AI-powered digital twins are about to change the face of industrial ecosystems

As the energy transition creates immense and growing tensions between the imperatives of security, affordability and sustainability, action on energy efficiency is crucial to achieve climate and development goals. The good news is that acting on energy efficiency represents a huge potential net gain: Actions on energy demand, including improving efficiency, could reduce energy consumption by up to 31%, saving up to $2 trillion per annum by the end of this decade and preventing vast amounts of emissions.

Governments worldwide are under pressure to revise their ambitious energy transition goals and strengthen the collaboration with the industrial sector for more effective and responsible solutions for their high energy demand. In support of these efforts, the World Economic Forum has built a community of 21 ambitious industrial clusters around the world, where co-located companies optimize the use of shared resources and infrastructure, reducing overall energy consumption.

In this effort to bolster energy efficiency without sacrificing productivity, digital twins are emerging as a pivotal technology.

Digital twins, or virtual replicas of physical assets like factories, buildings, roads or electric grids are a promising technology that enables companies to collaborate effectively, share data and optimize four key areas:

Production: Identify bottlenecks, predict maintenance needs and fine-tune processes for maximum efficiency.

Waste: Gain real-time insights into material usage and energy consumption, leading to economic benefits as well as greener and more sustainable operations.

Profits: Make data-driven decisions that unlock hidden potential and improve product quality.

Emissions: Achieve sustainable operational targets across the entire industrial cluster by monitoring and minimizing emissions.

Digital twins have been around for some time but have only recently been able to provide end-to-end capabilities, using AI to drive further optimization. Elevating this technology and adopting it to a cluster level would further reduce emissions and enhance the performance of the entire industrial ecosystem. This, however, requires new systems for governance and collaboration, interfacing and data sharing.

Each company in an industrial cluster collects an enormous amount of data from the operation of its physical assets. This ranges from design and energy use of the building itself to all activities performed, such as manufacturing and logistics. Traditionally, this data was isolated in different silos, hindering decision-making and profits. An integrated digital ecosystem could change that.

The first step towards developing a holistic digital ecosystem is by enabling the connection between silos. This can be done by leveraging the digital twin technology infused with industrial AI insight. At building- or factory-scale, this means that the data from different processes, such as heating ventilation and air conditioning systems, can create a matrix between real-time physical data and past experiences to develop predictions for self-maintenance and indoor comfort. This data is communicated to the grid in real-time, meaning that the system can adjust power consumption and avoid overloading the grid, thus contributing to stabilizing frequency and reducing congestion and renewable curtailment. This is particularly relevant given the increased electrification of industrial assets and the pressure on grids to seamlessly accommodate an increased electrical input from renewables

The second step is giving access to unified, trusted information and insights that span the entire company, for instance through an industrial intelligence platform. This approach ensures that companies benefit from a single source of truth, available in real-time, that is shared by their entire team. This maximizes value by fostering an environment of trust through collaboration, facilitating the discovery of partners and resources and coordinating efforts towards common goals. A great example is how US power leader Dominion Energy opened up a new revenue stream by sharing data collected from across their network with their end customers, helping them validate their sustainability commitments.

Building on those steps companies will be able to create a central nervous system for all their collocated assets, by compiling building with macro data, such as energy prices and demand, environmental patterns, and more. The next milestone is connecting multiple companies in an industrial cluster with each other.

While the digital twin technology is yet to be implemented at a cluster level, some pioneering projects at a product, building, company and city scale provide insight into exciting future opportunities.

One technology to look at is the Green Digital Twin, an application calculating a product’s environmental footprint using information of the entire supply chain including sourced parts, tools and devices. The Green Digital Twin not only enables transparency of the current or future environmental footprint (and easy calculation at an early stage), but also allows for maximum flexibility in designing parts for repair. This flexibility also includes re-manufacturing with a low environmental footprint while maintaining material and process requirements. This measuring tool can help track and help mitigate emissions incurring across the supply and production chain, including those in clusters.

Grid-interactive buildings can be another lesson for industrial assets. Grid-interactive buildings (including industrial sites) use Internet of Things and AI to actively manage energy consumption. By adjusting energy usage based on real-time data, they optimize their interaction with the grid. This flexibility helps to reduce imbalances and bottlenecks caused by intermittent renewable energy sources on the grid side, while unlocking new revenue streams on the buildings side. Once implemented in every building in a cluster, this means, by default, optimized energy management across the overall infrastructure. For the regional and national level, this can be a game-changer. In the US, this system is expected to provide about 40% of the expected additional renewable-based capacity by 2030. This will unlock annual grid investment savings up to almost $12 billion, or about 8% of the annual North American (US & Canada) spending on grids in 2030.

Finally, digital twins coupled with AI-infused solutions enable global multi-discipline teams to work in a shared data-centric environment, to connect engineering, design and simulation in a single database and to optimize project design and delivery. This allows companies to bring bigger projects to market faster than ever before. This technology can also improve prediction and forecasting of environmental threats. One example is how the City of Salem in Oregon keeps drinking water safe using a digital twin technology empowered with AI.

As the world is transitioning towards a multi-modal multi-fuel future, the need for an integrated approach forward has never been greater. By scaling available technologies, digital twins can build networks across all companies within an industrial cluster, unifying data from every corner of their operations to ultimately reduce emissions, identify the potential for upskilling and job creation and contribute to the local economy.

The development and implementation of digital twins in industrial clusters represents a transformative leap towards achieving greener, more efficient and collaborative industrial environments. By addressing the challenges and harnessing the capabilities of digitalization companies, industrial clusters can not only meet but exceed the environmental and economic goals set forth by global stakeholders. That’s good for business and good for the planet.