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© 2013 IBM Corporation IBM Research ‘Big Bets’ in Sustainable Technologies: Smarter Water Management April 2013 Sherif El-Rafei, Business Development Executive,

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Presentation on theme: "© 2013 IBM Corporation IBM Research ‘Big Bets’ in Sustainable Technologies: Smarter Water Management April 2013 Sherif El-Rafei, Business Development Executive,"— Presentation transcript:

1 © 2013 IBM Corporation IBM Research ‘Big Bets’ in Sustainable Technologies: Smarter Water Management April 2013 Sherif El-Rafei, Business Development Executive, IBM Research Middle East & Africa

2 © 2013 IBM Corporation Smarter Planet/Smarter City 2

3 © 2013 IBM Corporation Reimagining how science and technology can have impact Fighting infectious disease by spreading data Improving communication by talking to the Web Creating drinking water by filtering oceans Managing human impact on rivers by streaming information Reducing traffic jams by creating them Helping premature infants by sensing complications before they happen Reimagining the energy grid by synchronizing supply Reducing CO2 while boosting business efficiency Mapping beneath the seafloor to help reduce the risk of dry holes

4 © 2013 IBM Corporation 4 Smarter Water Management Overview

5 © 2013 IBM Corporation Smarter Water Management means enabling higher levels collaboration and innovation across value chains and ecosystems Natural Water Sources Raw Water Transport Clean Water Supply Consumers Sewage Treatment Recycled/Treated SupplyDemandControl Regulation Climate Change Intelligence Infrastructure Environment Engagement

6 © 2013 IBM Corporation We Work at Three “Scales” Utility scale  Water quality and usage  Discharge, combined sewer overflow  Asset management  “Smart levees” and levee monitoring systems  Weather event assimilation  Energy management Natural scale  Water resource mapping and availability  Water quality monitoring and management (surface and subsurface)  Land use analysis  Extraction monitoring (surface and subsurface)  Flood control Enterprise Scale  Water usage tracking  Water quality control (into and within plants, discharges)  Supply chain optimization  Energy management  Business process improvements  Metrics and management

7 © 2013 IBM Corporation 7 Smarter Water Source Management

8 © 2013 IBM Corporation Hydrogeosphere – an Integrated Computational Modeling Framework Weather / Climate / Atmospheric modeling Ocean Model Groundwater model Hydrological model Water Cycle Watson hydrological model basin model Deep Thunder Water Cycle Water Quality (Measurement Management Technology) Large River Basin Simulation New Insights come from integration of multiple disciplines

9 © 2013 IBM Corporation  Total number of reaches: ~3,900  Number of pour ports: ~1,800  Total length: ~15,000 km  Modeled by 131K nodes, two unknowns at each node (depth and velocity). 262K unknowns solved at each time point Phase II - Large River Basin Simulation  Cooperation between IBM Austin Research Laboratory & University of Texas.  Full scale simulation of the Guadalupe River. – Demonstrating a predictive model with ~100X speedup.  Availability of geographical and sensor data is crucial to success.  Eventual goal: Mississippi River. – About 80X larger than the Guadalupe.  Width of each segment represents depth  The color represents flow velocity  Red: high velocity  Blue: low velocity

10 © 2013 IBM Corporation Subsurface (Hydro-geological) Flow Model  Variable-scale using unstructured (tetrahedral) meshes  Time-dependent, model-based subsurface flow modeling  Can be coupled with the surface flow model  Model solved using: Locally conservative multiphase (water, air) Numerical model based on Control-Volume Finite Element discretization Can include geo-mechanical effects of elastic/plastic aquifers, and topography and density driven flows Transient temperature effects, fracture and faults can be specified Numerical kernel extensively used in basin modeling (scalable to from millions to billions of cells)

11 © 2013 IBM Corporation  Coastal storm with heavy rains (up to 284mm in 24 hours) starting at about 1700 BRT on 5 April 2010 – heaviest recorded compared to the previous 48 years  One of the most significant global weather events of 2010  Local flooding leading to mudslides, killed over 200 people and left homeless  Widespread disruption of transportation systems (e.g., road closures, airport and rail delays)  Rio de Janeiro mayor Eduardo Paes admitted that the city's preparedness for heavy rainfall had been "less than zero," but added "there isn’t a city that wouldn’t have had problems with this level of rainfall." 5-6 April 2010 Flooding Event

12 © 2013 IBM Corporation  A mathematical model that describes the physics of the atmosphere –The sun adds energy, gases rise from the surface, convection causes winds  Numerical weather prediction is done by solving the equations of these models on a 4- dimensional grid (e.g., latitude, longitude, altitude, time)  Complementary to observations (e.g., NWS weather stations)  Solution yields predictions of surface and upper air –Temperature, humidity, moisture –Wind speed and direction –Cloud cover and visibility –Precipitation type and intensity What is Weather Modelling?

