Near Real-Time Detection Of Pipe Burst Events In Cascading District Metered Areas

A fully automated Event Recognition System (ERS) for the near real-time detection of pipe bursts and other network events such as boundary valve status changes and pressure management valve faults has been recently developed by the authors. This paper focuses on the further development of this system. The aim is to enhance the ERS approximate event location and alarm handling capabilities by developing and testing a new methodology that, in the case of cascading District Metered Areas (DMAs), automatically determines in which DMA an event occurred. The newly developed methodology makes use of a set of heuristic rules based on engineering knowledge, the Water Distribution System (WDS) schematic and the ERS outputs. The results of applying the new methodology to the historical pressure/flow data from several groups of cascading DMAs in the United Kingdom (UK) with real-life burst events are reported in this paper. The results obtained illustrate that the developed methodology not only enabled detecting the burst events occurred in a timely (i.e., within 30 minutes) and reliable (i.e., without any false alarm) manner but also allowed to always successfully determine in which DMA the event happened. The latter capability enables water companies to target the resources for the identification of the exact burst location to the greatest effect. Additionally, it enables reducing the potential of false alarms and the overall number of detection alarms, thereby facilitating interpretation of the ERS results

A Novel Decision Support System For Optimized Sewer Infrastructure Asset Management

This paper approaches the sewerage asset management challenge from a UK perspective by outlining a comprehensive methodology capable of optimising the performance of sewerage infrastructure networks using a series of Hydroinformatic tools. In order to define, evaluate and forecast the future performance of sewerage assets, a unique deterioration model is established to predict the future condition of the network. The model analyses historic CCTV survey information to identify deterioration trends based on key pipe characteristics. Against, this improved understanding of past, current and future condition, a collapse rate is predicted by correlating historic failures against the observed sewer condition profiles. The result is a novel relationship which is drawn between collapse rate and condition profile. From here, a prioritised inspection programme can be delivered that targets poorly performing and high consequence of failure assets. The survey information gather from these studies feeds into a previously successful sewer rehabilitation optimisation model that has been adapted under this new study to provide a mechanism for engineers to evaluate the trade-offs that exist between different sewer rehabilitation schemes. A series of GIS tools have been integrated within the model to identify the benefits from an operational perspective, thus guiding investment decisions towards those assets predicted to be in poor structural condition as well as causing operational issues, i.e., pollution, blockage and/or flooding events. As a result, the methodology acts as a series of strategic decision support tools which is capable of helping sewerage engineers and planners in the evaluation of different intervention programmes of work. A UK case study is provided to demonstrate the benefits of this approach

Genetic Programming For Cellular Automata Urban Inundation Modelling

Recent advances in Cellular Automata (CA) represent a new, computationally efficient method of simulating flooding in urban areas. A number of recent publications in this field have shown that CAs can be much more computationally efficient than methods than using standard shallow water equations (Saint Venant/Navier-Stokes equations). CAs operate using local state-transition rules that determine the progression of the flow from one cell in the grid to another cell, in many publications the Manning’s Formula is used as a simplified local state transition rule. Through the distributed interactions of the CA, computationally simplified urban flooding can be processed, although these methods are limited by the approximation represented by the Manning’s formula. Literature demonstrates that the viability of the Manning’s formula will break down with too large a time step, flow rates, too small a cell size, or too smooth roughness factor; Therefore further increases in computational efficiency could be gained with a better approximation, or rather one capable of producing the required simulation with enough accuracy at larger time steps, smaller cells sizes, smoother roughness factors. Genetic programming has the potential to be used to optimise state transition rules to maximise accuracy and minimise computation time. In this paper we present some preliminary findings on the use of genetic programming (GP) for deriving these rules automatically. The experimentation compares GP-derived rules with human created solutions based on the Mannings formula and findings indicate that the GP rules can improve on these approaches

