Water Productivity and the Role of Automated Water Control Technologies Professor Peter Scales

Size: px

Start display at page:

Download "Water Productivity and the Role of Automated Water Control Technologies Professor Peter Scales"

Beryl Carson
6 years ago
Views:

1 Water Productivity and the Role of Automated Water Control Technologies Professor Peter Scales Department of Chemical and Biomolecular Engineering Deputy Dean, School of Engineering Director, Australia-China Water Centre Leader, Water Theme, Carlton Connect Initiative

2 Collaborators Zhilei Zheng Hang Zheng Zhongjing Wang Tsinghua University Patrick Wang Rubicon Su Ki Ooi Dominic Skinner Eric Weyer Iven Mareels Yongping Wei Andrew Western University of Melbourne

3 Australia-China JRC Reduce delivery losses Water across regions Reduce water use Channel Control ET and use caps On-Farm Control WRM Pollution Ident. Water Trading Water recycle Tipping points Realize water benefits Pollution reduction

4 Outline World Water Issues Improving Water Productivity Case study: Transmission Losses Case Study: On-Farm Losses Automation versus Intuition Conclusions Photo credit: Michael Kai

5 World Water Issues Sustainable Development Goals assessment shows we are getting worse Situation 60% of easy (run-off) water is in use 95% of all river basins are severely over-exploited Meat rich diet is increasing water demand x3-10 Distribution of water in time and space is uneven A 40% difference between demand and easily accessible supply is predicted by 2030* Need to increase water productivity by at least 100% * Water Resources Group, 2030 Report: Managing Our Way To Scarcity

6 Water and Development Water is essential for development Developed societies demand high water quality and use >1000 m 3 /person/year (>70% is used for irrigation) Country *Water available per capita (m 3 ) *Water Use Per capita (m 3 ) Irrigation distribution efficiency (%) On-farm water use efficiency (%) Overall water use efficiency (%) Australia 25,000 1, China 2, India 1, <50 <20 * China Country Water Resources Partnership Strategy, World Bank, 2013

7 Water and Development Water is essential for development Developed societies demand high water quality and use >1000 m 3 /person/year (>70% is used for irrigation) Country *Water available per capita (m 3 ) *Water Use Per capita (m 3 ) Irrigation distribution efficiency (%) On-farm water use efficiency (%) Overall water use efficiency (%) Australia 25,000 < China 2,100 < India 1, <50 <20 * China Country Water Resources Partnership Strategy, World Bank, 2013 Can we achieve our development goals with 500 m 3 /person/year

8 Outline World Water Issues Improving Water Productivity Case study: Transmission Losses Case Study: On-Farm Losses Automation versus Intuition Conclusions

9 Modernised Philippines Japan Cyprus Australia South Korea Malaysia Taiwan France Austria USA Spain Columbia Germany Mexico Portugal China Egypt Greece Italy India Distribution losses in open channel systems around the world 40% 45% 57% 60% 54% 50% 10% 13%16% 24% 30% Sources: United Nations FAO and International Institute for Land Reclamation and Improvement (1990) as cited by Land & Water Australia and Coleambally Annual Report

triple available water Un-Automated Modernised/Automated 70 70 60 50 40 30

10 % Losses % Losses Distribution losses in open channel and on-farm Since 70% of water is used for irrigation, potential is to more than double and even triple available water Un-Automated Modernised/Automated Total On-Farm Conveyance Total On-Farm Conveyance

11 Outline World Water Issues Improving Water Productivity Case study: Transmission Losses Case Study: On-Farm Losses Automation versus Intuition Conclusions

13 Irrigation systems are large scale e.g. Goulburn-Murray Water district 21,000km 2 irrigated land; 14,400 serviced properties 6,000km of major irrigation canal; 6,000 main flow regulating control points 2.7Gm 3 per annum allocated, $15B produce at farm gate (30% Au) IFAC World Congress

No 25 Day - week between adjustments Drop

Manual on site operation Ordering lead time

efficiency 70% Dethridge Meter Wheel +/-20%

14 No 25 Day - week between adjustments Drop bar structure Sumerian technology today Manual on site operation Ordering lead time > 3 days Travel time 30 days Poor water level regulation (±30cm) Poor accountability Oversupply is the norm Low efficiency 70% Dethridge Meter Wheel +/-20% accurate Manual overshot gate Manual undershot gate 02/05/2002 to farm

15 Modernisation - Shepparton Irrigation Region ( ) >3000 km 2 irrigated agriculture 960 canal gates 1630 metered farm outlets 60/650 km of canal renovated On-line water ordering Wireless control network

16 Flume Gate Up and downstream water level sensors (ultrasonic transducers) inclinometer PC, radio, manual interface, solar tracker Battery Housing, cabling Motor, gear, cable assembly Backlash eliminated Magnetic pick up Temperature sensors Leaf angle 120 deg Gate closed 10 deg Gate open 90 deg (all determined at install) radius 0.6m 3m Magnetic inserts (precision located) 33 and 66 degrees (calibration) Variable width 0.6m 3m

17 Upstream transducer Downstream transducer (4MHz) Ultrasonic wave guide: wave reflects off reference and water to determine accurate water level (+/- 0.05mm)

