The concept of creating a virtual copy of a complete Cyber-Physical System opens up numerous possibilities, including real-time assessments of the physical environment and continuous learning from the system to provide reliable and precise information. This process, known as the twinning process or the development of a digital twin (DT), has been widely adopted across various industries. However, challenges arise when considering the computational demands of implementing AI models, such as those employed in digital twins, in real-time information exchange scenarios. This work proposes a digital twin framework for optimal and autonomous decision-making applied to a gas-lift process in the oil and gas industry, focusing on enhancing the robustness and adaptability of the DT. The framework combines Bayesian inference, Monte Carlo simulations, transfer learning, online learning, and novel strategies to confer cognition to the DT, including model hyperdimensional reduction and cognitive tack. Consequently, creating a framework for efficient, reliable, and trustworthy DT identification was possible. The proposed approach addresses the current gap in the literature regarding integrating various learning techniques and uncertainty management in digital twin strategies. This digital twin framework aims to provide a reliable and efficient system capable of adapting to changing environments and incorporating prediction uncertainty, thus enhancing the overall decision-making process in complex, real-world scenarios. Additionally, this work lays the foundation for further developments in digital twins for process systems engineering, potentially fostering new advancements and applications across various industrial sectors.

翻译：构建完整信息物理系统虚拟副本的概念开启了诸多可能性，包括对物理环境的实时评估以及通过持续学习系统获取可靠精准的信息。这一过程被称为孪生过程或数字孪生（DT）开发，已在各行业得到广泛应用。然而，在实时信息交换场景中部署人工智能模型（如数字孪生所采用的模型）时，其计算需求带来了挑战。本文针对油气行业气举过程提出了一种面向最优与自主决策的数字孪生框架，重点提升数字孪生的稳健性与适应性。该框架融合贝叶斯推断、蒙特卡洛模拟、迁移学习、在线学习以及包括模型超维降阶与认知追踪在内的新型策略，赋予数字孪生认知能力。由此，可构建高效、可靠且可信的数字孪生辨识框架。所提方法填补了当前文献中关于数字孪生策略中集成多种学习技术与不确定性管理的空白。该数字孪生框架旨在提供一种可靠高效的系统，能够适应环境变化并纳入预测不确定性，从而提升复杂真实场景中的整体决策过程。此外，本文为过程系统工程领域中数字孪生的进一步发展奠定了基础，有望推动各工业领域的新进展与应用。