The exploration of alternative energy sources has gained significant traction in recent years, driven by the urgent need to mitigate greenhouse gas emissions and transition towards sustainable energy. Among these alternatives, ${\mathrm{C}\mathrm{O}}_2$ plume geothermal and hydrothermal systems have emerged as promising options due to their potential for providing clean, renewable energy. This study presents a probabilistic investigation into the sensitivity of ${\mathrm{C}\mathrm{O}}_2$ plume geothermal and hydrothermal systems under various reservoir conditions in the Williston Basin, North Dakota. In addition to elucidating the impact of reservoir conditions on system performance, the study utilizes probabilistic methods to assess energy output of ${\mathrm{C}\mathrm{O}}_2$ plume geothermal and hydrothermal systems. Insights derived from this probabilistic investigation offer valuable guidance for the working fluid selection, systems design and optimization in the Williston Basin and beyond. Results from the sensitivity analysis reveal the profound influence of reservoir conditions on the behavior and efficiency of ${\mathrm{C}\mathrm{O}}_2$ plume geothermal and hydrothermal systems. Our case study on Red River Formation and Deadwood Formations shows a potential of 34% increase and 32% decrease in heat extraction based on varying reservoir conditions. Our investigations in the Beaver Lodge field within the Red River Formation yielded arithmetic mean values for ${\mathrm{C}\mathrm{O}}_2$ best case resources, hydrothermal resources and the ${\mathrm{C}\mathrm{O}}_2$ worst case as 6.36 $\times {10}^{18}$ J, 4.75 $\times {10}^{18}$ J and 3.24 $\times {10}^{18}$ J, respectively. Overall, this research contributes to advancing the knowledge and understanding of ${\mathrm{C}\mathrm{O}}_2$ plume geothermal and hydrothermal systems as viable pathways towards sustainable energy production and carbon sequestration. By highlighting the sensitivity of these systems to reservoir conditions, the study provides valuable insights that can inform decision-making processes and future research endeavours aimed at fostering the transition to a low-carbon energy landscape.

The Magnetic Flux Leakage (MFL) system has become the key solution to the need for effective pipeline predictive control and maintenance, as it provides non-destructive tests. MFL systems work by introducing a magnetic field into the pipeline material and measuring changes in that field due to features such as corrosion, cracks, and metal loss. These variations which are detected by sensors give real time information on the condition of the pipeline. This review algorithm focuses on use of MFL as a predictive control technique, with an emphasis on the capacity to predict failure and improve safety. MFL combined with state-of-the-art data analytic and machine learning enhances the ability to detect defects and promote timely maintenance for efficient pipeline operations across various industries. This review analysed 81 existing studies which are taken from the year 2019 to 2024. Elaborate discussions on Predictive Control Strategies for Pipeline Management, Magnetic Flux Leakage (MFL) Technique in Modern Pipeline Maintenance, the Evolution and Impact of MFL in Pipeline Inspection, Pipeline Defects: Detection and Classification Using MFL, integrating MFL Systems into Predictive Maintenance Frameworks, recent Advancements in Magnetic Flux Leakage Sensor Technology. Furthermore, tables have been provided which explains the limitations of the techniques are discussed.

Solid control of flow and pressure of fluids is core to efficiency, safety and cost effectiveness of industrial processes within industries like petrochemicals, manufacturing and energy. This paper represents an intensive case study of a medium-sized industrial facility and comes up with resolving inefficiencies in the flow management system and pressure control. The work examines possible approaches to the optimization of fluid transport based on computational fluid dynamics (CFD) simulations, real-time deployment of sensors and system reconfiguration, in order to mitigate system losses.As the results show, the actual improvements in flow-rate stability and energy consumption by flow are impressive, even when insignificant design alterations are made like valve repositioning, changes in diameter of pipes, refined integration of control logic. The figures after the implementation depict that pressure fluctuations have dropped by 22 percent and there is a 15 percent increase in the uptake of pump energy consumption. Another issue presented in the paper is the practicality of the economic and operational aspects of upgrading the current systems with automation technology so that they may be smarter in controlling the pressure. The results can also be used to contribute to the upcoming body of knowledge of this topic, that is, process optimization, and also provide some practical recommendations to engineering professionals who want to optimize the performance of a system without investing a significant amount of capital.