Alfeu Sá Marques

Estimation of the benefits yielded by pressure management in water distribution systems

The occurrence of water losses in Water Distribution Systems is inevitable. Knowing that most of the real losses take place in distribution mains and in service connections, the methodology proposed in this paper is based on several leakage-assessment approaches from literature and on water distribution network modeling. This allows assessment of the benefits that can be achieved by pressure management in Water Distribution Systems, particularly in terms of water production reduction. Moreover, this approach can be useful for cost benefit analysis to help establish the level after which there is no more economic interest in reducing water losses (Economic Level of Leakage). Finally, the results from hypothetical case studies are presented and discussed, assuming the installation of Pressure Reducing Valves at District Metered Areas entry points.

Identification of the optimal entry points at District Metered Areas and implementation of pressure management

Nowadays, the implementation of pressure management in District Metered Areas (DMAs) is considered one of the most effective tools for leakage control, particularly in large networks and in systems with deteriorated infrastructures and with high pressure. The goal of the methodology proposed in this paper is to identify the optimal entry points at DMAs, determine the network needs in terms of reinforcement/replacement, and fix both the location and settings of different types of Pressure Reduction Valves (PRVs) for leakage control. This methodology is based on an optimisation model, which is solved by a Simulated Annealing algorithm, and the solutions obtained always fulfil the minimum pressure requirements for the network. The objective function comprises the total cost of the DMAs implementation and the economic benefits that can be achieved by pressure management. Finally, the results for two case studies are presented and discussed.

Decision support system to divide a large network into suitable District Metered Areas

This paper presents a new approach to divide large Water Distribution Networks (WDN) into suitable District Metered Areas (DMAs). It uses a hydraulic simulator and two operational models to identify the optimal number of DMAs, their entry points and boundary valves, and the network reinforcement/replacement needs throughout the project plan. The first model divides the WDN into suitable DMAs based on graph theory concepts and some user-defined criteria. The second model uses a simulated annealing algorithm to identify the optimal number and location of entry points and boundary valves, and the pipes reinforcement/replacement, necessary to meet the velocity and pressure requirements. The objective function is the difference between the economic benefits in terms of water loss reduction (arising from the average pressure reduction) and the cost of implementing the DMAs. To illustrate the proposed methodology, the results from a hypothetical case study are presented and discussed.

District Metered Areas Design Under Different Decision Makers’ Options: Cost Analysis

Water loss is a big challenge for water supply companies worldwide, and the Water Network Partitioning (WNP) is an excellent tool for water loss management–particularly in the current difficult economic and financial conditions. WNP is a recent research line and consists in dividing the water distribution network into smaller zones called District Metered Areas (DMAs) with one (or more, in exceptional cases) supply point, to reduce the network complexity and/or allow pressure management. Since there are several possible future scenarios, such as the water demand and/or the infrastructure degradation forecasts, which may have different impacts on the hydraulic behaviour, in this paper a computational application, based on an optimization model, is proposed to achieve a compromise between robustness required for the DMAs design (using a baseline scenario) and different decision makers’ options (using other scenarios with a lower “probability” of occurrence), to reduce the total cost. The objective function reflects the minimization of the squared deviations between the total cost of the DMAs design and the minimum cost for each scenario forecasted for the project plan, multiplied by the weight or “probability” of occurrence for each of the scenarios. The performance of the computational application is illustrated with a case study, and the results are encouraging.

Estimating the potential water reuse based on fuzzy reasoning

Studies worldwide suggest that the risk of water shortage in regions affected by climate change is growing. Decision support tools can help governments to identify future water supply problems in order to plan mitigation measures. Treated wastewater is considered a suitable alternative water resource and it is used for non-potable applications in many dry regions around the world. This work describes a decision support system (DSS) that was developed to identify current water reuse potential and the variables that determine the reclamation level. The DSS uses fuzzy inference system (FIS) as a tool and multi-criteria decision making is the conceptual approach behind the DSS. It was observed that water reuse level seems to be related to environmental factors such as drought, water exploitation index, water use, population density and the wastewater treatment rate, among others. A dataset was built to analyze these features through water reuse potential with a FIS that considered 155 regions and 183 cities. Despite some inexact fit between the classification and simulation data for agricultural and urban water reuse potential it was found that the FIS was suitable to identify the water reuse trend. Information on the water reuse potential is important because it issues a warning about future water supply needs based on climate change scenarios, which helps to support decision making with a view to tackling water shortage.

