STREAM EngD Project. Cohort VII (Oct 2015 start) DETAILS OF THE PROJECT 1. Proposed work

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1 STREAM EngD Project Cohort VII (Oct 2015 start) DETAILS OF THE PROJECT 1. Proposed work Background / rationale Currently, an innovative and smarter management of Water Distribution Systems (WDS) is needed to achieve higher levels of operational efficiency and customer service in spite of more tightening budgetary constraints. The use of advanced data analysis techniques for exploiting the huge amount of data already generated by and stored into the Information and Communication Technology (ICT) systems used for WDS management operations has the potential to enable a shift toward the smart water management paradigm. Data includes Supervisory and Control Data Acquisition (SCADA) systems and hydraulic simulation models, as well as new data made available following the increase in the density of coverage of pressure/flow monitoring locations and by the introduction of smart metering solutions (Automatic Meter Reading, AMR). Flow and pressure instrumentation has evolved over many years, and highly robust and accurate off the shelf designs are readily available. Data analytical tools are seeing wider takeup, since as data gathering and communication technologies steadily improved they became less expensive to own and operate, and data collection has become automated. Techniques utilising machine learning have been developed that manage and analyse increasing numbers of datastreams (Mounce et al., ; Romano et al., ). The current placement of instrumentation in water distribution networks in the UK is to meet the requirement to monitor and report leakage and low pressure. For example, flow and pressure, have become more widely used, especially on trunk mains and at District Meter Area (DMA) level, to facilitate zone-based asset management. However, measurement locations are not always the most sensitive for this purpose. Hence work has been conducted to explore increasing the number of instruments for both optimal location and for event localisation studies (e.g. Farley et al., ; and Romano et al., ). These techniques can utilise well calibrated hydraulic models and evolutionary techniques (such as Genetic Algorithms) 1 Mounce, S. R., Boxall, J. B., and Machell, J. (2010). Development and Verification of an Online Artificial Intelligence System for Burst Detection in Water Distribution Systems, ASCE Water Resources Planning and Management, 136, Romano, M., Kapelan, Z., and Savić, D. (2014). Automated Detection of Pipe Bursts and Other Events in Water Distribution Systems. ASCE Water Resources Planning and Management, 140 (4), Farley, B., Mounce, S. R., and Boxall, J. B. (2010). Field Testing of an Optimal Sensor Placement Methodology for Event Detection in an Urban Water Distribution Network, Urban Water J., 7, Farley, B., Mounce, S. R., and Boxall, J. B. (2013). Development and Field Validation of a Burst Localisation Methodology, ASCE Water Resources Planning and Management, 139, Romano, M., Kapelan, Z., and Savić, D. (2013). Geostatistical Techniques for Approximate Location of Pipe Burst Events in Water Distribution Systems, Journal of Hydroinformatics, 15 (3),

2 and/or Geostatistical techniques. Strategies have been developed for event fusion, recognition and interpretation (Romano et al., ). With increasing measurement points, the potential benefits of near real-time hydraulic models can be realised in which measured data is regularly streamed into a model to set and update simulation boundary conditions. Pressure data has been found to be extremely useful in this case for model calibration and fault finding (Machell et al., ). Availability of a stream of continuously updating flow and pressure data enables calibration to current, rather than historic measurements, allowing a continuous and iterative process, and reflecting ever changing dynamics in the network caused, for example, by changes to valve positions, the timing of pump operations or the turnover rate of service reservoirs. Smart water network technologies such as AMR, or Advanced Metering Infrastructure (AMI), will allow water companies to gather high frequency consumption data directly from residential and commercial customers as well as existing sites. This will remove the need to estimate components of leakage calculations, and flow or demand in hydraulic modelling applications; and provide a far better understanding of distribution network flow and pressure dynamics. While various components of these areas of work have been proven at conceptual, and some even at prototype stage, the true potential and value will be realised when they are combined and delivered as part as an integrated, harmonious package. The fusion, integration and development of a systems approach will be at the heart of this research. Research challenge / questions The project will develop and integrate technologies for improved operational management of events (such as leakages and bursts) in water distribution systems. The project will answer the following key questions: Can existing data sources be effectively analysed and fused to provide accurate and near real-time detection and then localisation of events within a DMA? Which algorithms from the fields of machine learning and evolutionary computation are best suited to optimally locate sensors within a DMA for event localisation? Can real-time hydraulic models and AMR data be incorporated to further refine and improve detection and location accuracy (e.g. to provide pipe level probabilities)? Which data analytics can best automatically verify, process, and continually analyse these large amounts of data? Can static asset data sources (such as available through GIS) improve performance and accuracy of the system even further? How can system outputs and information be best presented in an appropriate time frame to the operator? Methodology An overly ambitious programme of work has been specified here. The level of depth and detail and particularly the balance between use of near real-time hydraulic models and novel data 6 Romano, M., Kapelan, Z., and Savić, D. (2014). Evolutionary Algorithm and Expectation Maximisation Strategies for Improved Detection of Pipe Bursts and Other Events in Water Distribution Systems. ASCE Water Resources Planning and Management, 140 (5), Machell, J., Mounce, S. R., and Boxall, J. B. (2010) Online Modelling of Water Distribution Systems: a UK Case Study, Drink. Water Eng. Sci., 3, 21 27, doi: /dwes , 2010.

