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Optimal design for pipelines layout is gaining further importance because of the increase in demand on oil and gas products. Due to the crucial importance of pipelines design, the need for optimization of pipelines layout and pipeline networks has acquired the attention of operators and practitioners. Shortest path optimization schemes of pipelines need new assessment to find the optimal solution for real-world topologies. In this paper, three optimal path planning techniques—breadth-first search (BFS), A-star (A*), and artificial potential field (APF)—are compared and introduced to the field of choosing pipelines layout. Although these three techniques are successfully used in other applications, such as robotics and self-driving vehicles, a benchmark study is required to determine the most suitable technique out of these three techniques for pipelines placement application. In this work, a comparison between the three techniques was applied on three types of maps represented different degrees of difficulties, simple, medium, and complex, to choose the optimal technique. Also, these techniques were applied on a real-world problem considering real topologies and complex obstacles. Results of this work show the superiority of the APF algorithm with respect to finding the more realistic, shortest path and taking the lowest computational time. However, the application on real-world problems shows some limitation of this technique due to local minimum problem. The test of the A* algorithm shows moderate computational time and moderate generated path with regard to length. Practical Applications: Shortest path planning is usually related to minimizing cost of construction and operation of pipelines. In this work, the objective was to provide practitioners and field engineers with a tool to choose the shortest path for pipelines. Usually, pipelines are designed without considering the shortest path but to prevent geographical obstacles. This work used three path planning techniques, A* , BFS and APF algorithms, to create pipeline paths on various geographical difficulty-level maps. These techniques were appraised in other fields of research but were not compared to solve path planning problems. The results of this work are used to complete the path planning from oil and gas wells to the production and test manifolds and from manifolds to the early production facility (EPF). Further work is currently being done to obtain a software that performs this design and the cost calculations to obtain a realistic pipeline path on all type of maps. [ABSTRACT FROM AUTHOR]

The combined effects of aqueous corrosion, stress factors, and seeded cracks on leakage in cast iron pipes have not been thoroughly examined due to the complexity and difficulty in predicting their interactions. This study seeks to address this gap by investigating the interdependencies between corrosion, stress, and cracks in cast iron pipes to optimise the material selection and design in corrosive environments. Leakage experiments were conducted under simulated localised corrosive conditions and internal pressure, revealing that leakage increased from 0 to 25 mL with crack sizes of 0.5 mm, 0.8 mm, 1 mm, and 1.2 mm, along with corrosion times of 0, 120, 160, and 200 h, and varying stress levels. An empirical model was developed using a curve-fitting approach to map the relationships among corrosion time, crack propagation, and leakage amount. The results demonstrate that the interaction between corrosion, stress, and crack propagation was complex and nonlinear, and the leakage amount increased from 0.7 to 0.10 mm every 15 min, as evidenced by SEM microstructure images and empirical data. [ABSTRACT FROM AUTHOR]

In recent years, various approaches have been proposed to design control systems that directly utilize data without mathematical plant models. Data-driven control involves updating or redesigning a controller using actual operating data, enabling fine-tuning control systems and achieving desired characteristics. However, the increasing prevalence of cyber-attacks targeting control systems presents significant societal challenges. A study by Russo and Proutiere (in Proceeding of American Control Conference (ACC), 2021) showed a poisoning approach targeting virtual reference feedback tuning, a data-driven control method. The study suggests that compromising the data used in the data-driven method may result in the closed-loop performance failing to achieve desired specifications and, in the worst case, destabilizing the control system. Hence, investigating the adverse effects of cyber-attacks on data employed in data-driven methods becomes crucial. This study explores the impact of a poisoning attack on the data used in the data-driven control method, specifically emphasizing virtual internal model tuning as a representative data-driven control approach. [ABSTRACT FROM AUTHOR]