Identification of leakages by calibration of WDS models

Open Access journalCopyright © 2013 The Authors. Published by Elsevier Ltd.12th International Conference on Computing and Control for the Water Industry, CCWI2013Leakage detection is critical for the proper management of water distribution systems (WDS). This paper proposes a leak detection approach based on a Bayesian calibration method. The methodology uses a newly formulated index, μ, which takes into account the variation of roughness in pipes between the calibrated models with and without leaks. Case studies, which use literature networks, are presented to demonstrate how the approach can be used in identifying pipes with losses. The approach starts with a calibration method followed by the analysis of sensitivity matrices. The approach proved to be effective in finding leaks, but the results depend crucially on the number and quality of the observed data.European CommissionEuropean Social FundRegion of Calabri

Model calibration as a tool for leakage identification in WDS: A real case study

16th Water Distribution System Analysis Conference, WDSA2014 — Urban Water Hydroinformatics and Strategic PlanningCopyright © 2014 The Authors. Published by Elsevier Ltd.Water leakage detection is important for a proper management of water distribution systems (WDS). This paper proposes the application of the leak detection approach based on a new Bayesian calibration methodology. The methodology uses a new developed index μ, which takes into account the difference in roughness values in pipes of the calibrated models with and without leaks. The case study is referred to a real network and is presented to demonstrate how the approach can be used in identifying pipes with losses. The approach starts with the UNINET calibration method followed by the analysis of sensitivity matrices. The case study proves that the approach is effective in finding leaks in real networks, but the results depend on the quality of the observed data

Alternative approaches for solving the sensor placement problem in large networks

Positioning sensors in a water supply network is a NP - hard task. We propose three algorithms - one based on integer linear programming (ILP) and the other two based on the Greedy paradigm. We apply these algorithms to real case networks and compare the results of these algorithms with the results of an algorithm based on NSGA II, a genetic algorithm. We come to the conclusion that our algorithms outperform NSGA II in every single case. The algorithm based on linear integer programming may be applied as a competitor to the algorithm implemented in TEVA - SPOT (Berry, 2009), while the first Greedy algorithm may replace the ILP algorithm in large networks due to its faster running time. The second Greedy algorithm approaches the question on finding those nodes which are the most sensitive to variations in pressure and are thereby ideal places to monitor the hydraulic state of a water distribution network. © 2011 ASCE.R. Pinzinger, J. Deuerlein, A. Wolters, and A. R. Simpso

Identifying parameters of a broaching design using non-linear optimisation

Broaching is one of the most recognised machining processes that can yield high productivity and high quality when applied properly. One big disadvantage of broaching is that all process parameters, except cutting speed, are built into the broaching tools. Therefore, it is not possible to modify the cutting conditions during the process once the tool is manufactured. Optimal design of broaching tools has a significant impact to increase the productivity and to obtain high quality products. In this paper, an optimisation model for broaching design is presented. The model results in a non-linear non-convex optimisation problem. Analysis of the model structure indicates that the model can be decomposed into smaller problems. The model is applied to a turbine disc broaching problem which is considered as one of the most complex broaching operations

Smart Urban Water Networks

This book presents the paper form of the Special Issue (SI) on Smart Urban Water Networks. The number and topics of the papers in the SI confirm the growing interest of operators and researchers for the new paradigm of smart networks, as part of the more general smart city. The SI showed that digital information and communication technology (ICT), with the implementation of smart meters and other digital devices, can significantly improve the modelling and the management of urban water networks, contributing to a radical transformation of the traditional paradigm of water utilities. The paper collection in this SI includes different crucial topics such as the reliability, resilience, and performance of water networks, innovative demand management, and the novel challenge of real-time control and operation, along with their implications for cyber-security. The SI collected fourteen papers that provide a wide perspective of solutions, trends, and challenges in the contest of smart urban water networks. Some solutions have already been implemented in pilot sites (i.e., for water network partitioning, cyber-security, and water demand disaggregation and forecasting), while further investigations are required for other methods, e.g., the data-driven approaches for real time control. In all cases, a new deal between academia, industry, and governments must be embraced to start the new era of smart urban water systems

Advances in Modeling and Management of Urban Water Networks

The Special Issue on Advances in Modeling and Management of Urban Water Networks (UWNs) explores four important topics of research in the context of UWNs: asset management, modeling of demand and hydraulics, energy recovery, and pipe burst identification and leakage reduction. In the first topic, the multi-objective optimization of interventions on the network is presented to find trade-off solutions between costs and efficiency. In the second topic, methodologies are presented to simulate and predict demand and to simulate network behavior in emergency scenarios. In the third topic, a methodology is presented for the multi-objective optimization of pump-as-turbine (PAT) installation sites in transmission mains. In the fourth topic, methodologies for pipe burst identification and leakage reduction are presented. As for the urban drainage systems (UDSs), the two explored topics are asset management, with a system upgrade to reduce flooding, and modeling of flow and water quality, with analyses on the transition from surface to pressurized flow, impact of water use reduction on the operation of UDSs, and sediment transport in pressurized pipes. The Special Issue also includes one paper dealing with the hydraulic modeling of an urban river with a complex cross-section