3 sources such as AMR/AMI, will to some extent be subject to company developments, pilots and initiatives and open to review to ensure that a practicable but valuable EngD programme of work is delivered. The research programme will comprise the following principal tasks: 1. Literature review of state of the art optimal sensor location along with algorithms and approaches for event detection and localisation at DMA and sub-dma level plus assessment of the latest related sensor technology available. 2. Familiarisation with company event detection systems capabilities and their operation, interfaces, data warehousing and potential enhancement. 3. Develop an approach for dealing with cascading DMAs to allow DMA level localisation and alarm suppression. 4. Develop a methodology using Geostatistical techniques, machine learning (such as with artificial neural networks) and, possibly, near real-time hydraulic models and AMR data aimed at associating a probability value of an event occurrence to each DMA pipe. 5. Utilise the outputs of 4, along with static asset data such as GIS and other data, to provide a means to identify the group of DMA pipes where an event most likely occurred, and data mining to explore precursor event identification. 6. Test and prototype the methodology developed offline. 7. Prototype and test the system with appropriate interfaces in overpopulated DMAs, first on historical events, then with system engineered events and in a near real-time environment. 8. Investigate companywide implementation and rollout. 9. Evaluate performance obtained and the benefits and savings realised from the system. 10. Thesis write up. The technology developed in this project will be tested on a pilot area within the Liverpool Demand Monitoring Zone (DMZ). This area is already used by United Utilities for different intelligent water network management piloting purposes. United Utilities has 231 DMAs in the Liverpool DMZ. These DMAs are usually observed by using: (i) flow sensors located at the DMA entry (and any water import/export) point and (ii) pressure sensors located at the critical points in the DMA. These sensors usually collect and transmit data with a 15 minute frequency. In addition to this, United Utilities has recently installed 168 extra pressure loggers in 35 of the aforementioned 231 DMAs (about 5 loggers per DMA) collecting data with a 1 minute frequency and is currently investigating smart metering opportunities. All this makes the selected area an ideal location for testing and demonstration activities in this project. Anticipated outcomes & benefits for the sponsoring organisation and other stakeholders This project will develop an enhanced and extended event recognition methodology that enables United Utilities to efficiently deal with the aforementioned increasing data availability and exploit event information resulting from the analysis of a larger number of DMA signals for determining the approximate location of an event within a DMA (in addition to detecting it in a fast and reliable manner). The reliable and timely detection of events together with the identification of their approximate location has the potential to facilitate prompt interventions and repairs. This, in turn, may reduce the potential damages to the infrastructures and to third parties and improve the water company s operational performance and customer service thereby yielding substantial improvements to the state-of-the-art in near real-time WDS incident management. The benefits can be summarised as :

