Real-time water quality assessment and metrics for strategic water distribution system operation and intervention

Transcription

1 STREAM EngD Project Cohort X (Sept 2018 start) Real-time water quality assessment and metrics for strategic water distribution system operation and intervention DETAILS OF THE PROJECT 1. Proposed work Background / rationale Our society is founded on distributed infrastructure, dependent on it to deliver our fundamental needs; such as pipe networks for clean drinking water. However, distributed infrastructure does not generate data by default; data must be generated by asset specific instrumentation. Water Distribution Systems (WDS) offer some of the greatest challenges and potential for transformation. Its infrastructure systems are amongst the most complex, uncertain (location, age, connectivity, material etc.), and oldest with lowest investment rates. Yet WDS are already recognised as being Data Rich and Information Poor (DRIP). Water quality at the point of distribution is obtained from manual sampling or temporarily deployed portable water quality monitors. In 2016 water companies took a total of 3.87m water quality samples split roughly a third between water treatment works, service reservoirs and consumer taps. The proposed research is urgently needed, aiming to fix the current DRIP status for water quality. Research challenge / questions The overall aim is to develop, test/validate new methodologies and associated tool(s) for effective water quality proactive management of discolouration and chlorination via data driven analytics. Key questions are: How do longer term trends in hydraulics (including transient behaviour) and water quality influence discolouration risk? How can long term monitoring data be used (by tracking correlations and metrics over time) to inform intervention options? What fingerprints exist across multiple water quality (and hydraulic) parameters to enable event detection and identification, and can their discovery be automated with pattern recognition? What should be the frequency of flow conditioning events? How can network specific safe flows be calculated from data? How can a company use this knowledge to reduce risk by intervening as appropriate? Methodology The methodology is based around developing new powerful algorithms (utilising machine learning) to analyse large data sets in real time that will give actionable insights to the operator. The deliverables of this project will enable the water company to more efficiently

2 operate these networks resulting in a better quality of water delivered to the consumer at reduced cost and reduced risk. Water distribution networks must be resilient to meet changes in demand. Prognostic analytics will increase resilience, possible outcomes of the project are algorithms that allow, for example, to identify the optimal frequency of flow conditioning events to reduce the risk of a discoloration event caused by a flow change. Company developments, pilots and initiatives are 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 in water quality monitoring (latest sensor technology) along with algorithms and approaches for data usage as regards risk, modelling, pattern recognition and the evaluation of metrics i.e. knowledge from data. 2. Familiarisation with company water quality data and systems capabilities and their operation, interfaces, data warehousing and potential enhancement. 3. Identification of case study / test area in the UU and collection/verification of relevant data. 4. Development of prognostic algorithms for water quality data from pilot site evaluate methodologies and approaches from machine learning and statistics, including for event tracking and correlations (turbidity vs chlorine, or against hydraulic parameters). Initial models for: o Turbidity metric. o Chlorine metric. Data sources may also allow inclusion of temperature and conductivity. 5. Develop a software based suite of tools implementing the selected algorithms 6. Test and prototype the methodology developed offline (on historic datasets). 7. Further prototype and test the system with appropriate interfaces with system engineered events and in a near real-time environment. Analysis of results. 8. Investigate companywide final implementation and rollout using integration with company data warehouses/ cloud based interfaces (using the latest platforms). 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 120 extra pressure loggers in 13 of the aforementioned 231 DMAs (about 9 loggers per DMA) collecting data with a 1 minute frequency and plans to install additional high speed pressure monitoring devices, multiparameter water quality monitor (e.g. MetriNet) and a number of smart valves (e.g. pressure reducing valves with electronic pilot controllers). 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 help improve the Water Quality Service Index Outcome Delivery Incentive (ODI) through a potential reduction in unwanted customer contacts for water quality. This ODI can attract a reward for out performance and financial penalty for poor performance. A reduction in DWI water quality incidents is also expected together with reduced operational

