Systems Engineering for Water Management A decade of Water Information Network collaboration
Outline • Water management? • An information infrastructure • From data to model • Control • Ongoing work & challenges
UNESCO World Water Reports 2003-2005-2009 • “Water management crisis” – Water efficiency < 50% – 55 to 60% of “easy” water is in use • Water consumption pressures – Equity – Industrialisation – Irrigation expansion (70% of all water usage) – Climate change – Population growth • No change scenario, the world runs out of “water” in 2025
• >30% of extracted water is unaccounted for • 1B people have access < 10l/day 2B people have access < 50l/day • human activity → climate change • Economic & physical water scarcity Lake Chad 1972 Lake Chad 2007 National Geographic
UNESCO World Water Report 2: Water scarcity estimate China, India, USA, Australia all face serious challenges
Climate change in action? Human impact in action Victoria= 1/3 Texas Murray-Darling Basin = 1.5 Texas
Dam evaporation ≈ 8% • Efficiency < 50% • Over-irrigation leads to soil degradation Dam release 100 • Accountability? Metering error ± 20% • Seepage ≈ 5 Channel • Evaporation ≈ 5 to farm • Outfalls ≈ 5 consumes • Outfalls ≈ 15 • Conservative 30 • Seepage ≈ 15 management ≈ 15 • Plants ≈ 40 Farm gate to plant consumes 30 Plants store (1%) • Low energy footprint 0.4 • More productive land • 50% of all farm profits
24h between Manual on site operation adjustments Ordering delay > 3 days No 25 Poor regulation (30cm fluctuation in water level) Drop bar structure OHS issues No 35 06/12/2001 Dethridge Meter Wheel +/-20% accurate No 49 Manual undershot gate to farm 02/05/2002 Manual overshot gate
Summary • Water is a limiting resource • Water usage efficiency <50% • Irrigation accounts for 70% of all water usage • Irrigation – gravity fed open channel distribution system – (semi)-manually operated (reservoir-channel-gate) – policy based, open loop, exploitation regime – poor information (infrastructure) – materially unchanged since the hanging gardens of Babylon (700BC)
A pool, gates, datum Pool = canal section between gates ν Length varies from 1km – 10km − Off take i 1 flow u i ν Off take close to y , − y , pivot point i u i 1 d i flow u + i 1 Water level for no flow y , u i Canal slope ≈1/10,000 g y Pivot point , + i d i 1 Datum level may not be g + i 1 unified across system
The FlumeGate™ • Water tight • Self cleaning • Low head loss • Precision manufacturing – Accurate & repeatable measurements – Flow actuator & meter – Precision control – High duty cycle • Radio based internet • 1 PC on board • Patented technology
Information Infrastructure On-farm - Water levels, gate positions, flows ad-hoc 2.4kb/s links F1 network at all regulators and farm off takes - Soil moisture, plant response and on-farm irrigation actuation - Radio internet along channel 1-3km (hop number <8) (<10km) Repeater - Ad-hoc network on farm 19.6kb/s 4 Fs - Event based sampling & actuation - Not fully integrated across farm gate (demonstrators only) 10-30km On-farm ≈100km 2.4kb/s ad-hoc Line of sight links F2 network
Water Information Network Regulator Channel System Commercial main Gate Central node Water managed from reservoir to plant Information feedback loop from the “crop” perspective moderated by the “overall system” perspective On farm experimental Farm nodes
Water level & flow monitoring & control Central Goulburn 2004-2006 New ICT Main canal infrastructure (retrofitted) Gate (design & Minor canal calibration) patented Replaces Dethridge Automated wheel operation, both on canal and to farm To farm
Information Infrastructure • Target water efficiency & on-farm profitability – Reliable, quantitative information – Real time water balance creates a water market for buying and selling of water in real time – Water-on-demand – Reduce ordering delay (better on-farm management) – Improve water level regulation (better farm land command) • Build a data based dynamic model – Enable short term prediction (say over a week) – Enable feedback based control – Inform long term policy • Scale, retrofitting, expandability
