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Convolutional deep neural networks are one of the main machine learning techniques applied to computer vision and object recognition tasks. Currently, they are very popular due to their proven effectiveness in solving image classification tasks and their significant theoretical and practical importance to the advancement of the deep learning field. Examples of successful image classification networks developed are AlexNet, VGG, and GoogLeNet.1,2,3
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The Brazilian cost of corrosion was estimated at 3% of the GPD in 2018, that percentage is equivalent to approximately $US 49 billion, according to an ABRACO(1) journal released in 2020.1 It is estimated that from this cost $US 19 billion could have been saved through anticorrosive actions. In another research conducted by the EPRI(2) the results showed that at least 22% of corrosion costs could be avoided through adequate mitigating actions.2
The Brazilian cost of corrosion was estimated at 3% of the GPD in 2018, that percentage is equivalent to approximately $US 49 billion, according to an ABRACO1 journal released in 20201. It is estimated that from this cost $US 19 billion could have been saved through anticorrosive actions. In another research conducted by the EPRI2 the results showed that at least 22% of corrosion costs could be avoided through adequate mitigating actions2.
Impressed current rectifiers are the backbone of a pipeline operator’s cathodic protection (CP) systems. A rectifier’s ability to protect a large length of electrically continuous pipeline considerably improves efficiencies and reduces material costs as compared to galvanic systems. However, like galvanic anodes, impressed current anodes are a consumable asset, and require replacement at the end of their service life to ensure that the rectifier can continue to adequately protect the pipeline.
One of the pillars of the fourth industrial revolution (4IR) is to let machines make decisions on behalf of humans; this paper describes new technology that allows machines to decide inspection programs and field validation and testing of results. The technology described is a part of integrity management, and uses data, statistics and expert decisions to design inspection programs. These inspection programs are an important part of the safeguarding of equipment to maintain production and safety.This technology is a data-driven predictive model of material loss from corrosion, based on domain expert input and historical data in the form of non-destructive testing (NDT) tests. The technology trends is based on historical data and SME input, while accounting for uncertainties in NDT measurements, with uncertainties in historical trends and uncertainties in future trends. This produces a more realistic failure prediction to enhance existing RBIs and adds safety by improving on early detection of trends in data. In total, this enables the machine to update inspection plans autonomously, reducing the number of inspections significantly.The paper also describes how the technology can be developed further to use production data and integrity operating windows to improve predictions, deal with localised corrosion and assess if the test points on a corrosion circuit are sufficient, can be reduced in number or should be manually evaluated by adding more test points.
Corrosion of metallic structures is a ubiquitous problem in industries such as power generation, oil and gas, pulp and paper, metals processing etc. which also results in significant financial losses. According to the National Association of Corrosion Engineers (NACE) International report, the global cost of corrosion was ~ 2.5 trillion USD in 2013 - close to 3.4 percent GDP of the entire world. The use of corrosion inhibitors is one of the most effective and economical ways to mitigate corrosion of metal and alloy components. Corrosion inhibitors are substances that are added in small quantities in corrosive media to protect metal and alloy components from corrosion.
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.
Virtual paint training systems are a needed and valuable addition to teaching methods. As the accuracy and complexity of simulations improve, the industry has begun to exploit this fusion of simulation and education. This presentation explores the next step - how to use the simulation to increase student engagement, enrich their skills development, and improve the trainees’ knowledge base.