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Biofouling is the phenomenon of micro-organism attachments to wet surfaces. Complete understanding of the mechanisms and rates of biofilms creations are partially understood therefore forecasting their formations is difficult. This study is on biofouling predictions for water distribution systems pipelines using model trees, artificial neural networks, and logistic regression. The three methods were tested through base runs and sensitivity analysis runs using data from the experiment conducted by Simões et al. (2006). The results showed that none of the models were superior for all cases, therefore a single model could not be recommended. This leads to an important conclusion that utilising ‘low cost’ modelling methods such as logistic regression can be sufficient for providing reliable estimates for biofilm growth potential. ‘Low cost’ approaches should be applied prior to invoking expensive models such as data driven methods as the latter might not be needed. [ABSTRACT FROM PUBLISHER]

This study presents a methodology for the inclusion of hydraulics uncertainty in contamination source identification. Current research normally considers the system hydraulics as deterministic and the water quality sensors as ideal. In reality however only a small portion of the hydraulic data is known and most likely only Boolean sensor information of a contamination existence. There is a need to incorporate these considerations in contamination source identification models and to explore their influence on the modelling ability to correctly detect the characteristics of a contamination intrusion. This problem is addressed in this manuscript. The proposed method is based on a previous contamination source detection model developed by the authors which is further embedded in a statistical framework for quantifying the uncertainty of a contamination source detection outcome. The methodology is demonstrated on three example applications of increasing complexity through base runs and sensitivity analyses. [ABSTRACT FROM AUTHOR]

A simple, straightforward, modified genetic algorithm scheme for contaminant source characterization using imperfect sensors is presented and demonstrated in this study. Previous work on this subject concentrated on developing source-inversion models using sensors that provide accurate, unbiased, contamination concentration measurements. The developed contamination source-detection model is implemented using three sensor types: (1) perfect sensors providing accurate, unbiased, contamination concentration measurements; (2) sensors transmitting fuzzy measured information (i.e., high, medium, and low contamination); and (3) '0-1' (Boolean) sensors indicating only a contamination presence. A comparison between the three sensor types is explored taking into consideration thesystem's response time (i.e., the time elapsed between a contaminant detection and a decision-maker's response action). The methodology capabilities are demonstrated using two example applications of increasing complexity, showing the trade-offs between the sensor types and the model abilities to receive a unique solution to the source-detection problem. [ABSTRACT FROM AUTHOR]

The optimal design problem of a water distribution system is to find the water distribution system component characteristics (e.g. pipe diameters, pump heads and maximum power, reservoir storage volumes, etc.) which minimize the system's capital and operational costs such that the system hydraulic laws are maintained (i.e. Kirchhoff's first and second laws), and constraints on quantities and pressures at the consumer nodes are fulfilled. In this study, an adaptive stochastic algorithm for water distribution systems optimal design based on the heuristic cross-entropy method for combinatorial optimization is presented. The algorithm is demonstrated using two well-known benchmark examples from the water distribution systems research literature for single loading gravitational systems, and an example of multiple loadings, pumping, and storage. The results show the cross-entropy dominance over previously published methods. [ABSTRACT FROM AUTHOR]

This article extends previous work on optimal booster chlorination injection design and operation in water distribution systems by solving the scheduling problem of pumping units in conjunction with the design and operation problem of booster chlorination stations. Two models are formulated and solved using a genetic algorithm scheme tailor-made to EPANET: Min Cost—for minimizing the costs of pumping and the chlorine booster design and operation, and Max Protection—for maximizing the system protection by maximizing the injected chlorine dose. An example application is explored through a base run and sensitivity analysis showing that the algorithm proposed is robust and reliable, and that the pump and chlorine injection scheduling are mutually connected. [ABSTRACT FROM AUTHOR]

Drinking water utilities around the world are vulnerable to various types of terrorist attacks including warfare contamination and bioterrorism. A distribution system comprises water tanks, pipes, pumps, and other components that deliver treated water from treatment plants to consumers. Particularly among large utilities, distribution systems may contain thousands of kilometers of pipes and numerous delivery points, which can be highly vulnerable to a terrorist deliberate contamination injection. This paper extends previous work on optimal early warning monitoring system layout for water networks security by addressing the monitoring stations detection sensitivities and response delays, and the consumer demands and contaminant injected flow rates randomness. The methodology developed is demonstrated on two example applications. [ABSTRACT FROM AUTHOR]

This paper describes the methodology and application of a genetic algorithm scheme tailor-made to EPANET, for optimizing the operation of a water distribution system under unsteady water quality conditions. The water distribution system consists of sources of different qualities, treatment facilities, tanks, pipes, control valves, and pumping stations. The objective is to minimize the total cost of pumping and treating the water for a selected operational time horizon, while delivering the consumers the required quantities at acceptable qualities and pressures. The decision variables for each of the time steps that encompass the total operational time horizon include: the scheduling of the pumping units, settings of the control valves, and treatment removal ratios at the treatment facilities. The constraints are: head and concentrations at the consumer nodes, maximum removal ratios at the treatment facilities, maximum allowable amounts of water withdrawals at the sources, and returning at the end of the operational time horizon to a prescribed total volume in the tanks. The model is explored through two example applications. [ABSTRACT FROM AUTHOR]

An application of stochastic simulation for the reliability analysis of single and multiquality water distribution systems (MWDS) is formulated and demonstrated. MWDS refers to systems in which waters of different qualities are taken from sources, possibly treated, mixed in the system, and supplied as a blend. The stochastic simulation framework was cast in a reliability analysis program (RAP), based on EPANET, and quantifying three reliability measures: the fraction of delivered volume (FDV), the fraction of delivered demand (FDD), and the fraction of delivered quality (FDQ). RAP is demonstrated on two example applications: a simple illustrative, and a more “real-life” complex one. The results quantify the reliability of the systems and provide lower bounds for the reliability measures adopted. [Copyright &y& Elsevier]