Adaptive digital twins for energy-intensive industries and their local communities
Timothy Gordon Walmsley, Panos Patros, Wei Yu, Brent R. Young, S. M. Burroughs, Mark Apperley, James K. Carson, Isuru A. Udugama, Hattachai Aeowjaroenlap, Martin J. Atkins, Michael R.W. Walmsley
Abstract
Digital Twins (DTs) are high-fidelity virtual models that behave-like, look-like and connect-to a physical system. In this work, the physical systems are operations and processes from energy-intensive industrial plants. The creation of DTs demands expertise not just in engineering, but also in computer science, data science, and artificial intelligence. Here, we introduce the Adaptive Digital Twins (ADT) concept, anchored in five attributes inspired by the self-adaptive systems in software engineering. These attributes are self-learning, self-optimising, self-evolving, self-monitoring, and self-protection. This new approach merges cutting-edge computing with pragmatic engineering needs. ADTs can enhance decision-making in both the design phase and real-time operation of industrial facilities and allow for versatile 'what-if' scenario simulations. Seven applications within the energy-intensive industries are described where ADTs could be transformative.