Can Digital Twins Transform Singapore’s Built Environment?
August 04, 2021
Singapore was quick to embrace Digital Twin technology, investing SGD 73 million to create a digital replica of the entire city as far back as 2014 [1]. While the benefits of the technology have been seen in pilot projects, it has yet to fully deliver on the promise of improving the built environment. Vague and conflicting definitions, tech jargon, ambiguity around specific use cases, high costs of implementation, and lack of training have been a few of the major challenges impeding the adoption and full utilisation of Digital Twin technology. Companies need to move beyond the proof-of-concept phase, with carefully targeted support demonstrating the Returns on Investment on that only Digital Twins can produce.
Simply put, a Digital ‘Twin’ is a digital model of an actual physical element, which can be anything from the human body or a jet engine, to a building or an entire city. What makes Digital Twins novel and exciting is the connection to the real-world; Digital Twins incorporate the data from their physical twins in real time to model real-world processes. Recent developments in sensors, batteries, wireless communications, and processors have enabled the collection and processing of large amounts of data, fuelling the growth and excitement around Digital Twins.
The built environment has been at the forefront of Digital Twin adoption. Building Information Modeling (BIM) software has now become mainstream as a tool to help design, build, and operate buildings. Digital Twins create value over and above what BIM can do, in that they (i) Extend coverage beyond a single building, to a network of interconnected buildings; (ii) Bring added capabilities of incorporating real-time data such as from Internet-of-Things (IoT) sensors; and (iii) Feature simulations of dynamic factors (e.g. complex user behaviours such as personal settings for ambient conditions, hybrid work preferences, or responsiveness to behavioural nudges) to rapidly test hypotheses, solve problems, and support optimisation.
New possibilities therefore open up across the Built Environment value chain. For example, Digital Twins can help developers plan smart solutions at the estate level, such as district cooling, waste management, mobility networks, or the use of renewable energy. Analytics from sensor data can help facilities management firms up-sell predictive maintenance, especially for high-stakes assets such as lifts in Grade A office buildings. The ability to run physics-based models to accelerate the ageing process can help architects and engineers test and select different materials.
According to a UN report, buildings and construction account for 39 percent of global carbon emissions. Governments and organisations are focusing on reducing the embodied carbon in buildings and the operational emissions [2]. Certification programs, such as Green Mark and LEED; net-zero commitments; and governmental policies are some instruments used to improve the built environment by reducing energy and water consumption and waste. Digital Twins are a promising technology to accelerate sustainability across the built value chain with better, more efficient developments that improve the experience of users at lower environmental costs.
The operation of buildings has traditionally tended towards being more reactive, with a mindset of addressing problems when they arise. Not many buildings have prioritised proactively managing energy consumption, water use, and waste, over other performance indicators such as improving tenant feedback or minimising breakdowns. As buildings increasingly adopt new technologies and equipment, Digital Twins can be leveraged to significantly improve operations and reduce resource use, such as by interpreting real-time data from an existing building with AI, to create a dynamic, accurate model of the building. This model can then be used to test improvements and changes before designing renovations or adjusting operating parameters to optimise resource use. Such an approach can help building managers reduce the environmental impact of building maintenance and operations, and enable greener facilities which produce less waste and consume less energy and water.
Applying Digital Twin Technologies
Most firms across the built environment can easily use Digital Twins to improve on three priority areas of water, energy, and waste management. Currently, most buildings still use traditional sub-metering systems, with individual meters on different floors and units connected to a master meter. Utility managers typically conduct manual readings of the sub-meter every month. These traditional sub-metering devices are unable to log consumption trends over time as they only provide figures at the point of readings. The ability to detect faults and the opportunity to identify energy saving measures are therefore limited due to insufficient continuous data. Sub-metering technology is thus less effective as a proactive decision-making tool.
Reducing Energy Consumption of Campus Buildings
However, there are growing examples of firms investing in Digital Twin technology to enhance their energy management. For example, Nanyang Technological University (NTU) commissioned IES, an environmental services company that conducts integrated performance-based analysis, to deliver a 3D masterplanning and visualisation model, along with virtual testing and building performance optimisation, for NTU’s 250-hectare flagship Ecocampus [3].