13 © 2013 IBM Corporation Match the Scale of the Weather Model with the Client’s Needs  Capture the geographic characteristics that affect weather (horizontally, vertically, temporally)  Ensure that the weather forecasts address the features that matter to the business 2km  “You don't get points for predicting rain. You get points for building arks.” (Lou Gerstner)

14 © 2013 IBM Corporation Nowcasting (Sensors) Deep ThunderRemote Near-real time revisionFine-tune approach based upon extrapolation from Doppler radar and satellite observations Forecast for asset-based decisions to manage weather event, pre-stage resources and labor proactively Forecasting (Modelling) NWS / Commercial Providers Forecast for longer-term planning where decisions require days of lead time, but may not have direct coupling to business processes Time Horizon for a Local Weather Event (Hours of Lead Time) Continental to Global Scale Local Scale In Situ Local Scale Short-Term Weather Event Prediction and Observation

15 © 2013 IBM Corporation Command Center for Rio de Janeiro

16 © 2013 IBM Corporation The Importance of Real-Time Coastal Awareness 16 Tracking pollutant dispersion Monitoring/managing coastal agriculture and industries Managing maritime operations Protecting coastal cities Our vision: coastal awareness, weather prediction and flood prediction in concert to protect citizens, infrastructure, and the environment Protecting our environment

17 © 2013 IBM Corporation Realtime Coastal Awareness Collaboration with National University of Ireland, Galway Objective: Real-time prediction of bay conditions (quality and circulation patterns) for environmental decision support Challenges: –Noise and uncertainty in measurements –Model scale Methodology: –Data assimilation for real-time modelling –HPC implementation CODAR = high frequency radar for water surface speed

18 © 2013 IBM Corporation CODAR HF radar for water speed CODAR adds to wealth of sensors in Galway Bay –Smart Bay tidal gauges and flow measurements –Sonars for water velocity at varying depth –Two weather stations Ideal prototyping environment CODAR project infrastructure `Assimilation of 10GB / hr.

19 © 2013 IBM Corporation 19 Smarter Water Distribution Management

20 © 2013 IBM Corporation A Measurement and Modeling Technology Platform Management Environment Integrated Modeling Environment Smart Sensor Bus Measurement Platform General Technology platform to deliver physical intelligence for smarter planet applications by leveraging state of-the-art metrology, a broad set of models and unique controls to different length & time scales of the physical world

21 © 2013 IBM Corporation Leakage & Pipeline Failures…  Water losses reduction – More than 32 billion cubic meters of treated water is lost annually through distribution network leaks [1] – A conservative estimate of the total annual cost of water loss to utilities worldwide is US$14 billion [1] – According to IWA, 15%~30% water is leaked [2]  Public image improvement – 250~300 pipe bursts per year in Trondheim City, Norway [3] – About 900 leakage per year in Hong Kong. [4] 21 Source: 1)From Bentley company 2) “Water Industry: Managing Leakage”. Engineering and Operations Committee, UK. 3)Jianhua Lei and Sveinung Segrov, Statical approach for describing failures and lifetimes of water mains. Wat. Sci. Tech. Vol. 38, No. 6, pp ) Hong Kong Water Supplies Department Annual Report (2008) 5) A Lambert, (2001) What do we know about pressure-leakage relationships in distribution systems? IWA Conf. n Systems approach to leakage control and water distribution system management. Brno, Czechoslovakia. ISBN –15%~30% water leaking in the world [2] – 900 leakage/burst per year in big cities [4] May 25, 2010, pipe burst at Beijing JingGuang Bridge causing a 5-hour water supply disruption and severe traffic jam in the business center

22 © 2013 IBM Corporation 22 Addressing Non-Revenue Water using Analytics and Optimization 22 Leakage or Theft Detection at the Residential Level Leakage Reduction using Dynamic Pressure Control Optimal Valve Placement for Pressure Reduction Understand usage patterns and detect anomalies for low and high consumption to detect leakage, theft or faulty meters Create optimization model to adjust the pressure dynamically so that only the required flow will be supplied yielding cost reduction in energy and water achieved. Find “optimal” location of leak(s) to explain difference between actual measurements and model predicted measurements Use an optimization model to find the optimal number of valves, and their location, so as to enable the most effective pressure management Leakage Detection at the Network Level using optimization

