tais relações são chamadas de ¿guias¿ ou indicadores de gestão. O presente trabalho coletou da literatura (de organizações de reconhecida credibilidade) um conjunto de 254 indicadores de gestão para empresas de abastecimento. Estes indicadores foram submetidos à análise, por meio de questionário apropriado, pelos diversos setores de um grupo de empresas selecionadas. O resultado das análises, fornece indícios do baixo emprego e conhecimento de indicadores de gestão por parte das empresas de abastecimento (a menos dos exigidos por parte de órgãos de financiamento). Também a falta de uma cultura de manutenção de um banco de dados atualizados, a falta de um rigor no trato destas informações, põem em duvida os valores obtidos para os indicadores utilizados como contribuição deste trabalho. Aproveitando o resultado dos questionários o trabalho estabelece um conjunto de 61 indicadores que parece atender em conjunto a todos os entrevistados sendo portanto tomados como o elenco básico de indicadores de gestão / Abstract: The management of a water suply company is based on general data. One conceive information about this data, and with these information one can take decisions of effective actions. The information are translated in pure indicators, such as number of consumers, the net length, produced and cosumed volume, etc. The proper relations between these pure indicators may supply worth information for the diagnosis of the system. Such relations are called ¿Guides¿ or management indicators. This paper has collected from the literature from the organizations that have recognized credibility a set of 254 management indicators for water supply companies. These indicators were analised, by means of proper questionnaire, by several sections from some chosen companies. The result of this analisis, shows that the water supply companies has a low use and knowledge of these management indicators except those that are required by the finance companies. Also the lack of a culture of maintenance of updated data, the lack of a rigor in treat these information, make the values got to the indicators used as a contribuition for this paper doubtful. Using the questionnaires result this paper establish a set of 61 indicators that seems to serve all the interwied people and so they are used as the basic management indicadores / Mestrado / Recursos Hidricos / Mestre em Engenharia Civil

Water scarcity has caused severe impact on the entire ecosphere while the climate change is resulting in high frequency of extreme weather conditions, especially extended period of drought. Due to the even increasing world’s population and the continued societal modernization, water scarcity is now one of the leading global challenges towards the development of human society. On the other hand, atmospheric water, accounting for 6 times the water in all rivers on Earth, is emerging as an alternative water resource. This dissertation thoroughly investigated the fully solar energy driven atmospheric water harvesting (AWH) process in a broad scientific and application context. The light-to-heat conversion process of solar photothermal materials was investigated first with a rationally designed droplet-laser system, which in combination with the calculation of heat of absorption of water vapor for various application scenarios, formed a theoretical basis of this dissertation research. As a result, a series of commonly used hydrated salts and their anhydrous counterparts were judiciously selected and successfully proven to be low-cost AWH materials to generate clean fresh water for arid regions. A hydrogel-deliquescent salt composite was further developed as AWH material with a significantly enhanced fresh water production capacity. A new design of nano-capsule encapsulated deliquescent salt was further put forward to enhance water vapor sorption/desorption kinetics, which enabled, for the first time, multiple sorption/desorption cycles within one day and thus multiplied water production capacity. The first-ever continuous AWH device, as opposed to batch-type one, was rationally designed, fabricated, and successfully tested in field conditions outdoors. At last, the dissertation pioneered a novel concept of atmospheric water sorption and desorption cycle for photovoltaic (PV) panel cooling. This dissertation shines significant light on sorption based atmospheric water harvesting and inspires more research efforts on this important research topic.

Water supply system planning is concerned about the design of water supply infrastructure for distributing water from sources to users. Population growth, economic development and diminishing freshwater supplies are posing growing challenges for water supply system planning in many urban areas. Besides the need to exploit alternative water sources to the conventional surface and groundwater supplies, such as reclaimed water, a systematic point of view has to be taken for the efficient management of all potential water resources, so that issues of water supply, storage, treatment and reuse are not considered separately, but rather in the context of their interactions. The focus of this dissertation is to develop mathematical models and optimization algorithms for water supply system planning, where the interaction of different system components is explicitly considered. A deterministic nonlinear programming model is proposed at first to decide pipe and pump sizes in a regional water supply system for satisfying given potable and non-potable user demands over a certain planning horizon. A branch-and-bound algorithm based on the reformulation-linearization technique is then developed for solving the model to global optimality. To handle uncertainty in the planning process, a stochastic programming (SP) model and a robust optimization (RO) model are successively proposed to deal with random water supply and demand and the risk of facility failure, respectively. Both models attempt to make the decision of building some additional treatment and recharge facilities for recycling wastewater on-the-site. While the objective of the SP model is to minimize the total system design and expected operation cost, the RO model tries to achieve a favorable trade-off between system cost and system robustness, where the system robustness is defined in terms of meeting given user demands against the worst-case failure mode. The Benders decomposition method is then applied for solving both models by exploiting their special structure.

