Developing Multi-Scale Models for Water Quality Management in Drinking Water Distribution Systems

Drinking water supply systems belong to the group of critical infrastructure systems that support the socioeconomic development of our modern societies. In addition, drinking water infrastructure plays a key role in the protection of public health by providing a common access to clean and safe water for all our municipal, industrial, and firefighting purposes. Yet, in the United States, much of our national water infrastructure is now approaching the end of its useful life while investments in its replacement and rehabilitation have been consistently inadequate. Furthermore, the aging water infrastructure has often been operated empirically, and the embracement of modern technologies in infrastructure monitoring and management has been limited. Deterioration of the water infrastructure and poor water quality management practices both have serious impacts on public health due to the increased likelihood of contamination events and waterborne disease outbreaks. Water quality reaching the consumers’ taps is largely dependent on a group of physical, chemical, and biological interactions that take place as the water transports through the pipes of the distribution system and inside premise plumbing. These interactions include the decay of disinfectant residuals, the formation of disinfection by-products (DBPs), the corrosion of pipe materials, and the growth and accumulation of microbial species. In addition, the highly dynamic nature of the system’s hydraulics adds another layer of complexity as they control the fate and transport of the various constituents. On the other hand, the huge scale of water distribution systems contributes dramatically to this deterioration mainly due to the long transport times between treatment and consumption points. Hence, utilities face a considerable challenge to efficiently manage the water quality in their aging distribution systems, and to stay in compliance with all regulatory standards. By integrating on-line monitoring with real-time simulation and control, smart water networks offer a promising paradigm shift to the way utilities manage water quality in their systems. Yet, multiple scientific gaps and engineering challenges still stand in the way towards the successful implementation of such advanced systems. In general, a fundamental understanding of the different physical, chemical, and biological processes that control the water quality is a crucial first step towards developing useful modeling tools. Furthermore, water quality models need to be accurate; to properly simulate the concentrations of the different constituents at the points of consumption, and fast; to allow their implementation in real-time optimization algorithms that sample different operational scenarios in real-time. On-line water quality monitoring tools need be both reliable and inexpensive to enable the ubiquitous surveillance of the system at all times. The main objective of this dissertation is to create advanced computational tools for water quality management in water distribution systems through the development and application of a multi-scale modeling framework. Since the above-mentioned interactions take place at different length and time scales, this work aims at developing computational models that are capable of providing the best description of each of the processes of interest by properly simulating each of its underlying phenomena at its appropriate scale of resolution. Molecular scale modeling using tools of ab-initio quantum chemical calculations and molecular dynamics simulations is employed to provide detailed descriptions of the chemical reactions happening at the atomistic level with the aim of investigating reaction mechanisms and developing novel materials for environmental sensing. Continuum scale reactive-transport models are developed for simulating the spatial and temporal distributions of the different compounds at the pipe level considering the effects of the dynamic hydraulics in the system driven by the spatiotemporal variability in water demands. System scale models are designed to optimize the operation of the different elements of the system by performing large-scale simulations coupled with optimization algorithms to identify the optimal operational strategies as a basis for accurate decision-making and superior water quality management. In conclusion, the computational models developed in this study can either be implemented as stand-alone tools for simulating the fundamental processes dictating the water quality at different scales of resolution, or be integrated into a unified framework in which information from the small scale models are propagated into the larger scale models to render a high fidelity representation of these processes

Battle of the Attack Detection Algorithms:Disclosing cyber attacks on water distribution networks

The BATtle of the Attack Detection ALgorithms (BATADAL) is the most recent competition on planning and management of water networks undertaken within the Water Distribution Systems Analysis Symposium. The goal of the battle was to compare the performance of algorithms for the detection of cyber-physical attacks, whose frequency increased in the past few years along with the adoption of smart water technologies. The design challenge was set for C-Town network, a real-world, medium-sized water distribution system operated through Programmable Logic Controllers and a Supervisory Control And Data Acquisition (SCADA) system. Participants were provided with datasets containing (simulated) SCADA observations, and challenged with the design of an attack detection algorithm. The effectiveness of all submitted algorithms was evaluated in terms of time-to-detection and classification accuracy. Seven teams participated in the battle and proposed a variety of successful approaches leveraging data analysis, model-based detection mechanisms, and rule checking. Results were presented at the Water Distribution Systems Analysis Symposium (World Environmental & Water Resources Congress), in Sacramento, on May 21-25, 2017. This paper summarizes the BATADAL problem, proposed algorithms, results, and future research directions

Modeling Soluble and Particulate Lead Release into Drinking Water from Full and Partially Replaced Lead Service Lines

