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

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

Product Number: 51322-17991-SG
Author: Otavio Correa, Jorge Seleme Mariano
Publication Date: 2022
Industries: Coatings and Linings , Coatings
$0.00
$20.00
$20.00

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. 

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. 

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

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

Product Number: 51322-17992-SG
Author: Otavio Correa, Jorge Seleme Mariano
Publication Date: 2022
$20.00

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. 
Picture for Applying Machine Learning Techniques To Identify And Predict Behavior Of Rectifier And Groundbed State Change
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Applying Machine Learning Techniques To Identify And Predict Behavior Of Rectifier And Groundbed State Change

Product Number: 51322-17833-SG
Author: Matt Barrett, Will Maize, Tony da Costa
Publication Date: 2022
$20.00

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.
Advanced Materials For Condensing Heat Transfer
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Advanced Materials For Condensing Heat Transfer

Product Number: 51322-17969-SG
Author: Anna Wojdyla-Cieslak, Alan Taylor, Garima Mittal, Imran Bhamji, Shiladitya Paul,
Publication Date: 2022
$20.00

Geothermal energy is an excellent source of renewable clean power generation, as well as for heating and cooling. Unlike other renewable energy sources, it is unaffected by local climate conditions. However, the heat exchangers used in geothermal power plants are under constant threat of scale formation and corrosion due to the harsh operational conditions to which they are exposed. Therefore, surface modifications to heat exchanger materials, for example through coatings, are necessary to improving the efficiency and durability of geothermal plant.v 
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