© 2013 ibm corporation ibm research ‘big bets’ in sustainable technologies: smarter water...

© 2013 IBM Corporation

IBM Research ‘Big Bets’in Sustainable Technologies:Smarter WaterManagement

April 2013

Sherif El-Rafei, Business Development Executive, IBM Research Middle East & [email protected]

mailto:[email protected]

© 2013 IBM Corporation2

Smarter Planet/Smarter City


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


Smarter Water Management Overview


Smarter Water Management means enabling higher levels collaboration and innovation across value chains and ecosystems

NaturalWater

Sources

RawWater

Transport

CleanWaterSupply

ConsumersSewage

Treatment

Recycled/Treated

Supply Demand Control

Regulation Climate Change

IntelligenceInfrastructure

Environment

Engagement


We Work at Three “Scales”

Utility scaleWater quality and usage Discharge, combined

sewer overflowAsset management“Smart levees” and

levee monitoring systems

Weather event assimilation

Energy management

Natural scale Water resource mapping

and availabilityWater quality monitoring

and management (surface and subsurface)

Land use analysisExtraction monitoring

(surface and subsurface)Flood control

Enterprise ScaleWater usage tracking Water quality control

(into and within plants, discharges)

Supply chain optimization

Energy managementBusiness process

improvementsMetrics and

management


Smarter Water Source Management


Hydrogeosphere – an Integrated Computational Modeling Framework

Weather / Climate / Atmospheric modeling

Ocean ModelGroundwater 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


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 andsensor 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


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)


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 2010Local flooding leading to mudslides, killed over 200 people

and left 15000 homelessWidespread 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


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?


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

2km

“You don't get points for predicting rain. You get points for building arks.” (Lou Gerstner)


Nowcasting (Sensors)

Deep Thunder Remote

Near-real time revision Fine-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 ProvidersForecast 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)3 018-7272-168

Continental to Global Scale

Local Scale

In Situ

Local Scale

Short-Term Weather Event Prediction and Observation


Command Center for Rio de Janeiro


The Importance of Real-Time Coastal Awareness

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


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


CODAR

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.


Smarter Water Distribution Management


A Measurement and Modeling Technology PlatformManagement 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


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]

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. 209-217.4) 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 80-7204-197-5

– 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


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


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

Operational Plan

annual cost

failure rate

replace

repair

Periodic inspectionStrategic replacement in 2, 5, and 10 years Efficient use of crew and equipment

Usage / Smart Meters

Architecture Demo

Business Innovation


Integrated Water Management


Strategic Water Information Management Platform


Water Resource Management


Strategic Water Information Management (SWIM) Platform

Visualization layer

Applications layer

Models layer

Data content layer

Network layer

Data handling layer

Sensing layer

(Op

en) s

tand

ards

Sec

urity

“An integrated set of technologies, data and tools”

Business rules layer

Energy data

Geology/ hydrology

Economic

Climate

Environment/Ecology

Quality

Quantity/Flow

Run-off

Usage and Discharge

Dat

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© 2013 IBM Corporation31 February, 2013

Thank You

Merci

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Environmental Analytics Platform

Factories, Bridges, Refineries, Airports etc.

Vineyard


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


LMT for Agriculture ApplicationsWhat 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


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


• 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


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.


Research’s Strategic Disciplines

Exploratory

Systems

TechnologySoftware

Industry Solutions

Business Analytics &

Math. Sciences

Services

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