Partial replacement of lead service lines (LSLs) often results in the excessive long-term release of lead particulates due to the disturbance of pipe scale and galvanic corrosion. In this study, a modeling approach to simulate the release and transport of particulate and dissolved lead from full and partially replaced LSLs is developed. A mass-transfer model is coupled with a stochastic residential water demand generator to investigate the effect of normal household usage flow patterns on lead exposure. The model is calibrated by comparing simulation results against experimental measurements from pilot-scale setups where lead release under different flow rates and water chemistry scenarios was reported. Applying the model within a Monte Carlo simulation framework, partial replacement of the LSL was predicted to result in releasing spikes with significantly high concentrations of particulate lead (1011.9 ± 290.3 μg/L) that were five times higher than those released from the simulated full LSL. Sensitivity analysis revealed that the intensity of flow demands significantly affects particulate lead release, while dissolved lead levels are more dependent on the lengths of the stagnation periods. Preflushing of the LSL prior to regulatory sampling was found to underestimate the maximum monthly exposure to dissolved lead by 19%, while sampling at low flow rates (<5.2 LPM) was found to consistently suppress the high spikes induced by particulate lead mobilization

Spatiotemporal Distribution of Indoor Particulate Matter Concentration with a Low-Cost Sensor Network

Real-time measurement of particulate matter (PM) is important for the maintenance of acceptable air quality. The high cost of conventional instruments typically limits the number of monitoring sites, which in turn undermines the accuracy of real-time mapping of sources and hotspots of air pollutants with sufficient spatial resolution. In this study, a wireless network of low-cost particle sensors that can be deployed indoors was developed. To overcome the well-known limitations of low sensitivity and poor signal quality associated with low-cost sensors, a sliding window and a low pass filter were developed to enhance the signal quality. Utility of the networked system with improved sensitivity was demonstrated by deploying it in a woodworking shop. Data collected by the networked system was utilized to construct spatiotemporal PM concentration distributions using an ordinary Kriging method and an Artificial Neural Network model to elucidate particle generation and ventilation processes

CCWI2017: F98 'Two-Point Constraint Control of Water Quality in Distribution Networks'

Providing potable drinking water, with sufficient free residual chlorine to prevent microbial regrowth and contaminant by-product formation, is of utmost importance for municipal authorities. This work aims towards water quality control as it transports through water distribution networks. Presence of excessive chlorine (used as disinfectant worldwide) in water causes the generation of carcinogenic disinfectant by-products such as Tri-halo methane (THMs). On the other hand, lower chlorine levels in water results in microbial contamination. Hence, this leads to a two-point constraint control problem of maintaining free residual chlorine levels under the upper and lower bounds, by suitably optimizing the disinfectant dosage at booster stations. However, transport delay and complex interrelations present amongst the nodes in large water distribution network, makes it difficult to design a global feedback control system. Therefore, in this work we have proposed to decentralize the system by clustering demand nodes of the network based on concentration sensitivity matrix analysis. Further, input-output pairs for decentralized controllers were mapped with the knowledge of interactions between manipulating and control variables using partial correlation analysis. We also considered the results of partial correlation analysis to generate communication links which will be implemented for higher level co-ordinated decentralized control to account for dynamic, non-negligible interactions as disturbances to local control units. The proposed three step clustering-mapping strategy and mathematical formulation for two point control by optimized dosage is successfully verified for the steady state case on a prototype distribution example network

Battle of the Attack Detection Algorithms:Disclosing cyber attacks on water distribution networks

The BATtle of the Attack Detection ALgorithms (BATADAL) is the most recent competition on planning and management of water networks undertaken within the Water Distribution Systems Analysis Symposium. The goal of the battle was to compare the performance of algorithms for the detection of cyber-physical attacks, whose frequency has increased in the last few years along with the adoption of smart water technologies. The design challenge was set for the C-Town network, a real-world, medium-sized water distribution system operated through programmable logic controllers and a supervisory control and data acquisition (SCADA) system. Participants were provided with data sets containing (simulated) SCADA observations, and challenged to design an attack detection algorithm. The effectiveness of all submitted algorithms was evaluated in terms of time-to-detection and classification accuracy. Seven teams participated in the battle and proposed a variety of successful approaches leveraging data analysis, model-based detection mechanisms, and rule checking. Results were presented at the Water Distribution Systems Analysis Symposium (World Environmental and Water Resources Congress) in Sacramento, California on May 21-25, 2017. This paper summarizes the BATADAL problem, proposed algorithms, results, and future research directions.</p