Cyber Physical System (CPS) is a system where cyber and physical components work in a complex co-ordination to provide better performance. By exploiting the communication infrastructure among the sensors, actuators, and control systems, attackers may compromise the security of a CPS. In this dissertation, security measures for different types of attacks/ faults in two CPSs, water supply system (WSS) and smart grid system, are presented. In this context, I also present my study on energy management in Smart Grid. The techniques for detecting attacks/faults in both WSS and Smart grid system adopt Kalman Filter (KF) and χ2 detector. The χ2 -detector can detect myriad of system fault- s/attacks such as Denial of Service (DoS) attack, short term and long term random attacks. However, the study shows that the χ2 -detector is unable to detect the intelligent False Data Injection attack (FDI). To overcome this limitation, I present a Euclidean detector for smart grid which can effectively detect such injection attacks. Along with detecting attack/faults I also present the isolation of the attacked/faulty nodes for smart grid. For isolation the Gen- eralized Observer Scheme (GOS) implementing Kalman Filter is used. As GOS is effective in isolating attacks/faults on a single sensor, it is unable to isolate simultaneous attacks/faults on multiple sensors. To address this issue, an Iterative Observer Scheme (IOS) is presented which is able to detect attack on multiple sensors. Since network is an integral part of the future CPSs, I also present a scheme for pre- serving privacy in the future Internet architecture, namely MobilityFirst architecture. The proposed scheme, called Anonymity in MobilityFirst (AMF), utilizes the three-tiered ap- proach to effectively exploit the inherent properties of MF Network such as Globally Unique Flat Identifier (GUID) and Global Name Resolution Service (GNRS) to provide anonymity to the users. While employing new proposed schemes in exchanging of keys between different tiers of routers to alleviate trust issues, the proposed scheme uses multiple routers in each tier to avoid collaboration amongst the routers in the three tiers to expose the end users.

Energy efficiency is currently a hot topic on all regional, national, and global stages. Accurate measurements on how energy is being used over a period of time can improve performance of the drinking water utility substantially and reduce energy consumption. Nevertheless, the drinking water industry does not have a specific benchmarking practice to evaluate its energy performance of the system. Therefore, there are no standards to compare energy use between water utilities that have a variety of system characteristics. The goal of this research is to develop quantitative approach to select energy benchmarking parameters of the water system, so the drinking water utilities can use those parameters to improve their energy efficiency. In addition to a typical benchmarking of drinking water utilities, the energy benchmarking can specifically compare energy efficiency of a utility with other utilities nationwide. The research developed a regression model based on the statistical representation of the energy use and descriptive characteristics of the drinking water utilities data throughout the U.S. Methodologies to eliminate singularity and multicollinearity from collinear survey dataset are discussed. The all possible regressions were chosen as parameters selection methodology to identify a subset of most significant parameters, i.e. system characteristics, that can mathematically correspond to energy use across different utilities. As a result, the energy benchmarking would be able to calculate the predicted total energy use of the system from given system characteristics. / Master of Science

The integration of Artificial Intelligence (AI) into water supply systems (WSSs) has revolutionized real-time monitoring, automated operational control, and predictive decision-making analytics. However, AI also introduces security vulnerabilities, such as data poisoning. In this context, data poisoning could involve the malicious manipulation of critical data, including water quality parameters, flow rates, and chemical composition levels. The consequences of such threats are significant, potentially jeopardizing public safety and health due to decisions being made based on poisoned data. This thesis aims to exploit these vulnerabilities in data-driven applications within WSSs. Proposing Water Generative Adversarial Networks, H2OGAN, a time-series GAN-based model designed to synthesize water data. H2OGAN produces water data based on the characteristics within the expected constraints of water data cardinality. This generative model serves multiple purposes, including data augmentation, anomaly detection, risk assessment, cost-effectiveness, predictive model optimization, and understanding complex patterns within water systems. Experiments are conducted in AI and Cyber for Water and Agriculture (ACWA) Lab, a cyber-physical water testbed that generates datasets replicating both operational and adversarial scenarios in WSSs. Identifying adversarial scenarios is particularly importance due to their potential to compromise water security. The datasets consist of 10 physical incidents, including normal conditions, sensor anomalies, and malicious attacks. A recurrent neural network (RNN) model, i.e., gated recurrent unit (GRU), is used to classify and capture the temporal dynamics those events. Subsequently, experiments with real-world data from Alexandria Renew Enterprises (AlexRenew), a wastewater treatment plant in Alexandria, Virginia, are conducted to assess the effectiveness of H2OGAN in real-world applications. / Master of Science / Today, a significant portion of the global population struggles with access to essential services: 25% lack clean water, 50% lack sanitation services, and 30% lack hygiene facilities. In response, AI is being leveraged to tackle these deficiencies within water supply systems. Investments in AI are expected to reach an estimated $6.3 billion by 2030, with potential savings of 20% to 30% in operational expenditures by optimizing chemical usage in water treatment. The flexibility and efficiency of AI applications have fueled optimism about their potential to revolutionize water management. As the era of Industry 4.0 progresses, the role of AI in transforming critical infrastructures, including water supply systems, becomes increasingly vital. However, this technological integration brings with it heightened vulnerabilities. The water sector, recognized as one of the 16 critical infrastructures by the Cybersecurity and Infrastructure Security Agency (CISA), has seen a notable increase in cyberattack incidents. These attacks underscore the urgent need for sophisticated AI-driven security solutions to protect these essential systems against potential compromises that could pose significant public health risks. Addressing these challenges, this thesis introduces H2OGAN, a time-series GAN-based model developed to generate and analyze realistic water data within the expected constraints of water parameter characteristics. H2OGAN supports various functions including data augmentation, anomaly detection, risk assessment, and predictive model optimization, thereby enhancing the security and efficiency of water supply systems. Extensive testing is conducted in ACWA Lab, a cyber-physical testbed that replicates both operational and adversarial scenarios. These experiments utilize a RNN model, specifically a GRU, to classify and analyze the dynamics of various scenarios including normal operations, sensor anomalies, and malicious attacks. Further real-world validation is carried out at AlexRenew, a wastewater treatment facility in Alexandria, Virginia, confirming the effectiveness of H2OGAN in practical applications. This research not only advances the understanding of AI in water management but also emphasizes the critical need for robust security measures to protect against the evolving landscape of cyber threats.