Digital twins are central to how AI improves performance in energy-intensive industries. Understanding what they are, how they work, and what they deliver is essential for anyone engaged with industrial decarbonisation.
Artificial intelligence is being deployed across the world’s most carbon-intensive industries. In cement plants, steel mills and chemical facilities, AI systems are delivering measurable reductions in energy consumption and emissions. At the centre of many of the most effective deployments is a technology called the digital twin.
A digital twin is a virtual replica of a physical industrial system. It can model a single piece of equipment, a production line, or an entire plant. The twin is continuously fed real-time data from sensors, control systems and operational databases. It uses this data to mirror what is happening in the physical world and to simulate what will happen next.
For industrial decarbonisation, this capability is foundational. It is the mechanism through which AI can observe, understand and optimise processes that are too complex for conventional control systems to manage with the required precision.
Why Industrial Processes Need a Virtual Mirror
A large cement kiln operates under thousands of interdependent variables: fuel feed rates, clinker chemistry, airflows, temperature gradients and dozens more. Changing one affects many others in ways that experienced operators learn to anticipate over years.
Traditional process control relies on fixed rules and human judgment. These methods are effective, but they leave efficiency on the table. An operator monitoring hundreds of variables cannot detect every subtle inefficiency. A slow-fouling heat exchanger, a compressor drifting off its efficient range, a steam trap beginning to fail: these signals accumulate into significant energy waste.
A digital twin changes what is possible. Running continuously and processing far more data than any human observer can absorb, it identifies patterns and inefficiencies that would otherwise go undetected. Crucially, it allows proposed process changes to be tested virtually before physical implementation. This eliminates the cost and risk of trial and error in complex, high-value systems.
The AI Layer That Makes Twins Intelligent
A digital twin without AI is a model. Its value increases substantially when machine learning is integrated. AI enables the twin to do more than reflect current conditions. It learns from operational history, recognises complex relationships between variables and makes autonomous adjustments at a speed no human controller can match.
Carbon Re’s work in the cement sector illustrates this at scale. Its AI operating system is built on digital twin principles: a bespoke virtual model of each plant, trained on that plant’s own operational data. Analysis published by UNIDO estimates that each plant-level deployment reduces CO₂ by around 10,000 tonnes per year.
In steelmaking, ABB’s AI-enabled process digital twin operates as what one of its engineers describes as an autopilot for production. The system makes real-time adjustments based on the operator’s priorities, whether energy efficiency or output rate. In one project, it helped avoid thermal losses equivalent to around three kilotons of CO₂ per year, while simultaneously increasing production by 24,000 tonnes.
Both examples share a common architecture: a continuously updated virtual model of the physical process, integrated with machine learning that optimises that process in real time. This is what gives industrial AI its practical power.
What Digital Twins Can’t Do Alone
Digital twins are a powerful optimisation tool, but they are not a substitute for deeper technological transitions. Eight hard-to-abate sectors account for approximately 40 per cent of global greenhouse gas emissions, according to a 2025 joint report by IFS and PwC UK. Transforming those sectors will ultimately require hydrogen-based steelmaking, alternative clinker chemistries and carbon capture at scale.
The role of the digital twin is to extract maximum performance from existing assets while those transitions are underway. It bridges the gap between current operations and future technology deployment. The emissions reductions it delivers today are real and material. They also help build the data infrastructure that more ambitious transitions will require.
The System-Level Opportunity Ahead
Most current digital twin deployments optimise a single process or asset. The greater opportunity lies in connecting twins across an entire production system. When virtual models of individual processes are integrated, AI can coordinate material flows, energy consumption and production scheduling at the plant level and, eventually, across multiple sites.
Achieving this requires a step change in data infrastructure. Twins must communicate across systems built on different platforms and standards. The Alan Turing Institute’s ADViCE programme identifies interoperability as one of the central technical challenges the sector must resolve to realise the full potential of industrial AI.
For operators in energy-intensive industries, the immediate message is practical. A digital twin does not require a complete overhaul of existing systems. It is deployed alongside them, learning from their data and delivering value within months. The evidence that the technology works is accumulating. The question now is how quickly, and at what scale, it can be adopted across the world’s most carbon-intensive sectors.