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Markets and Strategies - digital twins for decision support Gareth Lewis UNEXE.pptx

FIWARE
FIWARE

In 2022, the ICT4Water research cluster released an updated Digital Water Action plan calling for enablers supporting data sharing and Intelligent and smart systems deployment. The FIWARE solutions, the NGSI-LD specification and related Digital twin approach for decision support have been identified as key prospects to answer these challenges. This session will summarise the ICT4Water.

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Vienna, Austria
12-13 June, 2023
#FIWARESummit
From Data
to Value
OPEN SOURCE
OPEN STANDARDS
OPEN COMMUNITY
Digital twins for decision
support using the LOTUS
project
Vienna, 12-13 June, 2023 | #FIWARESummit www.fiware.org
whoami?
Vienna, 12-13 June, 2023 | #FIWARESummit www.fiware.org
LOTUS
https://www.lotus-india.eu/
Vienna, 12-13 June, 2023 | #FIWARESummit www.fiware.org
Indian Water Distribution Networks
Vienna, 12-13 June, 2023 | #FIWARESummit www.fiware.org
FIWARE
Output
Service
Context Generation
[input]
Context Consumption
[output]
Context Management
[process]
IoT
Agent
Context
Broker
Output
Service
User
User
Processing
Service
User
Device
Meter
etc
.csv
Vienna, 12-13 June, 2023 | #FIWARESummit www.fiware.org
Synthetic Digital Twin
Context Generation
[input]
Context Consumption
[output]
Context Management
[process]
IoT
Agent
Context
Broker
Visualisation
Service
User
IIG
Leak / Quality
Processing
Simulation
Service
User
EPANET
Model
UNEXE
Leak Detection
/ Localisation
User

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Markets and Strategies - digital twins for decision support Gareth Lewis UNEXE.pptx

  • 1. Vienna, Austria 12-13 June, 2023 #FIWARESummit From Data to Value OPEN SOURCE OPEN STANDARDS OPEN COMMUNITY Digital twins for decision support using the LOTUS project
  • 2. Vienna, 12-13 June, 2023 | #FIWARESummit www.fiware.org whoami?
  • 3. Vienna, 12-13 June, 2023 | #FIWARESummit www.fiware.org LOTUS https://www.lotus-india.eu/
  • 4. Vienna, 12-13 June, 2023 | #FIWARESummit www.fiware.org Indian Water Distribution Networks
  • 5. Vienna, 12-13 June, 2023 | #FIWARESummit www.fiware.org FIWARE Output Service Context Generation [input] Context Consumption [output] Context Management [process] IoT Agent Context Broker Output Service User User Processing Service User Device Meter etc .csv
  • 6. Vienna, 12-13 June, 2023 | #FIWARESummit www.fiware.org Synthetic Digital Twin Context Generation [input] Context Consumption [output] Context Management [process] IoT Agent Context Broker Visualisation Service User IIG Leak / Quality Processing Simulation Service User EPANET Model UNEXE Leak Detection / Localisation User
  • 7. Vienna, 12-13 June, 2023 | #FIWARESummit www.fiware.org FIWARE on Windows Context Generation [input] Context Consumption [output] Context Management [process] IoT Agent Context Broker Visualisation Service User IIG Leak / Quality Processing Simulation Service User EPANET Model UNEXE Leak Detection / Localisation User
  • 8. Vienna, 12-13 June, 2023 | #FIWARESummit www.fiware.org Conclusions ▪ FIWARE as a short-term data hub ▪ Synthetic data generation ▪ Sensor placement optimisation
  • 9. Vienna, 12-13 June, 2023 | #FIWARESummit www.fiware.org Conclusions ▪ FIWARE as a short-term data hub ▪ Synthetic data generation ▪ Sensor placement optimisation
  • 10. Vienna, 12-13 June, 2023 | #FIWARESummit www.fiware.org Conclusions ▪ FIWARE as a short-term data hub ▪ Synthetic data generation ▪ Sensor placement optimisation
  • 11. Vienna, 12-13 June, 2023 | #FIWARESummit www.fiware.org Hosting Partner Keystone Sponsors Media Partners Find Us On Stay up to date Be certified and featured JOIN OUR NEWSLETTER
  • 12. Vienna, Austria 12-13 June, 2023 #FIWARESummit Thanks!