Stochastic evaluation of the impact of sewer inlets’ hydraulic capacity on urban pluvial flooding

Sewer inlet structures are vital components of urban drainage systems and their operational conditions can largely affect the overall performance of the system. However, their hydraulic behaviour and the way in which it is affected by clogging is often overlooked in urban drainage models, thus leading to misrepresentation of system performance and, in particular, of flooding occurrence. In the present paper, a novel methodology is proposed to stochastically model stormwater urban drainage systems, taking the impact of sewer inlet operational conditions (e.g. clogging due to debris accumulation) on urban pluvial flooding into account. The proposed methodology comprises three main steps: (i) identification of sewer inlets most prone to clogging based upon a spatial analysis of their proximity to trees and evaluation of sewer inlet locations; (ii) Monte Carlo simulation of the capacity of inlets prone to clogging and subsequent simulation of flooding for each sewer inlet capacity scenario, and (iii) delineation of stochastic flood hazard maps. The proposed methodology was demonstrated using as case study design storms as well as two real storm events observed in the city of Coimbra (Portugal), which reportedly led to flooding in different areas of the catchment. The results show that sewer inlet capacity can indeed have a large impact on the occurrence of urban pluvial flooding and that it is essential to account for variations in sewer inlet capacity in urban drainage models. Overall, the stochastic methodology proposed in this study constitutes a useful tool for dealing with uncertainties in sewer inlet operational conditions and, as compared to more traditional deterministic approaches, it allows a more comprehensive assessment of urban pluvial flood hazard, which in turn enables better-informed flood risk assessment and management decisions.

Optimal Management of Water Distribution Networks with Simulated Annealing: The C-Town Problem

AbstractSustainability is a major issue for water companies, who have to provide high quality services at achievable costs. In this context, water loss control and energy efficiency are two great challenges water companies have to face. Water loss represents both higher service cost (real losses) and loss of revenue (apparent losses), while the energy bill from treatment plants and pumping stations represents a significant part of the service cost. The Battle of Background Leakage Assessment for Water Networks (BBLAWN) was a competition dedicated to this subject: water distribution network (WDN) optimal management. Teams/individuals from academia, consulting firms, and utilities were invited to propose methodologies for solving the C-Town WDN problem: minimize operational and capital costs and background leakages. This paper presents one of the methodologies proposed at BBLAWN. The methodology proposed here to solve the C-Town WDN problem comprises two optimization models: a least-cost design model to ide...

Demonstration: Using IPython to Demonstrate the Usage of Remote Labs in Engineering Courses – A Case Study Using a Remote Rain Gauge

The use of collaborative tools that can contribute to share and demonstrate the usage of remote experiments, to support teaching and to enhance the learning process, is of great importance in several educational contexts and particularly in engineering courses. Jupyter/IPython notebooks are one of these tools that provide a programming environment to develop and share scientific contents and that can promote the access to remote and virtual labs. Teaching and learning activities in different high education courses, especially in engineering subjects, can benefit of using this type of resources. This paper presents an IPython-based approach to show how to interact with a remote rain gauge to obtain data about the rainfall in a given location, which may be useful in different learning contexts, namely in programming or environmental science subjects.

Geosensing-based platform for supporting operational river flood forecast

Up-to-date information is fundamental for monitoring and managing large-scale river floods efficiently. Geosensors (or environmental sensors) ranging from water gauges to weather stations are nowadays used to gather such information. This data must be available in near real-time to feed hydrological and hydraulic models used to predict river flows and water levels. These predictions provide guidance when to take an action such as the issuing of a warning. Real-time decision support systems, frequently designated as flood forecasting and warning systems, are used to organize the complex process of coupling data and models in real-time. In this work is presented the first construction steps of such a system at a local level, focusing on the data sensor collection and management. The prototype platform is applied to the Mondego River nearby Coimbra City, Portugal.

Web-based platform for river flood monitoring

River floods are a major challenge. Here we demonstrate a web-based dissemination and sharing platform that brings together the essential information on river floods under a single user interface. The system contains various types of flood-related information such as water levels, flows and rainfall, collected by geosensor networks, as well other kinds of relevant information. The platform can serve as an information channel between experts and authorities, to improve communication and collaboration, and as a web-based source of information for the public, satisfying their need of being timely informed on water and flooding conditions.