18 Sensor & Actuator: Flow = F(geometry) Sound wave travel time (4) angle encoder Magnetic pick up (2) Temp sensor(2) Inclinometer +/- 100ns +/- 1 LSB 16 bit +/ degree +/- 1 K +/- 1 degree Gate type ( determines F) Output refresh period 0.02s Filtering over s to min Differential water level +/- 0.1mm gate tip position +/- 0.1mm Flow; error < 5% and < 0.6 max flow alarms Add controller to linearize

19 mass balance + waves or (leaky) integrator + lightly damped pole pair yi () 1 t Grey Box Modelling (Linear System!) p( ) y ( t) k u ( t ) u 1( t) ( t) i u ( t) f ( geometry ) i u i Inflow (t) i i i Inflow i i y() t i (t) i Outflows i u i 1 ( t) Off-take on pool i Outflow Local datum! i delay across pooli

Total Channel Control Internet-of-Things for Water Management Internet of sensors & actuators at all structures Wireless networked (Motorola Canopy), solar powered, 1PC-equivalent per gate An idea of

20 Total Channel Control Internet-of-Things for Water Management Internet of sensors & actuators at all structures Wireless networked (Motorola Canopy), solar powered, 1PC-equivalent per gate An idea of system complexity GMW 6,000km 20k sites; 20k+ flow measurements; 6k water level constraints, 10k model states; 300k control states; 400k variables to define system; 2.4 M tracked asset variables; 8 messages (event based)/per hour/ per site (700 M messages over one season, 200Gb) Overall software 50% complexity of a SAP 100 times Windows 100k function calls (1k for model & control & signal processing)

21 Outcomes Transmission efficiency improved to 90% Zero outfall and leakage around service points Low water level variations ( 2 cm) 75% of the recovered water returned to the river (required) An ability to identify system losses on-line: leaks, theft, over-supply Accurate and reliable flow to farms Water ordering <one day, usually <one hour Better on-farm lifestyle Equity of supply across the entire irrigation region.

22 Shule River Basin, Changma Irrigation Region, Gansu, China

23 Discharge (m 3 /s) Discharge (m 3 /s) Changma Irrigation Region, gate control tests sp sp :00 8:30 9:00 9:30 10:0010:3011:0011:3012:0012:3013:0013:30 Time of day (hours) 0.0 8:00 8:30 9:00 9:30 10:0010:3011:0011:3012:0012:3013:0013:30 Time of day (hours) Potential is to save 20-40% of diverted water through automated conveyance but introduce all the other benefits

24 Outline World Water Issues Improving Water Productivity Case study: Transmission Losses Case Study: On-Farm Losses Automation versus Intuition Conclusions

25 Farm Case Study (830 hectares maize) 45 automated bays (400x50m) Fast flow flood irrigation 2 metered farm inlets Soil moisture sensors In-bay water movement sensors (height and flow) Web hosted integrated control system linked to supply system On farm control On farm Zigbee network Farmer PC Farmer Mobile

26 Outcomes Delivery efficiency improved to 95% (from around 75%) 20% less water use Number of irrigations events reduced from 10 to 5 <1% outfall and 3% drainage below root zone Water delivery matched to plant needs Reduced plant stress because of reduced water-logging or drying Increased yield from 17 to 27 tonnes/hectare Vastly improved farm lifestyle, less labour and remote operation

Changma Irrigation Region, on-farm irrigation control tests Pole for AgNode (meter & solenoid) Pole for AgNode (Echo probe 1) Pole for AgNode (Echo probe 2) 1.5 m AgNode 1.5 m AgNode 1.5 m AgNode 0.

27 Changma Irrigation Region, on-farm irrigation control tests Pole for AgNode (meter & solenoid) Pole for AgNode (Echo probe 1) Pole for AgNode (Echo probe 2) 1.5 m AgNode 1.5 m AgNode 1.5 m AgNode 0.5 m To outlet Meter Actuator 0.5 m Underground 0.5 m From pump Moisture: Echo Probe Jun 0:00 30 Jun 0:00 10 Jul 0:00 20 Jul 0:00 30 Jul 0:00 09 Aug 0:00 19 Aug 0:00 29 Aug 0:00

28 Outline World Water Issues Improving Water Productivity Case study: Transmission Losses Case Study: On-Farm Losses Automation versus Intuition Conclusions

29 Productivity changes (south east Australia) In canal: 70 to 90 % delivery efficiency On-farm: 75 to 95% delivery efficiency, 65 to 85% overall Overall: 45 to 76% efficiency improvement 1.27 times improvement in output or less than 50% water use times improvement in production Overall (including agronomy) 2.6 times improvement in output or less than 30% water use

30 Conclusions Automation and web-hosted technologies have the potential to significantly influence water security by changing water productivity (by up to 400%) and changing choices on how we want to use water Its not about efficiency, its about less water diverted per tonne of product and more options (greater equity) for water use. These improvements cannot be achieved through intuition and models. The response time scale is too short, number of variables too great and the system too large Matching demand, supply and total integration is essential (+caps, markets, rights) The whole is greater than the sum of the parts.

Global experience of automating irrigation systems

Global experience of automating irrigation systems Sumith Choy 07 Apr 2016 Agenda 1 Irrigation 2 3 4 5 6 Automation benefits Automation enablers SCADA vs Autonomous Control Case study - On farm productivity