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Today, multi-purpose, built-for-purpose, all-in-one, pipeline integrity automation, wireless, data communication radios are available that monitor and report all cathodic protection rectifier operations, automate rectifier interruption, monitor rectifier operational status, monitor and report pipe-to-soil potential, pipeline pressure and pipeline pigging operations.
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We live in a data driven world where technology is constantly evolving and making our lives easier, but even with this progression, industrial facilities are still struggling with the lack of reliable and sufficient data. LoRa has the capability to affordably expand remote sensing technologies in industrial applications, thereby improving operational efficiency, automating processes, and improving safety. With several different types of sensors available, and many more being created every year, LoRa is set to become the industry standard for the Industrial Internet of Things (IIoT).
This paper will explore the process of conducting asset integrity management systems and the potential use for the existing facility data to analyze integrity status and predict any breach of integrity that would cause a direct major incident. In the dawn of the 4th industrial revolution and in the age of automation and artificial intelligence, asset integrity management systems are being integrated into a more sophisticated process of verification. Programs are being used to collect necessary risk-based data from inspection, maintenance programs and operational checklists in order to rationalize the integrity status and alert proponents of possible breach of integrity. These systems are more efficient than humans in predicting possible failures based on collective data from several critical elements from a facility and calculate the probability of failure based on the current integrity status. It is possible to optimize such systems to eliminate the human error factor and optimize inspection, maintenance and operation programs to better manage asset integrity. The result would be a software that would provide an overview of the plant’s integrity status and provide early alerts of any incoming incident event which allows the facility’s management to act accordingly and direct resources for effective prevention and mitigation.
Transportation of energy carriers (not only oil & gas, but also hydrogen, ammonia, methanol, heating fluids) and carbon dioxide requires the use of extensive pipeline networks that are usually built in metallic materials which are subject to material degradation. Carbon steel being the most prevalent due to its properties, availability, cost, and references. Carbon steel as well as other metallic materials suffer from corrosion processes.
In the 1990s, military assets transported shipboard to overseas locations by the U.S. Army arrived at their destinations already corroded due to saltwater. In response to this problem, U.S. Army Tank-automotive and Armaments Command (TACOM) began manually applying spray-on corrosion inhibitor (CI) to assets prior to transportation and realized a significant reduction in corrosion. However, due to an increase in the volume of military assets being transported overseas, manual application of CI soon proved too time-consuming and costly.
Shot blasting as a mechanical surface preparation process is widely used in finishing metallic parts. Sophistication in the use of this technique could range from simple manual systems to computer-controlled equipment for preparing aerospace and automotive components.
In most engineering and scientific applications, machine learning (ML) or artificial intelligence (AI) methods in general, are primarily oriented to design a statistical/heuristic procedure to predict the outcome of a system under new conditions. This mechanism aims at exploring non-evident correlations between inputs and outputs that are embedded in the data. However, a large body of this effort relies on black-box function approximations (e.g., neural networks) that have shown limitations to elucidate additional insights from the underlying physical process that generated the data. Thus, this type of knowledge is generated in a data-driven manner without fully explaining the physics governing the problem.