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Deep Learning

AI-Enhanced Microstructural Analysis, Simulation, and Optimization for Electrochemical Device Electrodes

  • Computer vision provides a rapid pathway from physical electrode to microstructural parameters
  • Deep neural networks provide analysis from microstructural parameters to predict long-term performance metrics  
  • Connecting the two will produce a rapid electrode assessment tool

NETL researchers have used EDX® to publicly release the largest known bank of 3D electrode microstructures of solid oxide fuel and electrolysis cells (SOCs) for training ML tools.

Access the SOC Synthetic Microstructure Bank here.

2024-09-18T17:44:19+00:00September 18th, 2024|

Subsurface Trend Analysis (STA) Method and Tool

Enhancing knowledge discovery from unstructured data using a deep learning approach to support subsurface modeling predictions

  • NETL advanced the subsurface trend analysis (STA) workflow with an AI-informed image segmentation/embedding model. 
  • The STA method was created to be a foundational technology, capable of assisting any subsurface predictive need.
  • The image embedding tool uses convolutional neural networks (CNNs) to:
    • Extract images from unstructured data
    • Categorically label the images
    • Create a repository for geologic domain postulation
  • A case study on data available for the Gulf of Mexico shows the STA image embedding tool extracts and accurately labels images with 90% to 95% precision.
  • The STA 2D Tool is available on NETL’s Energy Data eXchange® (EDX).
2024-09-18T17:24:54+00:00September 18th, 2024|

Machine Learning and Deep Learning for Mineralogy Interpretation and CO2 Saturation Estimation in Geological Carbon Storage: A Case Study in the Illinois Basin

Wang, H., Williams-Stroud, S., Crandall, D., and Chen, C. (2024). Machine learning and deep learning for mineralogy interpretation and CO2 saturation estimation in geological carbon Storage: A case study in the Illinois Basin. Fuel, 361(130586). https://doi.org/10.1016/j.fuel.2023.130586

2024-07-16T17:06:20+00:00January 1st, 2024|

TEA of the CO2 capture process in pre-combustion applications using thirty-five physical solvents: Predictions with ANN

Husain E. Ashkanani, Rui Wang, Wei Shi, Nicholas S. Siefert, Robert L. Thompson, Kathryn H. Smith, Janice A. Steckel, Isaac K. Gamwo, David Hopkinson, Kevin Resnik, Badie I. Morsi, 2023, TEA of the CO2 capture process in pre-combustion applications using thirty-five physical solvents: Predictions with ANN, International Journal of Greenhouse Gas Control, Volume 130, 104007, ISSN 1750-5836. https://doi.org/10.1016/j.ijggc.2023.104007.

2024-07-16T16:32:39+00:00November 6th, 2023|
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