Interactive 3D Visualization Of Optimization For Water Distribution Systems

This project investigates the use of modern 3D visualisation techniques to enable the interactive analysis of water distribution systems with the aim of providing the engineer with a clear picture of the problem and thus aid the overall design process. Water distribution systems are complex entities that are difficult to model and optimise as they consist of many interacting components each with a set of considerations to address, hence it is important for the engineer to understand and assess the behaviour of the system to enable its effective design and optimisation. This paper presents a new three-dimensional representation of pipe based water systems and demonstrates a range of innovative methods to convey information to the user resulting in the ability to simultaneously display more useful information than traditional two-dimensional plan view network representations. The interactive visualisation system presented not only allows the engineer to visualise the various parameters of a network but also allows the user to observe the behaviour and progress of an iterative optimisation method. This paper contains examples of the combination of the interactive visualisation system and an evolutionary algorithm enabling the user to track and visualise the actions of the algorithm down to an individual pipe diameter change. The visualisation will aggregate changes to the network over an evolutionary algorithm run and ‘lift the lid’ on the operations of an EA as it is optimising a network. In addition, the method allows the engineer to view other important optimisation-related information such as the extent to which constraints have been violated in the current design. It is proposed that this interactive visualisation system will provide engineers an unprecedented view of the way in which optimisation algorithms interact with a network model and may pave the way for greater interaction between engineer, network and optimiser in the future

Multi-Objective Pipe Smoothing Genetic Algorithm For Water Distribution Network Design

This paper describes the formulation of a Multi-objective Pipe Smoothing Genetic Algorithm (MOPSGA) and its application to the least cost water distribution network design problem. Evolutionary Algorithms have been widely utilised for the optimisation of both theoretical and real-world non-linear optimisation problems, including water system design and maintenance problems. In this work we present a pipe smoothing based approach to the creation and mutation of chromosomes which utilises engineering expertise with the view to increasing the performance of the algorithm whilst promoting engineering feasibility within the population of solutions. MOPSGA is based upon the standard Non-dominated Sorting Genetic Algorithm-II (NSGA-II) and incorporates a modified population initialiser and mutation operator which directly targets elements of a network with the aim to increase network smoothness (in terms of progression from one diameter to the next) using network element awareness and an elementary heuristic. The pipe smoothing heuristic used in this algorithm is based upon a fundamental principle employed by water system engineers when designing water distribution pipe networks where the diameter of any pipe is never greater than the sum of the diameters of the pipes directly upstream resulting in the transition from large to small diameters from source to the extremities of the network. MOPSGA is assessed on a number of water distribution network benchmarks from the literature including some real-world based, large scale systems. The performance of MOPSGA is directly compared to that of NSGA-II with regard to solution quality, engineering feasibility (network smoothness) and computational efficiency. MOPSGA is shown to promote both engineering and hydraulic feasibility whilst attaining good infrastructure costs compared to NSGA-II

a two stage calibration for detection of leakage hotspots in a real water distribution network

Abstract The paper presents a two-stage approach for solving a calibration-based problem for the ultimate purpose of detecting leakage hotspots. This is compared with a one-stage approach. A Genetic Algorithm is used to solve optimization problems of searching for calibration parameters values, while minimizing the differences between observations and model predictions. The approach takes into account suspect valves with unknown status, as well as pipes with incorrect roughness values and nodal leakage. The methodology also takes advantage of a new approach to reducing solution search space size for the optimisation problems. These problems are then solved for different leakage scenarios. Artificial calibration data are generated by means of hydraulic modelling employed to mimic planned hydrant discharges during a low demand period, combined with step tests. The case study demonstrates the improved leakage detection and model calibration of the two-stage calibration approach relative to the one-stage approach, which considers all calibration parameters together. This can result in a useful practical network operation tool

Evaluating And Optimizing Sustainable Drainage Design To Maximize Multiple Benefits: Case Studies In China

In the past, the focus of drainage design was on sizing pipes and storages in order to provide sufficient network capacity. This traditional approach, together with computer software and technical guidance, had been successful for many years. However, due to rapid population growth and urbanisation, the requirements of a “good” drainage design have also changed significantly. In addition to water management, other aspects such as environmental impacts, amenity values and carbon footprint have to be considered during the design process. Going forward, we need to address the key sustainability issues carefully and practically. The key challenge of moving from simple objectives (e.g. capacity and costs) to complicated objectives (e.g. capacity, flood risk, environment, amenity etc) is the difficulty to strike a balance between various objectives and to justify potential benefits and compromises. In order to assist decision makers, we developed a new decision support system for drainage design. The system consists of two main components – a multi-criteria evaluation framework for drainage systems and a multi-objective optimisation tool. The evaluation framework is used for the quantification of performance, life-cycle costs and benefits of different drainage systems. The optimisation tool can search for feasible combinations of design parameters such as the sizes, order and type of drainage components that maximise multiple benefits. In this paper, we will discuss real-world application of the decision support system. A number of case studies have been developed based on recent drainage projects in China. We will use the case studies to illustrate how the evaluation framework highlights and compares the pros and cons of various design options. We will also discuss how the design parameters can be optimised based on the preferences of decision makers. The work described here is the output of an EngD project funded by EPSRC and XP Solutions

