The concept of digital transformation is taking the world by storm. It’s rare to see an enterprise unenamoured or unimpressed with technology-enabled possibilities and opportunities. Imagine having real-time visibility to assets halfway across the world through turnkey infrastructure security solutions.
Indeed, levels of implementation vary among businesses and across industries. However, non-implementation is typically a function of several factors, including shortage of technical know-how, insufficient capital, lack of necessary infrastructure, and concerns about returns, instead of an absence of any desire to use technology to improve business strategy and operations.
Take digital twin technology, for example. It holds much promise and is, in fact, one of the most transformative digital tools available to organisations and enterprises today. However, it’s not currently universally implemented.
That said, the global digital twin industry is growing at a brisk rate of 39.8% compound annual growth rate between 2024 and 2032. Therefore, even if not all businesses use it now, many enterprises expect to adopt digital twin technology eventually.
What Is a Digital Twin?
A digital twin is a virtual representative of a physical object, system or process. They are revolutionising industries by allowing real-time simulations and predictive analytics.
It is fed real-time data, has programs that run simulations, and is integrated with artificial intelligence and machine learning capabilities to continually improve its modelling algorithms so that, over time, it can better predict and simulate its real-life counterpart’s responses.
By continuously receiving and analysing data, a digital twin can provide insights into how the mirrored physical asset operates, predict potential issues (and when they’re likely to occur) and suggest or even implement measures to improve the asset’s real-life performance.
The Four Types of Digital Twins
Digital twins vary in their scope. The four key types of digital twins below have unique functions and applications.
1. Component Twins
Component twins are virtual representatives of physical parts or components. When you think of a digital twin, you probably think of a digital recreation of a specific piece of equipment. Component twins go much smaller than that. For instance, a component twin mirrors not a subsea pump (the equipment) but its pressure vent valve (a part or component of that equipment).
Why are component twins important?
Component twins are highly useful for tracking the wear and tear of individual physical components, identifying the factors affecting their longevity, and predicting when every specific component will require a replacement.
Component digital twins are, therefore, essential to predictive maintenance. They predict when a particular component will reach the end of its useful (or efficient) life, so you can replace it before it causes production or operational issues.
2. Asset Twins
An asset twin is a digital twin that integrates several component twins into a larger system. Twins of products and equipment can be considered asset twins. Using the previous example, a subsea pump asset twin can mirror the entire subsea equipment by integrating the twins of the pump motor, blades, vents, and all other parts.
Another example is the digital twin of a wind turbine. The asset twin models the entire turbine, including its blades, rotor, and gearbox, collecting real-time data on wind speed, vibration, temperature, and overall performance instead of merely simulating and getting real-time data on specific components.
By monitoring the digital twin of their wind turbine assets, energy companies can assess how well their wind turbines are functioning, predict potential failures, and optimise maintenance schedules. The asset twin helps a company adjust its operation for varying wind conditions, improving energy output and extending the turbine’s lifespan. This leads to more efficient energy generation and reduced maintenance costs.
Why are asset twins important?
Asset twins allow companies to track and monitor physical assets in real time. You can simulate various conditions and see how the asset will perform. Asset twins also provide information on how individual asset components work when paired or interacting with other components.
In the context of predictive maintenance, asset twins allow you to simulate individual components’ actual working conditions – that is, in the context of the whole in which they operate. An artificial intelligence program can even compile and analyse the predicted maintenance requirements of various components to create an optimal asset maintenance schedule that will minimise downtime and maintenance costs (including opportunity costs).
3. System or Unit Twins
System or unit twins model entire systems rather than individual assets. The digital twin of a power plant is an example.
A power plant twin models the entire system of interconnected assets, such as turbines, boilers, generators, transformers, fuel supply systems, cooling systems, control systems, etc. It can provide a real-time, holistic view of the physical power plant’s operation.
Why are system twins important?
Using a power plant digital twin, you can simulate different scenarios, such as varying fuel loads, changes in energy demand, or equipment performance under extreme weather conditions. This helps optimise your overall energy output, enhance efficiency, reduce fuel consumption, and minimise downtime. The twin can also still predict potential equipment failures, enabling predictive maintenance, which helps avoid costly unplanned outages.
4. Process Twins
Process twins virtually simulate end-to-end workflows. They are the most advanced and complex type of digital twins, and implementing them requires the expertise of a system integrator.
A natural gas company’s process twin can model its entire workflow, from extraction, transportation, combustion, power generation, and grid delivery. This enables it to track various systems as a cohesive whole.
The process twin can track gas delivery from suppliers, ensuring that gas arrives when needed to meet demand without consuming resources for excess storage. If there’s a delay in gas supply due to a pipeline issue, the twin can show, in real time, this delay’s impact on downstream processes, like generation capacity and scheduling.
As the process twin simulates the conversion of chemical energy to mechanical energy (via turbines) and then into electrical energy, it can identify inefficiencies, such as how fuel quality affects combustion performance and energy output. This allows plant operators to adjust settings for optimal conversion efficiency throughout the process, not just at individual stages.
Since the process takes a holistic view, it can simulate making changes in individual systems to achieve business goals. If demand spikes, it can simulate increasing fuel intake, boosting turbine speeds and making other adjustments that will ultimately lead to higher production.
Why are process twins important?
This type of twin is particularly useful for streamlining processes across complex systems and ensuring optimal interaction between multiple stages of production and business operations.
Start Using Digital Twins
Digital twins are revolutionary, transformative technology that can benefit any industry. You can implement them at the component, asset, system and process level to minimise downtimes, boost efficiency, maximise value-capture, and improve workflows.