Bursts identification in water distribution systems

The presented thesis investigates the identification of burst locations in water distribution systems (WDS) by analysis of field and simulation experimental data. This required the development of a new hybrid method of burst detection and sizing, and also a burst location identification algorithm. Generally, existing practice relies on a combination of some simple procedure and experience of the involved staff and cannot be easily automated. The practical methods are based on direct manifestation of burst on the surface or on systematically surveying suspected areas e.g. by using listening sticks, such methods are very time consuming. The proposed burst location algorithm is based on comparing data by means of statistical analysis of field data with results of water network simulation. An extended network hydraulic simulator is used to model pressure dependent leakage terms. The presence of a burst changes the flow pattern and also pressure at network nodes which may be used to estimate the burst size and its location. The influence of such random factors as demand flows and background leakage on the process of burst detection is also considered. The field data is from a generalised fixed area and variable area (FAVOR) test where inlet pressure is being stepped up and down and the following variables are measured: inlet flow, inlet pressure (head) and pressure at a number of selected sensitive nodes. The method has three stages and uses two different models, one is inlet flow model (IFM) to represent the total inlet flow and another is the extended hydraulic model to simulate different burst locations. Initially the presence of a potential burst is investigated. If this is confirmed precise values of the demand, background leakage flow and burst flow in IFM are subsequently estimated. They are used to identify the burst site at the third stage of the method. The method can be easily adapted for practical use. It requires data from experiments carried out at night between 1am and 5am and involves placing typically about 20 temporary loggers to collect the measurements during this period. It also requires the availability of a hydraulic model which normally is in the possession of a water company. The program has been implemented in the Matlab package and is easy to use. The current methodology is tuned to identify a single burst but can be generalised to identify locations of multiple bursts

Collaborative Planning and Event Monitoring Over Supply Chain Network

The shifting paradigm of supply chain management is manifesting increasing reliance on automated collaborative planning and event monitoring through information-bounded interaction across organizations. An end-to-end support for the course of actions is turning vital in faster incident response and proactive decision making. Many current platforms exhibit limitations to handle supply chain planning and monitoring in decentralized setting where participants may divide their responsibilities and share computational load of the solution generation. In this thesis, we investigate modeling and solution generation techniques for shared commodity delivery planning and event monitoring problems in a collaborative setting. In particular, we first elaborate a new model of Multi-Depot Vehicle Routing Problem (MDVRP) to jointly serve customer demands using multiple vehicles followed by a heuristic technique to search near-optimal solutions for such problem instances. Secondly, we propose two distributed mechanisms, namely: Passive Learning and Active Negotiation, to find near-optimal MDVRP solutions while executing the heuristic algorithm at the participant's side. Thirdly, we illustrate a collaboration mechanism to cost-effectively deploy execution monitors over supply chain network in order to collect in-field plan execution data. Finally, we describe a distributed approach to collaboratively monitor associations among recent events from an incoming stream of plan execution data. Experimental results over known datasets demonstrate the efficiency of the approaches to handle medium and large problem instances. The work has also produced considerable knowledge on the collaborative transportation planning and execution event monitoring

Explicit Building-Block Multiobjective Genetic Algorithms: Theory, Analysis, and Developing

This dissertation research emphasizes explicit Building Block (BB) based MO EAs performance and detailed symbolic representation. An explicit BB-based MOEA for solving constrained and real-world MOPs is developed the Multiobjective Messy Genetic Algorithm II (MOMGA-II) which is designed to validate symbolic BB concepts. The MOMGA-II demonstrates that explicit BB-based MOEAs provide insight into solving difficult MOPs that is generally not realized through the use of implicit BB-based MOEA approaches. This insight is necessary to increase the effectiveness of all MOEA approaches. In order to increase MOEA computational efficiency parallelization of MOEAs is addressed. Communications between processors in a parallel MOEA implementation is extremely important, hence innovative migration and replacement schemes for use in parallel MOEAs are detailed and tested. These parallel concepts support the development of the first explicit BB-based parallel MOEA the pMOMGA-II. MOEA theory is also advanced through the derivation of the first MOEA population sizing theory. The multiobjective population sizing theory presented derives the MOEA population size necessary in order to achieve good results within a specified level of confidence. Just as in the single objective approach the MOEA population sizing theory presents a very conservative sizing estimate. Validated results illustrate insight into building block phenomena good efficiency excellent effectiveness and motivation for future research in the area of explicit BB-based MOEAs. Thus the generic results of this research effort have applicability that aid in solving many different MOPs