23 © 2013 IBM Corporation

24 Asset Lifecycle planning enables informed operational and strategic decision support Risk Estimation & Prediction Failure History Environmental Attributes Spatial Coordinates Asset Attributes Failure Impact Asset Condition Assessment Infrastructure Network Relationships Replacement Cost Estimation {Labor, material, service interruptions, …} Maintenance Cost Estimation Backup Assets {Labor, routine disruptions, cost, material, ….} Decision Support Operational Budget Capital Budget Business Constraints Strategic Plan Operationa l Plan annual cost failure rate replace repair Periodic inspection Strategic replacement in 2, 5, and 10 years Efficient use of crew and equipment Usage / Smart Meters ArchitectureDemo Business Innovation

25 © 2013 IBM Corporation 25 Integrated Water Management

26 © 2013 IBM Corporation

27 Strategic Water Information Management Platform 27

28 © 2013 IBM Corporation Water Resource Management 28

29 © 2013 IBM Corporation Strategic Water Information Management (SWIM) Platform Visualization layer Applications layer Models layer Data content layer Network layer Data handling layer Sensing layer (Open) standardsSecurity “An integrated set of technologies, data and tools” Business rules layer Energy data Geology/ hydrology Economic Climate Environment/ Ecology Quality Quantity/Flo w Run-off Usage and Discharge Data types (as examples) (from multiple sources and systems)

30 © 2013 IBM Corporation 30

31 © 2013 IBM Corporation 31 Februar y, 2013 Thank You Merci Grazie Gracias Obrigado Danke Japanese English French Russian German Italian Spanish Portuguese Arabic Traditional Chinese Simplified Chinese Hindi Tamil Thai Greek Ευχαριστώ Mulţumesc Romanian Dziekuje Polish شكرا Teşekkür ederim Turkish

32 © 2013 IBM Corporation 32 Environmental Analytics Platform Factories, Bridges, Refineries, Airports etc. Vineyard

33 © 2013 IBM Corporation 33 Low-Power Mote Technology (LMT)  LMT—a wireless data gathering technology  A general IBM wireless sensor platform – Highly robust and scalable sensing solution – Forms Mesh Network  World’s lowest power consumption – 5 to 7 year lifetime with two AA batteries  Very flexible and modular design  Sensors can be located with +/- 3 feet  Environmental sensing: – Temperature and Humidity – Soil Moisture and Temperature – Sun light / irradiation – Dew point – Pressure, Air flow – Carbon dioxide – Presence and Occupancy – Corrosion and Air quality – Location What are the benefits ?  Means to maintain soil moisture while minimizing water usage for irrigation  Prevent frost and/or fungal damage  Alarm workers to take measures to save crops.  Predicting local frost damage  Determine optimum harvest point  Optimize crop growing and food processing  Improved asset and operational management

34 © 2013 IBM Corporation 34 LMT for Agriculture Applications What can we monitor ? Soil temperature Soil moisture Air temperature Humidity Sunlight ….. pH ? What would like to measure which you cannot do today ? What are the benefits ? Means to maintain soil moisture while minimizing water usage for irrigation Predicting local frost damage Alarm workers to take measures to save crops. Determine optimum harvest point Prevent frost and/or fungal damage Optimize crop growing and food processing Improved asset and operational management

35 © 2013 IBM Corporation 35 Soil Moisture Detection – Full field and large- scale IR imaging Less moisture IR camera Semi-spherical mirror [1] Data from Iven Mareels’ IBM presentation in January 2011

36 © 2013 IBM Corporation 36 Total of 35.3 acres over three fields in Eastern New York 95 motes supporting 475 sensors Soil temperature Air temperature Soil moisture Humidity Light Data streamed back into a central gateway every 2 s Software Solution allows remote monitoring and control Deep Analytics Moisture Modeling Time Series Forecasting Optimization Statisical Correlation …. Example – Crop Growing

37 © 2013 IBM Corporation 37 Example - Fungal Disease Detection Phytophthora is a fungal disease in potatoes, which depends on temperature, humidity and whether the leaves are wet. Extensive wireless sensing system in the Netherland measures air pressure, temperature, relative humidity and illumination System alerts farmers of patches within his fields which are most susceptible and can be used to gauge the steps that need to be taken.

38 © 2013 IBM Corporation Research’s Strategic Disciplines Exploratory SystemsTechnologySoftware Industry Solutions Business Analytics & Math. Sciences Services


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