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Water Resources Systems Planning And Management

The formation of bacterial regrowth and disinfection by-products is ubiquitous in chlorinated water distribution systems (WDSs) operated with organic loads. A generic, easy-to-use mechanistic model describing the fundamental processes governing the interrelationship between chlorine, total organic carbon (TOC), and bacteria to analyze the spatiotemporal water quality variations in WDSs was developed using EPANET-MSX. The representation of multispecies reactions was simplified to minimize the interdependent model parameters. The physicochemical/biological processes that cannot be experimentally determined were neglected. The effects of source water characteristics and water residence time on controlling bacterial regrowth and Trihalomethane (THM) formation in two well-tested systems under chlorinated and non-chlorinated conditions were analyzed by applying the model. The results established that a 100% increase in the free chlorine concentration and a 50% reduction in the TOC at the source effectuated a 5.87 log scale decrement in the bacteriological activity at the expense of a 60% increase in THM formation. The sensitivity study showed the impact of the operating conditions and the network characteristics in determining parameter sensitivities to model outputs. The maximum specific growth rate constant for bulk phase bacteria was found to be the most sensitive parameter to the predicted bacterial regrowth.

Modeling Bacterial Regrowth and Trihalomethane Formation in Water Distribution Systems

Intermittent Water Supply (IWS) is prevalent in most developing countries. Specifically, in India, IWS is existent throughout the country. Many studies focus on documenting the effects of IWS, and rarely the drivers of the IWS regime are studied. In this study, a systematic literature review was conducted on IWS studies around the globe. The various causes for IWS were documented. Then, by studying India’s typical water supply system (WSS) configuration, the vicious cycle of IWS in India is discussed. Further, the drivers of IWS were identified and elaborated with the causing mechanisms. This knowledge will help devise strategies and solutions for improving the IWS in India and other developing countries with similar socio-economic conditions.

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Drivers for Intermittent Water Supply in India: Critical Review and Perspectives

Model-based investigation of the formation, transmission, and health risk of perfluorooctanoic acid, a member of PFASs group, in drinking water distribution systems.

A mechanistic simulation model predicting the response of water distribution systems (WDSs) operated with or without disinfectant residual toward accidental arsenic contamination is develop...

Modeling the Response of Nonchlorinated, Chlorinated, and Chloraminated Water Distribution Systems toward Arsenic Contamination

Making waves: Applying systems biology principles in water distribution systems engineering.

Ensuring consistent and high-level water quality is paramount for water utilities to meet health requirements and attain customer satisfaction. To this end, water utilities need to constantly surveil all relevant water quality parameters, for example, chlorine-concentration, as well as to optimally control dosage rates in their drinking water distribution systems (DWDSs). Simulation models coupling DWDS hydraulics and water quality have been well established and highly accurate. However, they are computationally very expensive such that optimization of control parameters may only be possible to a very limited extent. In this work, we are proposing the use of a lightweight, machine learning-based surrogate model for the coupled simulation of hydraulic and water quality parameters that may serve to reduce simulation times and render optimization of control parameters more efficient. The baseline model is system-specific and learns to predict the steady-state hydraulic and water quality state simultaneously based on common inputs to a DWDS model, that is, water demands and dosage rates at reservoir levels. Results indicate good prediction capabilities of the surrogate model with R2 values greater than 0.98 as well as error rates below 0.01% for hydraulic parameters and below 1% for water quality parameters. Some slight spatial trends in the prediction error’s variance are identified for hydraulics as well as for water quality parameters. 

