WATER & WASTEWATER
Industry 4.0 & IoT What is a digital twin? And how can it be used in the water sector? Professor Annie Bekker – research chair at Rand Water and professor in the Department of Mechanical and Mechatronic Engineering at Stellenbosch University – elaborates on the standard definition and its potential use. By Kirsten Kelly
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t took me a very long time to understand what a digital twin actually is and to many people, a digital twin is still a foreign concept. It is commonly defined as a digital representation of the state and behaviour of a real asset within its operational context towards decision support. But the best way to explain the meaning of a digital twin is to use examples,” says Bekker.
A digital twin example: pumps A digital twin can function on various levels. Looking at a pumping station of a Rand Water distribution network, a digital twin can be: • a single component – an impeller or a seal of a pump • a system – an assembly of components like the pump itself • a system of systems – a pump station with multiple pumps and several pumping stations connected to supported pipelines or an entire water distribution network. “When designing a pump, an engineer will evaluate different elements such as how changing the number of impeller blades or blade angle will affect the pump performance (e.g. flow and discharge pressure). A model is created; the pump is then manufactured and sold to a customer and that model is never used again. With a digital twin, one can look at the model and the real outcomes (such as the flow rate of water), so you are entangling a model with the real-life operation of an asset. Engineers can unhinge the benefits of a model beyond the design
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phase by using it in the operational phase as well,” adds Bekker. Data-driven digital twins can also allow engineers to take shortcuts where the geometrical detail is no longer modelled. “Going back to the example, an engineer may only be interested in the input-output relationship of water pressure in the pump. A model can be generated from data that is measured while the pump is in operation in different conditions. Therefore, an engineer would not have to go back to the design of the pump and use specialised software to make detailed engineering representations. They can use mathematical models to simulate the performance of the pump and create a black box model to create input-output relationships through techniques such as machine learning,” explains Bekker. Data-driven modelling is especially advantageous to assist in decision support in applications with low risk, where a wrong prediction would not result in a catastrophic result such as loss of life or ethical ramifications. It calculates quickly, is cost-effective and does not require domainspecific knowledge.
real asset deviate from the expected response and an inspection is triggered. Additionally, certain standard failures of a machine can be modelled and used to create a catalogue of possible signal attributes under such conditions. These patterns in measurements can then be used as an early recognition system from a catalogue of possible errors. A digital trend in the water sector is the use of existing hydrological models of a pipeline network in complement with sensor feeds on the real network to measure information at key points. Anomalies are found by comparing what the model reveals is happening in the network to what is revealed by the sensor feeds.
Detecting anomalies Another advantage is that digital twins can be used to detect anomalies. This can be done by looking at differences in the behaviour of the asset predicted by the model as opposed to its actual performance. A model can be used to generate a hypothetical ‘virtual sensor’ feed for normal or expected behaviour. An anomaly is detected if sensor feeds from the
Professor Annie Bekker – research chair at Rand Water and professor in the Department of Mechanical and Mechatronic Engineering at Stellenbosch University
