How to build a Smart Water Network A fictional case study Haggai Scolnicov CTO, TaKaDu Ltd. Goals • Introduce layered model of Smart Water Networks • Discuss current and future evolution of SWNs • How smart is “smart”? Numbers and notions • Mention TaKaDu enough to satisfy our VP Marketing • Hollywood ending 2 Meet Mr. Waters Mr. Waters, VP Water Supply at Typical Water Co., has decided to make his network smart! Why? - Increase efficiency (water, energy, manpower, $$$) - Be in control and proactive - Leverage existing IT and skills - Improve customer service 3 What is a Smart Water Network? And what is it good for? Data-driven components to help manage and operate the physical network of pipes etc. Decision support Feedback & Automation Reporting Monitoring & alerts Evidence-based planning Remote control 4 Pipe dreams The physical network of Typical Water Co. • • • • 100,000 service connections (25Mm3/yr) 1000 km of distribution mains Some trunk mains, reservoirs, pumps, PRVs Many valves and hydrants And probably much more… • Built for monitoring? Some DMAs, PMAs… 5 Taking the cake in SWN technology The physical layer may have many clever inventions in it – but they don’t make it “smart” or more data-enabled We need sensors! Physical layer 6 Senseless? The sensing and control layer • Hundreds of sensors + loggers on mains (e.g. flow & pressure in DMAs + installations) • Supply metering exists! • AMR? Or stick with monthly manual? • Acoustic? Pipe inspection? Mobile loggers…? Investment in sensors: $100-5000 a year for total cost of ownership 7 Taking the cake in SWN technology Data just piles up at multiple, remote data loggers. It is collected and used in models and planning a few times a year. It is usually too stale for operational use. We need telemetry! Sensing and control Physical layer 8 Failure to communicate? The communications layer • SCADA (exists on production-side) • Transmit every day? 4 hours? 15 minutes? (Conserve communications costs, battery longevity…) • Sample and log every 15 minutes • Remote control for valves? Pumps? • High frequency sampling to see transients? Investment in telemetry: $X M + $500 per sensor per year ? 9 Taking the cake in SWN technology Operators see data from hundreds of sources and control devices remotely. Some real-time usage! But how to organise and inspect all this data? And what about data from other systems (like asset management)? We need data management! Collection and communication Sensing and control Physical layer 10 Data, data, everywhere The data management and display layer • Off-the-shelf and tailored applications: – – – – Network data applications (operator / analyst) Water loss monitoring Hydraulic modeling and network planning Evidence-based reporting… • Interfaces with GIS, Asset Management, Workforce Management… • Some rule-based automation! • Applications reach multiple departments 11 Taking the cake in SWN technology Analysts and engineers use recent sensor data through systems But is this the best we can achieve? This was supposed to tailored to water distribution, together with workforce, asset, and be a smart network. With all these computers, couldn’t they other data, routinely initiating timely action. Reporting and help my staff make sense of the data? planning made easier and better grounded in fact. Data management & display Collection and communication Sensing and control Physical layer 12 Drowning in numbers? The data fusion and analysis layer • Real-time data-driven pressure management • Energy optimisation • Monitoring for water-loss and other anomalous events • Applications reach many departments, new users, field crews, management… 13 Taking the cake in SWN technology Data fusion and analysis Data management & display Collection and communication Sensing and control Automatic systems analysePhysical data from multiple sources, intelligently layer monitoring for faults, controlling remote devices for optimal operation, supporting decisions and network & business planning 14 TaKaDu – Water Infrastructure Monitoring Sensor data Sensor data Maintenance log GIS SCADA and Collected archiver data Other Systems 1 FTP / HTTPS Categorised Alert Generator events Data Cleaning Algorithmic Engine Event Generator 15 TaKaDu – Water Infrastructure Monitoring Tracked events Timely repairs USER INTERFACES Maintenance log GIS SCADA and archiver Other Systems 1 FTP / HTTPS Categorised Alert Generator events Data Cleaning Algorithmic Engine Event Generator 16 Which all boils down to… 17 Hollywood ending? 18 Are we smart yet? Some challenges to cake bakers • How accurate? How reliable? How effective? • Do the systems work together, reusing data and findings? • Data quality – garbage in, garbage out • Cost (cheaper data  more data  better results) • Added value to consumers (not just cost and availability) 19 Smart Water Networks are only as smart as the people who operate them • “Smart Water Users” • New work processes and skills! • Co-evolution of technology and processes 20