Redefining energy infrastructure: The game-changing impact of digital twins
The energy industry is standing at the intersection of innovation and necessity. As the backbone of economic growth, energy infrastructure comprises a vast array of components, including gas pipelines, oil refineries and offshore oil and gas platforms. This complex network ensures a steady energy supply to power industries, homes and services. However, it faces several significant challenges reshaping the industry's future.
One of the most pressing issues is aging infrastructure. Many energy facilities are decades old and becoming increasingly prone to inefficiencies and potential failures, particularly as they near their life expectancy. There is also the risk of these aged facilities being shut down altogether, which would profoundly impact not just energy production but also the communities and individuals who rely on these facilities for their daily needs. Service disruptions and safety concerns are real risks, and they escalate with each passing year of operation for these aging facilities. Simultaneously, the upkeep and replacement of aging infrastructure require extensive resources, adding to the strain.
Adding to these challenges is the urgent need for a transition to net-zero emissions. With the Paris Agreement and national commitments pushing for a significant reduction in greenhouse gas (GHG) emissions, the energy industry must work to reduce emissions and increase operational efficiency without drastically compromising output.
Finally, the industry is grappling with growing societal demands for safer operations. The energy sector has a broad environmental and social footprint; any incident can have substantial ecological and human impacts. As such, there is growing pressure from communities, regulators and investors for energy companies to prioritize safety and risk management.
UNLEASHING THE POTENTIAL OF DIGITAL TWIN TECHNOLOGY
In this context of aging assets, environmental concerns, safety imperatives and shifting societal expectations, the industry actively seeks innovative solutions to manage and overcome these challenges. One such solution is digital twin technology, where Akselos has emerged as a frontrunner, Fig 1. Akselos’ advanced technology creates precise virtual replicas of physical systems, enabling real-time monitoring and predictive maintenance, thereby addressing critical operational efficiency, safety and sustainability issues. This technology stands to revolutionize how we—as an industry—manage and maintain energy infrastructure in the coming years.
The underlying technology of this innovation is Reduced Basis Finite Element Analysis (RB-FEA) simulation software. RB-FEA is a product of 15 years of meticulous research and development in the Massachusetts Institute of Technology (MIT) mechanical engineering labs, and it was funded by the U.S. Department of Defense.
RB-FEA builds upon legacy Finite Element Analysis (FEA), which has long been recognized as a valuable tool in engineering and manufacturing. This numerical method allows for predicting how complex systems behave under various loads and boundary conditions. However, due to complex, time-consuming workflows, the traditional FEA approach can be cumbersome and somewhat limited when applied to large, intricate systems.
RB-FEA is Akselos' solution to this problem. Using a reduced order modeling (ROM) approach, RB-FEA simplifies the mathematical representation of the analyzed system while maintaining accurate results, which brings several main advantages.
The first advantage of RB-FEA is its speed, coupled with a guarantee of accuracy. This is because the number of degrees of freedom (DOF) involved in an RB-FEA simulation is typically 1/1,000th of an equivalent FEA solve. Such a reduction makes the analysis notably faster, resulting in a substantial speedup compared to the traditional FEA. While other Reduced Order Modelling (ROM) methods—such as machine learning or response surface methodology—can offer similarly speedy results, RB-FEA stands out by providing both rapidity and precision.
This assured accuracy comes from accuracy indicators, which identify whether an RB-FEA solve meets a specified accuracy threshold. If this threshold is not attained, the RB-FEA ROM can be further enriched until the required standard is met, thus ensuring accuracy.
Another substantial benefit of RB-FEA is its scalability to large models. The same DOF reduction that brings about the above speed-up also permits solving much larger models with RB-FEA than would be practicable with traditional FEA. Akselos has showcased this by generating RB-FEA models equivalent to over 100 million FEA DOFs, solvable within mere seconds.
Finally, the parametric nature of RB-FEA allows for a more detailed analysis. Parameters, such as material properties, geometry, loads or temperature, can be adjusted quickly without redoing the entire model. This feature enables users to update a model in real time, to match sensor measurements or efficiently explore a whole design space by solving thousands or millions of different model configurations. Thus, RB-FEA isn't just a reduced order model; it is a parametric reduced order model, significantly enhancing the depth and adaptability of system analyses.
USING RB-FEA TO CREATE DIGITAL TWINS
To build digital twins, engineers first build a detailed model and then pair it with data, including inspection and sensor data. This information is then fed into the model to create a digital twin. There are three main types of digital twins: virtual, process and structural.
Virtual digital twins are the simplest type of digital twins used to replicate a structure or asset. Creating a virtual digital twin involves adding a layer of modeling to a computer-aided design (CAD) model and tagging areas of interest. Data from these areas of interest are then used to update the digital twin.
Process twins build on virtual twins to enable the digitalization of entire processes. Almost every engineering process and any chemical process of any scale goes through a digital mapping process, using process digital twins before completion. As a result, this type of digital twin is the most mature and has been used for the longest time.
Akselos builds structural digital twins, which combine the power of RB-FEA simulation software with advanced sensor technologies and data analytics. The result is a precise virtual replica of the physical system—a digital twin that allows engineers to monitor and manage complex systems in real time.
The structural twin isn't just a static model. Thanks to continuous data feeds from sensors on the physical systems, the digital twin is constantly updated to reflect the system's current state. This gives operators a reliable, up-to-the-minute snapshot of system performance and the ability to detect and address anomalies early.