3 costs to respond to discolouration/water quality issues and avoidance of cost of failure e.g. customer call and complaints handling, customer compensation, etc. Intangible benefits such as avoiding distress to our customers and reputational damage should also be factored in. ATi are a supplier to UU and their new MetriNet sensor systems will be utilised particularly for supplying high quality chlorine and turbidity measurements, ideally also with pressure at the same point of the network (Turbidity and chlorine are critical control parameters for water treatment and disinfection). Siemens and OSISoft currently work with United Utilities and provide software and data infrastructure solutions to the water industry. Siemens produce a secure cloud based IoT platform (MetaSphere) and have a strategic research partnership with the University of Sheffield. OSISoft produce the PI System, for capturing and leveraging sensor-based data. 2. Potential scientific contribution of the project together and other innovative aspects. The core idea of the proposed research is that water quality monitoring and analysis of distributed infrastructure at higher resolution transforms perturbations from noise to probes for understanding system behaviour. This will have specific direct impact on water service providers (WSP) allowing them to select optimal cost benefit break points in investment in monitoring instrumentation. This research has been conceived, designed and will be carried out with industrial partners including a monitoring instrumentation manufacturer and two data infrastructure providers. Improved management has direct benefits for operators and customers but also indirectly the wider economy, society and environment. Regulators will use the research to redefine their expectations of these operators, the ultimate beneficiaries being not only the current and future customers of WSPs but also everyone that shares the environment in which they operate. Cloud-based efficient solutions for data analytics can be provided according to a Software as a Service (SaaS) paradigm. SaaS is a method of delivery of software in which it is centrally hosted on the cloud, and can be accessed remotely via a web browser. Through this project the consortium will validate new analytics solution at a scale that will be trusted by the Industry by collaborating with two key players (Siemens and OSISoft). The University of Sheffield are strategic partners to Siemens and long term collaborators with ATi and United Utilities. Through the Pennine Water Group, TWENTY65 and other research partnerships the group has been at the forefront of data driven water research for several decades. Mounce and Boxall had successful commercialisation of Artificial Intelligence based automated analysis research software (FlowSure) by Servelec Technologies, this was nominated for 'Innovative Technology' Water Industry Achievement Award 2017 with Welsh Water for application to wastewater networks. The academic investigators have an excellent track record in academic collaboration with all of the other leading UK water groups. Many areas of research related to water distribution systems will benefit from the improved accuracy and reduced uncertainty of systems understanding due to the planned instrumentation deployment and data sets. This could transform areas such as discolouration risk, energy optimisation, robust and resilient networks and modelling water quality with application to chlorine decay under unsteady conditions, discolouration research and integrated research such as quantitative microbial risk assessment.

4 The investigators have a long established track record of close integration and collaboration with UK water industry research. Previous EPSRC research (Pipe Dreams) and industrial project work at Sheffield has developed prototype data driven tools for water quality event detection 12, linking water quality with hydraulics 3, modelling discolouration erosion and regeneration processes 45, relating water quality to age 6, prediction of iron risk 7, case based reasoning for water quality incidents 8 and a tool to explore correlations (using semblance analysis) between water quality and hydraulic data streams in order to assess asset deterioration and performance 9. This project will benefit through Sheffield s involvement with the PODDs consortium of water companies exploring proactive discolouration management in distribution systems ( which will next year move from PODDS VI to PODDS+ (and of which United Utilities is a member). Latest research developments in PODDS involved machine learning approaches. The specific work programme proposed includes significant opportunities for contributions to novel data driven approaches, interfacing with and utilising outputs from research projects that have explored the development of evolutionary computing approaches and multivariate data mining. Much of these are active international research areas in hydroinformatics, particularly for water quality. Significant focus will be placed on scientific contribution and presentation of results through conferences and journal publications. The research is particularly timely - which enhances its economic impact - because manufacturers of monitoring instrumentation are now able to supply equipment that can be used extensively (miniature, low power, telemetered). Thus direct benefits will accrue to instrument manufacturers, with further indirect benefits to the economy as the project partners are UK based. Furthermore, because the UK is a world leader in the water industry success for these manufacturers will lead to further expansion in a global market which currently stands at $500 bn. 1 Mounce, S. R., Machell, J. and Boxall, J. B. (2012). Water quality event detection and customer complaint clustering analysis in distribution systems. IWA Journal of Water Science and Technology: Water Supply, Vol. 12 (5), pp Mounce, S. R., Mounce, R. B., Jackson, T., Austin, J. and Boxall, J. B. (2014). Associative neural networks for pattern matching and novelty detection in water distribution system time series data. Journal of HydroInformatics. Vol. 16 (3), pp Furnass, W. R., Mounce, S. R. and Boxall, J. B (2013). Linking distribution system water quality issues to possible causes via hydraulic pathways. Journal of Environmental Modelling and Software, Vol. 40, pp Furnass, W. R., Collins, R. P., Husband, P. S., Mounce, S. R. and Boxall, J. B. (2014). Modelling both the continual erosion and regeneration of discolouration material in drinking water distribution systems. IWA Water Science and Technology: Water Supply, Vol. 14 (1), pp Mounce, S. R., Blokker, E. J. M., Husband, S. P., Furnass, W. R., Schaap, P. G. and Boxall, J. B. (2016). Multivariate data mining for estimating the rate of discoloration material accumulation in drinking water distribution systems. IWA Journal of HydroInformatics. Vol 18 (1), pp Blokker, E. J. M., Furnass, W. R., Machell, J., Mounce, S. R., Schaap, P. G., Boxall, J. B. (2016). Relating water quality and age in drinking water distribution systems using Self-Organising Maps. Environments Journal: Special Issue "Data-Modelling Applications in Water System Management" 3(2), 10; doi: /environments Mounce, S. R., Ellis, K., Edwards, J., Speight, V., Jakomis, N. and Boxall, J. B. (2017). Ensemble decision tree models using RUSBoost for estimating risk of iron failure in drinking water distribution systems, Water Resources Management, Vol 31 (5), pp Mounce, S. R., Mounce, R. B., Boxall, J. B. (2016). Case-based reasoning to support decision making for managing drinking water quality events in distribution systems. Urban Water Journal,Vol 13 (7), pp Mounce, S. R., Gaffney, J. W., Boult, S., Boxall, J. B. (2015). Automated data driven approaches to evaluating and interpreting water quality time series data from water distribution systems. ASCE Journal of Water Resources Planning and Management, Vol 141 (11), pp

