The aftermath of COVID-19 has reshaped the landscape of asset management, particularly in heavy industries like refineries and manufacturing plants. With this challenge comes an opportunity to drive a paradigm shift regarding how plant operations are managed.
Industrial facility owners and operators are now grappling with several challenges, like global supply chain disruptions that may impact “run and maintain” operations, while striving to meet growing production demands. What keeps them up at night are the risks they cannot see, particularly when it comes to unexpected asset issues and possible long-term equipment damage.
To help manage the lifecycles of all assets in an industrial facility, organizations are looking for innovative solutions to optimize their equipment performance while ensuring safety and operational reliability. This includes data integration and standardization and implementing digital twin technology that provides predictive asset maintenance analytics.
What is Digital Twin Technology?
Today, engineering and design teams use dynamic process simulation models to view the current state of plant operations. This allows them to quickly pinpoint operational bottlenecks, unit shortcomings, design limitations, and the adequacy of relief systems. However, it takes advanced machine learning (ML) capabilities to model and predict the future state of plant operations.
Digital twin technology leverages machine learning algorithms to aggregate, integrate, analyze, and compare historical operational and design data against current data to form the statistical model.
The result is a virtual clone of each unit in a facility, replicating its physical counterpart’s functions, features, and behaviors, along with the mechanical and electrical systems feeding into it.
Digital twin models also include a facility or industrial site’s physical infrastructure and intricate web of plant processes, including procurement and supply chain logistics, to create a digital operational environment.
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