A novel framework for quantitative resilience assessment in complex engineering systems during early and late design stages

Recently, resilience assessment has evolved and grown in prominence, yet most studies are carried out on operational stage when sufficient knowledge on the processes is available, overlooking the design stage, a time frame that is more suitable for making a resilient system. To this end, this work aims at developing a novel quantitative resilience assessment framework for engineering systems with two different approaches that practically analyse resilience at the early and late design stages when detailed information on the system’s safety and resilience capabilities may be deficient. In the early design stage, system resilience attributes are identified, and expert judgment is used to assess their quality. In the late design stage, attributes are derived from revealed information such as detailed emergency response and safety barrier data. In both stages, dynamic Bayesian Network (DBN) is used to quantify resilience based on the acquired information. Since the green hydrogen technology is relatively novice, the application of the proposed framework is demonstrated in a resilience assessment of a green hydrogen plant undergoing hydrogen release scenarios. The proposed framework can be used as an effective tool for early design improvements as well as enhancing process safety in the late design stage of hydrogen plants or any other complex engineering system

Reliability-based design of offshore structures for oil and gas applications

Offshore structures are complex in their structural and functional form and operate in a harsh and uncertain environment with complex interactions between ocean variables. Consequently, the ocean environment presents a high risk to these structures hence the need to develop an efficient and reliable design. Therefore, the need for a design that effectively: captures complex ocean parameter interactions, reduces the computational burden in structural response determination, quantifies the structure's ability to bounce back when faced with disruptive events, and minimizes cost under uncertainty at the desired safety levels of the asset is critical. A robust offshore structural design under uncertainty is essential for the safety of life, asset, and the environment during oil and gas exploration and production activities. This thesis presents improved methods for the effective reliability-based design of offshore structures. First, a framework is developed to capture the dependency of multivariate environmental ocean variables using vine copula and its impact on the reliability assessment of offshore structural systems. The model was tested using a cantilever beam and applied to an offshore jacket structure. The comparative results from the jacket structure and cantilever problem reveals that failure probability considering dependence between ocean variables is closer to the reference value than when variables are independent or modeled with a Gaussian copula. The outcome shows the importance of capturing nonlinearity and tail dependence between ocean variables in reliability evaluation. Secondly, the effectiveness of a hybrid metamodel, which is a combination of two commonly and independently used methods, Kriging and Polynomial Chaos Expansions (PCE), is investigated for offshore structural response determination and reliability studies. The hybrid metamodel herein, called (APCKKm-MCS) is constructed from an adaptive process with multiple enrichment of Experimental Design (ED). The hybrid approach was tested on simple non-linear functions, a truss bar, and an offshore deepwater Steel Catenary Riser (SCR). The study's outcome revealed that APCKKm-MCS produced a high predictive response capacity, reduced model evaluation, and shorter computing time during reliability evaluation than the single enrichment case (APCK-MCS) and the adaptive ordinary Kriging case (AK-MCS) considered. In addition, a novel framework is developed for the resilience quantification of offshore structures in terms of their time-varying reliability, adaptability, and maintainability. The developed framework was demonstrated using an internally corroded pipeline segment subject to disruptive events of leak, burst, and rupture. The framework captured the resilience index of the natural gas pipeline for its design life, and its sensitivity analysis revealed the influence of the pipe wall thickness and corrosion depth growth rate on the resilience of the pipeline. The framework provides a quantitative approach to determine the resilience of offshore structures and ascertain their critical influencing parameters for effective decision-making. Finally, a methodology for optimal structural design under uncertainty considering the dependency of environmental variables with the implementation of a hybrid metamodel in the inner loop of a nested optimization problem is presented and demonstrated on a steel column function and a segmented SCR. The study showed different decision outcomes for various vine tree configurations in the dependence modeling for the steel column function noting the importance of choosing the appropriate variable order in the vine tree for optimal design under uncertainty. Also, the research reveals the suitability of adaptive PCK for the inner loop reliability phase for a double-loop structural optimization due to its high predictive capacity and observed relatively low cross-validation error. The method shows the importance of effective dependence modeling of environmental ocean variables in structural cost minimization and selecting optimal structural design variables under uncertainty. From the research outcomes, considering multivariate dependence between ocean variables using vine copula and utilizing multiple enrichment hybrid metamodels in response evaluation for reliability and optimal design assessment of offshore structures could better predict their failure probability and enhance a safer structural design. In addition, the resilience quantification framework developed provides a vital decision-making tool for offshore structural systems' design and integrity management. The research into high dimensional dependence modeling of offshore structures using vine copula, comparative study of sampling strategies required for the hybrid (Kriging and PCE) metamodel construction, dependence-based structural resilience quantification, and multiobjective dependence-based structural optimization under uncertainty are among areas proposed for future investigation

Data Mining in Smart Grids

Effective smart grid operation requires rapid decisions in a data-rich, but information-limited, environment. In this context, grid sensor data-streaming cannot provide the system operators with the necessary information to act on in the time frames necessary to minimize the impact of the disturbances. Even if there are fast models that can convert the data into information, the smart grid operator must deal with the challenge of not having a full understanding of the context of the information, and, therefore, the information content cannot be used with any high degree of confidence. To address this issue, data mining has been recognized as the most promising enabling technology for improving decision-making processes, providing the right information at the right moment to the right decision-maker. This Special Issue is focused on emerging methodologies for data mining in smart grids. In this area, it addresses many relevant topics, ranging from methods for uncertainty management, to advanced dispatching. This Special Issue not only focuses on methodological breakthroughs and roadmaps in implementing the methodology, but also presents the much-needed sharing of the best practices. Topics include, but are not limited to, the following: Fuzziness in smart grids computing Emerging techniques for renewable energy forecasting Robust and proactive solution of optimal smart grids operation Fuzzy-based smart grids monitoring and control frameworks Granular computing for uncertainty management in smart grids Self-organizing and decentralized paradigms for information processin