Real-Time Runoff Prediction Based On Data Assimilation And Model Bias Reduction

The conceptual nature of rain-runoff models causes a significant drawback in the form of model bias. In certain types of applications, like real-time prediction, the model bias is directly mapped to the prediction result. The methodologies that are developed to improve real-time prediction, like data assimilation or real-time model calibration are taking the advantage of the information in the form of measured real-time data, but does not face the problem of model inaccuracy. In this paper the method based on data assimilation that is capable of detecting and reducing model bias (hence leading to more accurate real-time predictions) is proposed. The method has two principal steps. In the first step, the most recent model prediction is modified using observed data from the near past. The model prediction with reduced bias is obtained in this step. In the second step, the most recently measured runoff data value is assimilated with the model prediction from the previous step to further improve the prediction. This is done using the least-squares data assimilation technique. The new method is tested and verified on the case study of rain-runoff model of town Aarhus in Denmark. The results obtained show that the new method has improved prediction capabilities when compared to both off line model predictions and predictions of the model with data assimilation only. The additional benefit is the low computational cost since the new method requires only one rain-runoff model simulation run for a single prediction

Using Multi-Objective Optimization To Maximize Multiple Benefits For Sustainable Drainage Design

For many years, drainage design was mainly about providing sufficient network capacity. This traditional approach had been successful with the aid of computer software and technical guidance. However, the drainage design criteria had been evolving due to rapid population growth, urbanisation, climate change and increasing sustainability awareness. Sustainable drainage systems that bring benefits in addition to water management have been recommended as better alternatives to conventional pipes and storages. Although the concepts and good practice guidance had already been communicated to decision makers and public for years, network capacity still remains a key design focus in many circumstances while the additional benefits are generally considered secondary only. Yet, the picture is changing. The industry begins to realise that delivering multiple benefits should be given the top priority while the drainage service can be considered a secondary benefit instead. The shift in focus means the industry has to adapt to new design challenges. New guidance and computer software are needed to assist decision makers. For this purpose, we developed a new decision support system. The system consists of two main components – a multi-criteria evaluation framework for drainage systems and a multi-objective optimisation tool. Users can systematically quantify the performance, life-cycle costs and benefits of different drainage systems using the evaluation framework. The optimisation tool can assist users to determine combinations of design parameters such as the sizes, order and type of drainage components that maximise multiple benefits. In this paper, we will focus on the optimisation component of the decision support framework. The optimisation problem formation, parameters and general configuration will be discussed. We will also look at the sensitivity of individual variables and the benchmark results obtained using common multi-objective optimisation algorithms. The work described here is the output of an EngD project funded by EPSRC and XP Solutions

Integrated Decision Support Framework

An Integrated Framework for the development of a Decision Support System (DSS) is described, facilitating strategic decision-making for the long-term city metabolism planning problem. A novel methodology for comparing and selecting alternative solutions is presented employing Multi-Criteria Decision Analysis and multiple scenarios handling resulting in a system able to deal with uncertain futures, complementing the modules developed in WP54 and using the same platform (AWARE-P) as the other software applications delivered in WA5. The DSS assists in the decision making process by managing problem definition, structuring/analysis and solving. This document outlines the Integrated Decision Support Framework upon which the DSS will ultimately be delivered. Succinctly, this is the software environment in which the DSS will be constructed, describing the concepts, data structures, data flows and associations that will be required to bring it to fruition.Morley, MS.; Kapelan, Z.; Savić, DA. (2012). Integrated Decision Support Framework. http://hdl.handle.net/10251/4552