The digital twin technology delivers tangible benefits to the energy infrastructure sector. It allows for improvements in speed, scale and accuracy. With its ability to handle complex systems rapidly and precisely, the technology increases process efficiency and throughput while reducing downtime. These benefits directly address the need for enhanced operational efficiency, and the technology can be particularly advantageous for managing ageing assets, which may be more prone to failures and inefficiencies.
Perhaps even more critical in many scenarios is the safety enhancement that digital twins offer. By providing real-time monitoring capabilities, potential issues can be identified and addressed before they become serious safety hazards. This ability to preemptively address problems is crucial in an era where safety and environmental stewardship are paramount.
SITE-WIDE ADOPTION
The power of this technology allows for developing digital twins that span an entire site, generating an interconnected web of precise digital replicas. By creating digital twins of these multiple assets, Akselos brings cutting-edge technology to the heart of the energy infrastructure, Fig. 2.
The profound understanding of systems, made possible by these digital twins, aids in creating a holistic view of the site. With detailed digital twins for each asset, operators can comprehend how each component interacts within the broader system. This deeper level of knowledge supports the identification of potential inefficiencies or failure points, thereby improving overall operational performance.
Utilizing a site-wide approach to digital twin technology also leads to the efficient distribution of resources. With the accurate digital representation of each critical component, site managers are empowered to strategize their resource allocation for maintenance or expansion. Companies can optimize their productivity and profitability by prioritizing resource deployment, based on real-time data from digital twins.
Maintenance becomes proactive and predictive with digital twins. Continuous analysis of real-time data from each asset—including cokers, blowers and pressure vessels—allows for the early detection of issues. Resolving problems before they lead to costly repairs or potentially dangerous failures yields significant cost-savings, enhances safety and minimizes unplanned downtime.
Communication and coordination are streamlined across all teams, thanks to interconnected digital twins. This network of digital twins provides easy sharing of data and insights among stakeholders, fostering efficient collaboration.
Beyond immediate benefits, a site-wide application of digital twins supports continuous learning and improvement. Through ongoing data analysis, patterns and trends can be recognized, revealing areas for improvement. This dynamic approach to learning empowers organizations to evolve their operations to keep pace with shifting conditions and demands, underpinning long-term operational efficiency and sustainability.
In essence, while digital twins are representations of individual assets, a site-wide application of this technology brings forth its full potential. Akselos' technology can create a complete digital echo of the operational site, enabling strategic, proactive and data-driven management of energy infrastructure.
REAL-WORLD APPLICATIONS: FROM LNG TERMINALS TO REACTORS
The digital twin technology has demonstrated significant value across diverse applications in the energy sector, showcasing its versatility and effectiveness in addressing unique operational challenges, Fig. 3.
For example, Akselos has joined forces with Adriatic LNG, to implement the digital twin technology and optimize Adriatic LNG's regasification terminal off the Italian coast in the Adriatic Sea. The terminal contains four Open Rack Vaporizers (ORVs), integral components in the regasification process. Seawater runs through the heat exchanger tubes within these ORVs, turning LNG into gas, leading to considerable strain on the pipes, due to temperature variations. If this issue isn't appropriately addressed, it can result in the failure of the ORVs, causing substantial downtime. The gravity of this situation is further highlighted when considering that replacing a single ORV can take up to two years.
To address this issue, Akselos deployed its RB-FEA technology to create a precise digital twin of an ORV. The digital twin incorporated various data sources, including drawings, inspection data and load history, enabling a thorough Fitness for Service (FFS) assessment. An intuitive dashboard identified key issues, thereby providing clear guidance for maintenance and significantly reducing the risk of unnecessary downtime.
In another case, a North American operator faced a problem with heavy-walled reactors' thermal cycling, a process which could take days and lead to weeks of lost production annually. The operator was using Minimum Pressurization Temperature (MPT) curves and temperature ramps set decades ago to start up and shut down their reactors. These general thermal ramps did not consider each reactor's unique attributes and often overestimated the required time. The operator sought a solution for a more detailed assessment and optimization of the start-up and shutdown process.
Responding to this challenge, Akselos utilized its engineering simulation software to build a structural digital twin of one of the reactors. The digital twin conducted a stress and fatigue analysis across the asset by integrating data from temperature/pressure sensors and historical performance data. The resulting analysis was displayed on an easy-to-use operations dashboard, providing actionable information for the operator to optimize and adjust thermal ramps during different operating windows. The result was striking: the digital twin found a safe way to reduce the start-up and shutdown time of the reactor by over 25%, boosting plant uptime significantly.
These real-world examples underscore the potential of digital twin technology to drive operational efficiency and reduce downtime across the energy industry and its infrastructure. Whether improving the performance of ORVs at an LNG terminal or optimizing the thermal cycling of reactors, this technology offers innovative solutions to some of the most pressing challenges seen by the sector.
LOOKING AHEAD
In conclusion, the energy industry confronts significant challenges, including aging infrastructure, environmental pressures and the necessity for safety. Digital twin technology, spearheaded by Akselos, provides a viable solution. This technology, based on RB-FEA simulation software, enables real-time monitoring and predictive maintenance, addressing operational efficiency, safety and sustainability.
These digital twins aid in identifying inefficiencies in aging assets, offer safety enhancements through real-time monitoring, and streamline resource allocation and maintenance strategies. These capabilities are crucial to addressing the outlined industry challenges.
As the sector evolves towards net-zero emissions and heightened efficiency, solutions like Akselos' digital twins will be pivotal. The ability to optimize energy infrastructure aligns with the industry's pressing needs, positioning this technology as a significant contributor to a more sustainable, efficient, and safe energy future.
