Carbon Capture

Carbon capture research focuses on developing and optimizing technologies to remove carbon dioxide from various sources, primarily to mitigate climate change. Current efforts concentrate on improving the efficiency and scalability of carbon capture methods, employing machine learning models like neural networks (including convolutional, recurrent, and graph neural networks), and diffusion models for tasks such as predicting CO2 plume behavior in geological storage, designing novel materials (e.g., metal-organic frameworks), and optimizing operational strategies. These advancements aim to enhance the safety, cost-effectiveness, and overall efficacy of carbon capture, impacting both scientific understanding of CO2 behavior and the practical implementation of large-scale carbon removal strategies.

Papers

October 2, 2024

An uncertainty-aware Digital Shadow for underground multimodal CO2 storage monitoring
Abhinav Prakash Gahlot, Rafael Orozco, Ziyi Yin, Felix J. Herrmann
Geological Carbon Storage Carbon Capture Subsurface Anatomy

July 25, 2024

Diffusion-based subsurface multiphysics monitoring and forecasting
Xinquan Huang, Fu Wang, Tariq Alkhalifah
State of the Art Forecasting Earthquake Monitoring Carbon Capture Subsurface Imaging

March 9, 2024

CarbonNet: How Computer Vision Plays a Role in Climate Change? Application: Learning Geomechanics from Subsurface Geometry of CCS to Mitigate Global Warming
Wei Chen, Yunan Li, Yuan Tian
Computer Vision Climate Change Carbon Capture Subsurface Property Subsurface Imaging

November 9, 2023

CarbNN: A Novel Active Transfer Learning Neural Network To Build De Novo Metal Organic Frameworks (MOFs) for Carbon Capture
Neel Redkar
Transfer Learning Metal Organic Framework Carbon Capture MOF Structure

June 14, 2023

A generative artificial intelligence framework based on a molecular diffusion model for the design of metal-organic frameworks for carbon capture
Hyun Park, Xiaoli Yan, Ruijie Zhu, E. A. Huerta, Santanu Chaudhuri, Donny Cooper, Ian Foster, Emad Tajkhorshid
Product Design Generative Framework Metal Organic Framework Carbon Capture Molecular Diffusion Model

April 19, 2023

Optimizing Carbon Storage Operations for Long-Term Safety
Yizheng Wang, Markus Zechner, Gege Wen, Anthony Louis Corso, John Michael Mern, Mykel J. Kochenderfer, Jef Karel Caers
Observable Markov Decision Process Partially Observable Markov Decision Process Geological Carbon Storage Carbon Capture

March 24, 2023

Machine Guided Discovery of Novel Carbon Capture Solvents
James L. McDonagh, Benjamin H. Wunsch, Stamatia Zavitsanou, Alexander Harrison, Bruce Elmegreen, Stacey Gifford, Theodore van Kessel, Flaviu Cipcigan
Data Driven Discovery Carbon Capture Candidate Molecule

January 18, 2023

Automated deep reinforcement learning for real-time scheduling strategy of multi-energy system integrated with post-carbon and direct-air carbon captured system
Tobi Michael Alabi, Nathan P. Lawrence, Lin Lu, Zaiyue Yang, R. Bhushan Gopaluni
Deep Reinforcement Learning System Description Energy Prediction Decarbonization Study Real Time Scheduling Carbon Figure Carbon Capture Multi Energy

December 16, 2022

De-risking Carbon Capture and Sequestration with Explainable CO2 Leakage Detection in Time-lapse Seismic Monitoring Images
Huseyin Tuna Erdinc, Abhinav Prakash Gahlot, Ziyi Yin, Mathias Louboutin, Felix J. Herrmann
Carbon Capture Seismic Image Leakage Detection Epidemic Containment Time Lapse Seismic

December 8, 2022

Enhanced prediction accuracy with uncertainty quantification in monitoring CO2 sequestration using convolutional neural networks
Yanhua Liu, Xitong Zhang, Ilya Tsvankin, Youzuo Lin
Convolutional Neural Network Uncertainty Quantification Prediction Accuracy Real Data Carbon Capture Seismic Inversion Carbon Sequestration

October 31, 2022

Real-time high-resolution CO$_2$ geological storage prediction using nested Fourier neural operators
Gege Wen, Zongyi Li, Qirui Long, Kamyar Azizzadenesheli, Anima Anandkumar, Sally M. Benson
Neural Operator Fourier Neural Operator Fourier Transform Geological Carbon Storage Carbon Capture

August 22, 2022

Prediction of $\textrm{CO}_2$ Adsorption in Nano-Pores with Graph Neural Networks
Guojing Cong, Anshul Gupta, Rodrigo Neumann, Maira de Bayser, Mathias Steiner, Breanndán Ó Conchúir
Graph Neural Network Human Prediction Metal Organic Framework Carbon Capture

December 22, 2021

Latent Space Simulation for Carbon Capture Design Optimization
Brian Bartoldson, Rui Wang, Yucheng Fu, David Widemann, Sam Nguyen, Jie Bao, Zhijie Xu, Brenda Ng
Carbon Capture

Carbon Capture

Papers

An uncertainty-aware Digital Shadow for underground multimodal CO2 storage monitoring

Diffusion-based subsurface multiphysics monitoring and forecasting

CarbonNet: How Computer Vision Plays a Role in Climate Change? Application: Learning Geomechanics from Subsurface Geometry of CCS to Mitigate Global Warming

CarbNN: A Novel Active Transfer Learning Neural Network To Build De Novo Metal Organic Frameworks (MOFs) for Carbon Capture

A generative artificial intelligence framework based on a molecular diffusion model for the design of metal-organic frameworks for carbon capture

Optimizing Carbon Storage Operations for Long-Term Safety

Machine Guided Discovery of Novel Carbon Capture Solvents

Automated deep reinforcement learning for real-time scheduling strategy of multi-energy system integrated with post-carbon and direct-air carbon captured system

De-risking Carbon Capture and Sequestration with Explainable CO2 Leakage Detection in Time-lapse Seismic Monitoring Images

Enhanced prediction accuracy with uncertainty quantification in monitoring CO2 sequestration using convolutional neural networks

Real-time high-resolution CO$_2$ geological storage prediction using nested Fourier neural operators

Prediction of $\textrm{CO}_2$ Adsorption in Nano-Pores with Graph Neural Networks

Latent Space Simulation for Carbon Capture Design Optimization