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.

该研究提出了一个数字孪生框架，应用于石油和天然气工业中的气举过程，旨在提高数字孪生系统的稳健性和适应性。该框架结合了贝叶斯推断、蒙特卡罗模拟、迁移学习和不确定性管理等技术，为数字孪生系统提供高效、可靠、值得信赖的识别，并致力于改进复杂实际场景中的决策过程。

在油气工业中增强可靠性和适应性的数字孪生框架，实现最优和自治决策

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

Data integration is considered a classic research field and a pressing need
within the information science community. Ontologies play a critical role in
such a process by providing well-consolidated support to link and semantically
integrate datasets via interoperability. This paper approaches data integration
from an application perspective, looking at techniques based on ontology
matching. An ontology-based process may only be considered adequate by assuming
manual matching of different sources of information. However, since the
approach becomes unrealistic once the system scales up, automation of the
matching process becomes a compelling need. Therefore, we have conducted
experiments on actual data with the support of existing tools for automatic
ontology matching from the scientific community. Even considering a relatively
simple case study (i.e., the spatio-temporal alignment of global indicators),
outcomes clearly show significant uncertainty resulting from errors and
inaccuracies along the automated matching process. More concretely, this paper
aims to test on real-world data a bottom-up knowledge-building approach,
discuss the lessons learned from the experimental results of the case study,
and draw conclusions about uncertainty and uncertainty management in an
automated ontology matching process. While the most common evaluation metrics
clearly demonstrate the unreliability of fully automated matching solutions,
properly designed semi-supervised approaches seem to be mature for a more
generalized application.

本文从应用角度探讨了基于本体匹配的数据集成问题，通过实际数据和现有工具的支持进行实验，发现自动本体匹配过程中存在的错误和不确定性，探索了半监督方法在更广泛应用中的成熟性和不确定性管理方面的前景