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The concept of creating a virtual copy of a complete Cyber-Physical Systemopens up numerous possibilities, including real-time assessments of thephysical environment and continuous learning from the system to providereliable and precise information. This process, known as the twinning processor the development of a digital twin (DT), has been widely adopted acrossvarious industries. However, challenges arise when considering thecomputational demands of implementing AI models, such as those employed indigital twins, in real-time information exchange scenarios. This work proposesa digital twin framework for optimal and autonomous decision-making applied toa gas-lift process in the oil and gas industry, focusing on enhancing therobustness and adaptability of the DT. The framework combines Bayesianinference, Monte Carlo simulations, transfer learning, online learning, andnovel strategies to confer cognition to the DT, including modelhyperdimensional reduction and cognitive tack. Consequently, creating aframework for efficient, reliable, and trustworthy DT identification waspossible. The proposed approach addresses the current gap in the literatureregarding integrating various learning techniques and uncertainty management indigital twin strategies. This digital twin framework aims to provide a reliableand efficient system capable of adapting to changing environments andincorporating prediction uncertainty, thus enhancing the overalldecision-making process in complex, real-world scenarios. Additionally, thiswork lays the foundation for further developments in digital twins for processsystems engineering, potentially fostering new advancements and applicationsacross various industrial sectors.

Digital Twin Framework for Optimal and Autonomous Decision-Making in  Cyber-Physical Systems: Enhancing Reliability and Adaptability in the Oil and  Gas Industry