Server maintenance is scheduled for Saturday, December 21st between 6am-10am CST.

During that time, parts of our website will be affected until maintenance is completed. Thank you for your patience.

Menu
Search
Filters
Close
Menu

Use GIVING24 at checkout to save 20% on eCourses and books (some exclusions apply)!

Towards Automated Development and Evaluation of Optimal Corrosion Inhibitor Products

Picture for Towards Automated Development and Evaluation of Optimal Corrosion Inhibitor Products
Available for download

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.

Product Number: 51323-18783-SG
Author: Hector Klie, Arturo Klie, Ana Ferrer, Yolanda De-Abreu
Publication Date: 2023
$0.00
$20.00
$20.00

In this work, a novel analysis approach using an artificial intelligence (AI) framework that automatically helps identify the drivers of a formulation from lab measurements was showcased. This AI framework builds and optimizes models in the form of physics-based equations from small amounts of measurement data. With this approach, the user can overcome the large data requirements of machine learning while building tailored models that outperform traditional analytical and statistical tools.
The effectiveness of this modeling framework in helping scientists reduce uncertainty early in the experiment process was demonstrated. This solution allows a significant reduction in the number of experiments required to achieve an optimal formulation. This was accomplished by generating new, custom models from existing data and well-known equations in electrochemistry. Then, these models were used to predict or hypothesize the performance of unseen formulations by altering their control parameters. This study showed the accuracy of these predictions by calculating its error against unseen measurement data.

In this work, a novel analysis approach using an artificial intelligence (AI) framework that automatically helps identify the drivers of a formulation from lab measurements was showcased. This AI framework builds and optimizes models in the form of physics-based equations from small amounts of measurement data. With this approach, the user can overcome the large data requirements of machine learning while building tailored models that outperform traditional analytical and statistical tools.
The effectiveness of this modeling framework in helping scientists reduce uncertainty early in the experiment process was demonstrated. This solution allows a significant reduction in the number of experiments required to achieve an optimal formulation. This was accomplished by generating new, custom models from existing data and well-known equations in electrochemistry. Then, these models were used to predict or hypothesize the performance of unseen formulations by altering their control parameters. This study showed the accuracy of these predictions by calculating its error against unseen measurement data.

Product tags
Also Purchased
Picture for Chemical & Enhanced Preservation Methodologies to Maintain the Integrity of Joint Operation (JO) Surface Facilities
Available for download

Chemical & Enhanced Preservation Methodologies to Maintain the Integrity of Joint Operation (JO) Surface Facilities

Product Number: 51323-18792-SG
Author: A/Rahman Al-Ghamdi, Hamad Al-Ajmi, Tariq Kamshad, Bakheet Al-Yami, Mohamed M. Farah, Hossam Mesbah
Publication Date: 2023
$20.00

The Wafra Joint Operation (WJO) Oilfield is located in the central-west part of the Kuwait-Saudi Arabia Neutral Zone. The Wafra oilfield reserves were first discovered and wells drilled in 1953 and production in commercial quantities began in 1954. This field produces two types of crude oil, Ratawi (light oil) and Eocene (heavy oil), with average water cut of 80-85%. During operation, the production wells produce the oil emulsion through mostly coated flowlines to sub-centres (SC) where the sour oil, water and gas are separated. The facility has two gathering fields: Eocene and Ratawi. Eocene has 2 phase separation, whilst Ratawi has 3 phase separation.
Picture for Monitoring Strategies for Effective AC Mitigation Integrity Management
Available for download

Monitoring Strategies for Effective AC Mitigation Integrity Management

Product Number: 51323-18885-SG
Author: Jamey Hilleary
Publication Date: 2023
$20.00

Corrosion risk due to AC interference has been known to be a possibility for decades but really came to the awareness of pipeline industry professionals starting around 2000 to 2004. Prior to that time there were some lab simulations as well as some suspected incidents in actual field situations, but many in the industry resisted accepting this as a real risk even as late as 2012 or later. Part of the reluctance to view AC interference as a genuine corrosion risk was that corrosion directly attributed to AC interference had not really been seen in the century of buried pipeline management, as well as a lack of understanding as to how this interference produced or accelerated corrosion on the pipeline.
Picture for Using Galvanic Modeling and Aerosol Spray Testing to Understand How Changes in Salt Loading Can Impact Atmospheric Corrosion
Available for download

Using Galvanic Modeling and Aerosol Spray Testing to Understand How Changes in Salt Loading Can Impact Atmospheric Corrosion

Product Number: 51323-18961-SG
Author: Thomas Curtin, Robert Adey, Andres Peratta, Ryan Butchers, Fritz Friedersdorf, Liam Agnew
Publication Date: 2023
$20.00

Corrosion is not just a sustainment concern that impacts the availability and safety of critical structural assets; it is also a damage mechanism that should be considered during the initial design phase. By considering the corrosion process and associated preventive strategies during the design phase it is possible to reduce total ownership cost and improve equipment readiness. The Department of Defense spends more than $23 billion each year to control corrosion on aircraft and other equipment in its operations around the world.
Categories
Industry
Recently viewed
Popular tags
View all

Association for Materials Protection and Performance

© AMPP All Rights Reserved.
Membership Terms of Content Use
Contact Customer Support