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Advances In Digital Twin And AI To Predict And Manage External Corrosion - A Smart Tool For Decision Makers

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

Product Number: 51322-17992-SG
Author: Otavio Correa, Jorge Seleme Mariano
Publication Date: 2022
Industry: Coatings
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This paper focuses on reviewing and offering a solution to manage the complex atmospheric corrosion process and its protection within industrial plants such as Oil & Gas, Petrochemicals, Pulp and Paper, and Mining. A review of the main protective coating failure mechanisms, as well as corrosion evaluation methods, are presented. Our objective is to gather the main parameters for a maintenance/inspection management tool, by developing a field data collection processing that feeds a digital model to predict coating failure and to enhance time, costs, and performance of asset integrity management activities. The development of a software is discussed, bringing a view on how to merge the Industry 4.0 technologies such as Digital Twins, Artificial Intelligence, Cloud services, and Mobile concepts to deploy predictive models. Advances in the platform showed ways to outline optimized inspection and maintenance plans, using condition data. It was possible to develop tools to predict costs, workforce and other variables to be considered for protective coating management. Finally, after carrying out all the necessary calculations for the activities mentioned above, the authors present how the technology can deliver other management information such as budget, productivity, the number of paintings done and/or future paintings followed for decision-makers to evaluate optimized scenarios.

This paper focuses on reviewing and offering a solution to manage the complex atmospheric corrosion process and its protection within industrial plants such as Oil & Gas, Petrochemicals, Pulp and Paper, and Mining. A review of the main protective coating failure mechanisms, as well as corrosion evaluation methods, are presented. Our objective is to gather the main parameters for a maintenance/inspection management tool, by developing a field data collection processing that feeds a digital model to predict coating failure and to enhance time, costs, and performance of asset integrity management activities. The development of a software is discussed, bringing a view on how to merge the Industry 4.0 technologies such as Digital Twins, Artificial Intelligence, Cloud services, and Mobile concepts to deploy predictive models. Advances in the platform showed ways to outline optimized inspection and maintenance plans, using condition data. It was possible to develop tools to predict costs, workforce and other variables to be considered for protective coating management. Finally, after carrying out all the necessary calculations for the activities mentioned above, the authors present how the technology can deliver other management information such as budget, productivity, the number of paintings done and/or future paintings followed for decision-makers to evaluate optimized scenarios.

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