4 1. Improved customer service by avoiding or minimising the impact of water main bursts and leaks. 2. Improved regulatory performance as a result of the above, namely: a. Improved Service Incentive Mechanism (SIM) scores. b. Improved Serviceability e.g. number of bursts. c. Meeting or bettering of leakage targets. 3. Reduced operational expenditure as a result of: a. Reduced search area for bursts and leaks. b. Avoidance of cost of failure e.g. customer call and complaints handling, customer compensation, increased repair costs, etc. c. Avoidance/reduction of mains repairs. 4. Reduced capital expenditure by prolonging the life of assets via increased condition and performance monitoring. 2. Potential scientific contribution of the project together and other innovative aspects. In the future, entire water supply (and sewerage) networks should be proactively managed from source to tap, using state of the art measurement and control technologies backed by data analysis and decision support systems; much of which will be programmed and analysed by artificial intelligence methods, making life much simpler for system operators and industry decision makers. In recent years, the UK water industry has been making increasing use of advances in sensor technology for monitoring parameters of water systems to identify performance shortfalls in order to improve asset management and hence provide better customer service, value and regulatory performance. The proliferation and diminishing costs of automated data transfer, such as by GSM, SMS and GPRS systems, is allowing all types of recorded data to be transferred from many disparate points on the networks. Water utilities have been struggling to archive or to transform the data effectively into knowledge with which to enable operational control. It is easy to anticipate that the environment may quite soon be teeming with tens of thousands of small, low-power, wireless sensors. Each of these devices will produce a stream of data, and those streams will need to be monitored and combined. The easier it is to collect and analyse large data sets the more water utilities will collect and, in a decade, tens or even hundreds of Petabytes of data may be routinely available. At this point, the water sector will truly be in the world of Big Data. However, even at this point, there is a burgeoning need for more information from data. Recent developments in the field of computational intelligence variously called soft computing, machine learning, or data-driven modelling are helping to produce practical and automated data analytics. The UK water industry is perceived to be a world leader and at the forefront in the area of telemetry and IT. This research would take advantage of this market lead initially in the UK, through development of a state of the art detection and location system at a leading water utility, but ultimately also with worldwide impact as machine leaning tools and methods are adopted. AMR is set to expand significantly in the UK as smart metering rolls out. All data is collected in near real-time and stored in a centralized database by data acquisition software. Future possibilities include linking smart metering to mobile technology. Information flow will be two way with the utility company for assessing when people are using supplies allowing proactive demand management and improved leakage detection. This project will benefit through Sheffield s involvement with FP7 project SmartWater4Europe ( as well as other STREAM projects using AMR data.

5 The specific work programme proposed includes significant opportunities for contributions to novel data driven approaches, interfacing with and utilising outputs from near real-time hydraulic models, application and development of evolutionary computing approaches and multivariate data mining. Much of these are active international research areas in hydroinformatics, particularly for leakage. 3. Business or management element that is to be tackled in the research. United Utilities is the main licensed water company for North West England and provides water and wastewater services to 6.9 million people and 0.2 million non-household customers. Every day United Utilities supplies almost 2,000 ML of water through a network of 42,000 Km of water mains. However, it loses 452 ML/day of treated and frequently pumped water lost through leaks and bursts. This is environmentally and economically damaging and has a negative impact on United Utilities operational performance, customer service and reputation. The technology developed in this project will provide United Utilities with a powerful tool to improve this situation and put United Utilities in a strong position for a changing market. It will also ensure good company performance as monitored by the regulator, Ofwat. Indeed, the change to the way that performance is measured in the UK is a major driver for this innovation. Specifically, performance regarding loss of supply was previously measured using the DG3 and Serviceability indicators. The former was measured via the number of properties affected for 6 hours or longer. The latter was the same but for 12 hours or more. The introduction of the Service Incentive Mechanism (SIM) means that all service failures, irrespective of duration, can impact performance. United Utilities is the UK leader in real-time logger coverage (monitoring about 100% of its ~2,700 DMAs) and has been one of the first water companies in the UK to recognise the high value of water infrastructure monitoring (i.e., potential for identifying network events both quickly and economically, improved network visibility and management, higher compliance with regulatory targets, etc.). The technology developed in this project will further give United Utilities a competitive edge on the national and international water markets. Therefore, aiding United Utilities to achieve its vision to be a leading North West service provider and one of the best UK water and wastewater companies.