Editor's Notes

  1. Hello everyone, welcome to my presentation. My name is Gareth Lewis and I am a research fellow at the Centre for Water Systems in the University of Exeter in the UK. The work we do at CWS is geared around water systems, so it’s generally working with water companies on distribution, wastewater processing and broader ecological and futuring activities. Generally all projects are EU H2020 style projects, so fairly large projects with lots of partners working across Europe and beyond, in this case India
  2. This presentation is about the LOTUS project, an EU-H2020 funded research project concerned with the development of a low-cost water sensor and associated monitoring and management of water distribution networks in India. Given the nature of research, we didn’t expect the water sensor part of the project to be completed early in the project, so we had a lot of opportunities to do interesting things with monitoring and management of WDNs. Key use cases tend to be measuring and managing water quality and leak detection Leak detection is a huge issue Water distribution networks tend to leak a lot of water Companies wasting resources in processing drinking water that never gets delivered to consumers Cost / environmental concerns
  3. The first consideration for this project is that Indian water distribution is not necessarily like europe’s while we think of european WDN generally being pipes, in India there’s a lot of tanker-based distribution and the project catered for that with developing approaches to install the low-cost sensor into trucks to ensure water quality. However, there’s also a lot of pipe networks and for this project, we UNEXE worked with Indian Institute of Tech in Guwahati on a monitoring and management project. What we wanted to be able to do on this project ideally was use the low-cost sensors in the WDN for leak and quality management. For the period where we wouldn’t have sensors, we developed a synthetic digital twin to operate a water network and create issues on-demand that could be detected and then addressed. - Once the sensors were developed and installed, the monitoring and management aspects could be re-routed to live sensors.
  4. To do this we used FIWARE as our base architecture input process output
  5. Our solution adds a few components FIWARE is the hub A simulation service as the front-end to the IoT Agent to simulate the water network & store results in the broker Our leak detection and localisation as a core process, given that it stores results in the broker The Indian/IIG MATLAB apps as a consumption process that takes synthetic data as its inputs and provides output to users Also, a visualisation service so we can see what’s going on graphically We used EPANET as our simulation source to feed data into FIWARE EPANET is the US environmental protection agencies water distribution network simulator and is a C library that performs hydrological (pressure and flow) and quality simulations on networks over time, typically periods of 15min over multiple days. We had a WDN for Guwahati, so we loaded it up and simulated normal behaviour. The key feature of EPANET is leak injection, which allows us to inject leaks all over the network on-demand In real networks, leaks are often difficult to detect we found from other WDN-based projects, it’s generally hard to get historic leak data, which makes developing leak detection algorithms difficult, especially with data-driven / big data-based approaches having leaks on-demand makes this a lot easier
  6. We set up our system on Windows command line rather than linux as it was our partner’s preferred environment Used ‘raw’ python libraries rather than Dockerised containers Indians developed their solutions with a MATLAB framework We developed in Python FIWARE data accessed through HTTP using appropriate libraries (requests) Decision support here: Once leaks can be detected, they can be addressed Data -> information -> knowledge Data from epanet Add context to create information And the leak processing gives us knowledge That operators can then use to make informed decisions about the state of their water network And what to do to address issues
  7. FIWARE as a short-term data hub This is unlike all the other FIWARE projects we’ve undertaken where we have been looking at FIWARE for long-term data management solution, say 1 yr+ of data Working with the synthetic digital twin was geared around fairly short bursts of simulation and testing Maybe several hours of real-time use Translates to a c1month of actual data Use case: Load the WDN Define sensors for the run Step through the simulation and store results to FIWARE Create leak(s) Step through the simulation and store results to FIWARE Run leak detection algorithms to see what happens Restart as necessary
  8. Synthetic data generation Clearly, very much geared around synthetic data operating with same NGSI-LD/v1 (HTTP) interfaces to live system Lift and shift from dev to ops For ‘R’ projects like this, need to be able to generate 3 types of data on-demand Proof-of-concept to make sure the right data turns up in the right place Really useful as an introduction to FIWARE for new staff Can see this with the Stellio beehive examples edge-cases on-demand to detect failure / issues In our case, leak injection In other projects has ‘just’ been ‘out of range’ data or take devices offline, but key for creating events that require decision support bulk data to demonstrate throughput Moving from test cases to real cases Demonstrate timely processing of large amounts of data Efficient HTTP calling We've found that often algorithms fail on scale, so adding scale early can address this. Key feature, was that simulations were 100% repeatable Same steps, same outcomes Key for building solutions
  9. Sensor placement optimisation Key part of decision support in working out optimal sensor placement to minimise cost / disruption of installation and maximise leak discovery effectiveness We have a digital twin with quantitative outputs, we can measure what happens and control it Ideal environment for genetic algorithms Thank you