Gas pipeline systems are one of the largest energy infrastructures in the world and are known to be very efficient and reliable. However, this does not mean they are prone to no risk. Corrosion is a significant problem in gas pipelines that imposes large risks such as ruptures and leakage to the environment and the pipeline system. Therefore, various maintenance actions are performed routinely to ensure the integrity of the pipelines. The costs of the corrosion-related maintenance actions are a significant portion of the pipeline’s operation and maintenance costs, and minimizing this large cost is a highly compelling subject that has been addressed by many studies. In this paper, we investigate the benefits of applying reinforcement learning (RL) techniques to the corrosion-related maintenance management of dry gas pipelines. We first address the rising need for a simulated testbed by proposing a test bench that models corrosion degradation while interacting with the maintenance decision-maker within the RL environment. Second, we propose a condition-based maintenance management approach that leverages a data-driven RL decision-making methodology. An RL maintenance scheduler is applied to the proposed test bench, and the results show that applying the proposed condition-based maintenance management technique can reduce up to 58% of the maintenance costs compared to a periodic maintenance policy while securing pipeline reliability.

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Superconducting (SC) in equal molar NbTaTiZr-based high-entropy alloys (HEAs) that were added with Fe, Ge, Hf, Si, and/or V was observed. According to investigation on crystal structure, composition, and the relationship between critical temperature and e/a ratio, as indicated in Matthias' empirical rule, the main superconducting phase was that enriched with Nb and Ta. The coherence length (ξ) that was calculated from the carrier density reveals that ξ value has the same order of magnitude of several hundreds of Angstroms as those binary Nb-Ti and Nb-Zr alloys showed.

Developed the predictive physics of failure models and system-level pipeline health monitoring methodology. The project involves a multi-disciplinary science, engineering, and operational approach to realize a comprehensive and state-of-the-art solution to pipeline integrity. The PSIM platform integrates real-time and historical data, methods, predictive failure models, and systemlevel inference algorithms to form a total system health management support tool to aid in integrity decision making and planning by the pipeline operators. The approach is innovative and unique in its comprehensive integrative perspective and focuses on providing practical solutions while advancing the critical scientific and engineering foundations.

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