A Machine Learning-Based Surrogate Model for Coupled Hydraulic and Water Quality Simulation in Water Distribution Networks

Supplying high-quality water is the key task of water distribution systems (WDSs). Although in Germany and some other countries chlorine is no longer used, it remains as a major disinfectant in WDSs worldwide. Therefore, chlorine concentration represents an important parameter for determining the water quality; that is, we should ensure the chlorine concentration within a reasonable range in a WDS. However, due to the complexity of the network structure and nonlinear behavior of the system, the control of chlorine concentration in WDSs imposes a challenging task. In this study, a model-based optimal control strategy is developed to address this problem. The mass and energy conservation laws are used to describe the hydraulic properties of WDSs. The one-dimensional advective transport model is simplified to describe the decay of chlorine in the pipelines. The chlorine concentration limits at the nodes are formulated as inequality constraints which will be satisfied by manipulating the flows and their directions in the pipelines. As a result, a nonlinear optimization problem is formulated and solved to achieve the specified chlorine concentration. For verifying our approach, we deliver the computed results for benchmark networks as input to the simulation model in EPANET, and the simulation gives satisfactory values of the specified chlorine concentration in the network. 

Optimal Control of Chlorine Concentration in Water Distribution System

Water distribution systems (WDSs) function to deliver high-quality water in major quantities. While standard water quality parameters are monitored at waterworks, it is still a challenge to monitor water quality in the WDS network itself. Only hydraulic parameters are frequently monitored and modeled in drinking water networks in Germany. Moreover, the majority of German drinking water utilities does not disinfect when the product leaves the waterworks. This is also common practice in Northern European countries. It is thus important to monitor specific organic and bacteriological water quality parameters which define the system state. This study develops an experimental and simulation integrated framework for continuously monitoring and simulating selected organic and bacteriological water quality parameters, so abnormal deviations in water quality behavior can be detected and responded to in real time. A simple reproducible bacteria regrowth model was taken to initialize the validation of experimental values in a water quality model simulation. Batch experiment measurements from a flow cytometer are analyzed to establish an interface between laboratory values and water quality simulations. Monod kinetics are utilized to describe the bacterial growth rate according to the respective substrate for modeling the bulk species in the water network. Experimental values are incorporated in the simulations for validation. The simulation of bacterial growth is conducted firstly on a network model of a real-life test bed and on various selected distribution system models of different sizes and complexities. First results of the water quality simulations show a successful transition of experimental analysis into water quality simulations and give a promising outlook for developing an online-monitoring and prediction methodology for detecting water quality anomalies efficiently in real time. 

Establishing an Experimental and Simulation Interface for Online Monitoring and Modeling of Bacterial Growth in Water Distribution Systems

Post-treatment drinking water is far from sterile, so microbial regrowth occurs, and the distribution pipes behave like a dynamic ecological niche for microbes. Unwarranted microbial regrowth within distribution pipes can lead to quality issues and health concerns. The application of computer-based tools adept at mechanistically simulating the dynamics of heterotrophic bacteria within distribution pipes is a practical approach to safeguarding biological stability during drinking water distribution systems (DWDS) operation. The imperfect understanding of most of the stochastic processes related to the heterotrophic bacterial dynamics and the natural randomness of the system constraints make mechanistic modeling complicated. Within the context of the current state of the art, the questions on the level of detailing in describing the processes within the pipe domain for mechanistically describing the microbiological quality with a certain acceptable level of accuracy are still unanswered. In this regard, we attempt to answer these questions and overcome the limitations of the state of the art to develop a practically relevant modeling tool for DWDS management. The specific objectives of this study are to (1) develop stochastic mechanistic models considering the three-level uncertainties—model parameters (kinetic rate constants), model coefficients (dispersion coefficient), and state variables (source quality characteristics); (2) understand the level of complexity that needs to be integrated into modeling to explain the water quality dynamics correctly; and (3) present the applicability of the developed models for simulating microbiological quality fluctuations in a real-world DWDS. 

G. R. Abhijith

Papers

Making waves: Applying systems biology principles in water distribution systems engineering.

A Machine Learning-Based Surrogate Model for Coupled Hydraulic and Water Quality Simulation in Water Distribution Networks

Optimal Control of Chlorine Concentration in Water Distribution System

Establishing an Experimental and Simulation Interface for Online Monitoring and Modeling of Bacterial Growth in Water Distribution Systems

Inferring the Stochasticity Associated with Modeling the Biological Stability of Drinking Water within Distribution Networks