Modeling • Pool = from up-stream flow to down-stream water level, with a downstream flow disturbance • View overall system as a concatenation of such models (generic structure for all flow distribution systems • Inputs u , outputs y , disturbances v u u y y − − i 1 i i i 1 . . . . . . - - + Pool i - 1 Pool i + d d − i 1 i ν ν − i 1 i
The Art and Science of Modeling: from data to model Utility The aim of modeling: (short term prediction) For a given set of data (= data complexity), there is an optimal (= best utility) model complexity Model complexity Data St-Venant optimal data complexity – complexity models optimal model complexity
Grey Box Modelling World-wide patent granted mass balance + waves Inflow Outflows ( ) = σ = − τ − − ν p ( ) y ( t ) k u ( t ) u ( t ) ( t ) (leaky) integrator + i i i i i 1 i + = Off-take lightly damped u ( t ) f ( geometry ) on pool i i i pole pair ν ( t ) u i ( t ) i y 1 ( ) t − i u i + 1 t ( ) Inflow y t ( ) i Outflow τ delay across pool i i
St-Venant Equation Model • In 1871 a 74y old St-Venant proposed a PDE (now called a 1D-Navier-Stokes) expressing conservation of momentum & mass with (viscous) friction in un-steady flow 1797-1886 • Boundary conditions = hydraulic characteristics of gates (need system identification techniques, scale issues!) • Must identify “friction” and “geometry” from data to make a predictive model (non-linear terms) (hard work) • 100+ year old model; first “real canal” models tested in 2000; first labo-canals tested/verified in 1971 Brutsaert
Continuity equation along the channel ∂ ∂ A Q + = 0 ∂ ∂ t x Momentum equation along the channel ∂ ∂ ∂ ( ) 2 Q gA Q A 2 Q Q + + + + − = gA S S 0 ∂ ∂ ∂ f 2 t B A x A x − − Cross sectional area Flow A Q − − Width of channel at water surface gravity B g − friction slope and mean bed slope S f , S Boundary conditions for inflow and outflow, off - take
24.03 Simulated models, compared with data 3 pool canal section 24.01 PDE (with estimated parameters) 23.99 Water level (mAHD) 23.97 Third order Validation data set 23.95 First order 23.93 23.91 Wave period ≈ 10min Delay ≈ 3.3min 900m downstream Time (min.) 23.89 200 220 240 260 280 300 320 340
Modelling for Automation • Grey box models are preferred – 3 rd order model suffices ( <10km pools) – Models validated across complete flow regime over 4 irrigation seasons (set point regulation!) – Model structure validated, easy to tune against data, both in closed and open loop identification mode – Physically relevant parameters easily recognised (delay, dominant wave frequency, dominant time constant) – Scales well
An Open Question in Model Order Reduction Data (flow and levels) 3 rd order grey box model St Venant equations + + regulator characteristics regulator characteristics ? a) nonlinear PDE models Model order b) closed loop validation reduction water level regulation, guided model validation
Summary Model • Simple input/output models suffice • “Grey box model” – Water balance, waves, boundary conditions (gate characteristics), delay time – Simulates well, over extended periods of time (week) • Models can be tuned from operational data
Decentralized Structure Preserving Control Control Control at gate at gate i - 1 i u u r r . . . . . . − − i 1 i i 1 i + + - - y y − i 1 i - - + + Pool i - 1 Pool i d d − i i 1 ν ν − i 1 i Regulation, rejecting disturbances, suppressing waves NON-TRIVIAL CHOICE of PAIRING VARIABLES
TCC™ - Filtering waves - Gate characteristic downstream flow FF inversion demand - Anti-windup reference FB Water level Local error Downstream Gate Pool i Local datum line Off-take
Total Channel Control™ • Total Channel Control™ industrial size implementations since 2002, over 2000km of canal in operation • Improvements – Distribution efficiency ≈ 90 % up from ≈70 % (CSIRO audited) – Copes easily with start/stop events (rain) • Other benefits – Water leak detection – Water flow accounting; (balances to about 2% accurate); enables real time water market – Water application totally different (farmers adapt, and obtain better on-farm outcomes) – Better regulation implies a higher water set point is feasible = more (flow) capacity and more land that can be irrigated within the same infrastructure IFAC World Congress 2005 29
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