IES implemented its Intelligent Communities (ICL) technology, built on the principles of Digital Twin technology, to provide high-level visualisation and analysis of testbed energy reduction technologies on a 3D masterplanning model. The model was used as a baseline to simulate and analyse these different technologies ranging from lighting sensors to chiller optimization, and smart plugs that turn off equipment in certain hours. The best technologies from this testing phase were then selected and applied to buildings on site.
From this project, NTU was estimated to potentially realise up to SGD 4.7 mn in financial savings stemming from a 31 percent energy saving potential. Such an example demonstrates the financial gains that firms stand to realise by investing in Digital Twin technology in their building operations.
Optimising Water Reclamation Processes
Higher up in the value chain, firms such as utility companies are also investing in Digital Twin technology to manage water and waste treatment. For example, Singapore’s PUB [4] created a Digital Twin of the Changi Water Reclamation Plant to improve its infrastructure performance and to enhance the plant’s water quality, as well as optimise its energy and chemical consumption.
One of the primary functions of the Digital Twin for PUB was to create forecasts of the plant’s hydraulics, controls, and processes. This is based on actual measured data that is collected from the plant’s operations, which is then compared against the Digital Twin’s model predictions of these different facets. Such data enables PUB to pre-emptively highlight areas which require attention from operators and maintenance staff.
For example, a warning will be issued if one of the primary effluent online ammonia probes differs significantly from the others in the model, or if the laboratory primary sludge total solids do not match up with the model results. For the latter, a check of the primary sludge flow meter or confirmation of the laboratory results would be required. Such an anticipatory approach to building management reduces resources that can otherwise be redirected to other channels, ultimately enhancing sustainability efforts.
Forecasting Flooding and Water Quality Issues
In the municipality of Porto, Portugal, water and waste company Águas do Porto was contracted to forecast flooding and water quality issues, improve water supply services and city responsiveness, and ensure resilience of water infrastructure [5]. Singapore faces increased flood risks from rising water levels but has yet to invest its Digital Twin technology efforts on flooding and water quality forecasting. Porto can serve as an excellent example of how a smart city might integrate Digital Twins into managing its water supply and its wastewater, stormwater, beaches, and bathing water systems.
To operationalize this, the city of Porto collected data from more than 20 sources including customer service management, billing, maintenance, asset accounting, operational systems, and GIS. They also integrated data from sensors, remote management and 30,000 telemetry devices. The Digital Twins provided digital representations of all water systems in the city and as well as three meteorological models; combined sewer and storm models for the sea front; estuary, coastal area, and wave models; and forecasting models. The twins provided access to all this information in real-time and were used to produce forecasts and to automatically update boundary conditions from water consumption and network sensors, run network scenario analysis for pipe bursts and valve and pump shut-downs, and to publish flows, velocity, water level, meteorology, and currents.
Collectively, these efforts resulted in operating gains of 25 percent, reduced water supply failures by around 30 percent, and duration of pipe bursts repairs by eight percent. The immediacy of sensor readings, improved decision making, and increased stability has increased reliability of data to almost 99 percent.
Conclusion
- Virtual Singapore. Retrieved from https://www.nrf.gov.sg/programmes/virtual-singapore
- (UN Environment and International Energy Agency (2017): Towards a zero-emission, efficient, and resilient buildings and construction sector. Global Status Report 2017. Retrieved from https://www.worldgbc.org/news-media/global-status-report-2017
- IES Website, NTU Singapore Project (2020). Retrieved from https://www.iesve.com/ntu-singapore
- Valverde-Pérez,B., Johnson, B., Wärff, C., Lumley, D., Torfs, E., Nopens, I., & Townley, L. (2021) Digital Water: Operational digital twins in the urban water sector: case studies. Retrieved from https://iwa-network.org/wp-content/uploads/2021/03/Digital-Twins.pdf
- Digital twins for managing water infrastructure, Water World, April 1, 2020. Retrieved from https://www.waterworld.com/water-utility-management/smart-water-utility/article/14173219/digital-twins-for-managing-water-infrastructure