5 Whilst UK focused, the research will generate international impact through ability to increase resilience, adaptability, capacity and capability of infrastructure systems in societies undergoing change. Whether that is increases in demand for services because of growth or damage due to environmental change, as well as overarching impacts of climate change. Rapidly developing countries, such as China, are key market opportunities as their infrastructure systems are already experiencing (for example Beijing) or rapidly heading towards many of these challenges. 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. Despite the number of contacts United Utilities receives for discolouration is improving year on year, this number is still high when compared to other UK water companies. This is 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 powerful tools 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 our regulators, Ofwat and DWI and avoid potential fines. United Utilities is a 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. The current nature of water utility network data is that it remains sparse (e.g. not all locations are sampled) and typically is not linked across functions (e.g. water quality data is not linked to hydraulic model data). There is a clearly identified need to collect, manage, analyse and use data from water systems and smart meters more effectively in order to improve water company asset management capabilities especially as automation progresses in the years ahead. Currently data/information access is often limited to secure remote platforms only at the premises of the service provider or data generator or on non-standard and expensive portable platforms. Water companies have substantial databases, but lack the connectivity and methods to extract the full potential benefit. The required blend of foresight and experience means a move towards 'Big Data' solutions and so called business intelligence (turning an organisation's data into patterns that help make intelligent business decisions) in water utilities. There is no proven solution on the market to detect the key water quality metrics at the distribution level. Reducing the 'cost of quality' and the 'cost of monitoring' is challenging because of the remote nature of many parts of the water network and rare co-location with power and communication network supply. Improvements in battery and wireless comms technology enabling increased remote sensing but an additional challenge for water quality (as opposed to pressure or flow) is that these are often electrochemical sensors and require careful calibration and maintenance. These technical challenges are compounded by the

6 CAPEX investment constraints on the utilities. This project will explore new analytics to progress from diagnostic monitoring to prognostics and to explore business model innovation using these analytics in a way that meets the utilities CAPEX constraints. If hydraulic and water quality correlations display periodicities and changes over time, discovering these may reveal valuable information that can be used for network condition assessment and decision making. Importantly, these strategic techniques and operational tools should allow the inference of system state prior to the occurrence of customer-impacting levels of discoloration due to asset deterioration, and thus the proactive selection of intervention options. The technology developed in this project will provide United Utilities with new insights and knowledge into the, water quality, sensing information required to achieve proactive network operation and management. This project will provide United Utilities with an objective and scientifically sound methodology to quantify costs and benefits (i.e., reduced operational and maintenance costs) of the new technology.