Multiscale Simulation Methods for CO2 Capture, Storage, and Utilisation
From Molecular Insights to Scalable Solutions in Carbon Management
- 1st Edition - June 1, 2027
- Latest edition
- Editors: Devis Di Tommaso, Mariëtte Wolthers
- Language: English
As the urgency to address climate change grows, innovative solutions to reduce atmospheric CO2 are becoming essential. Multiscale Simulation Methods for CO2 Capture, Storage, an… Read more
Description
Description
Key features
Key features
- Innovative computational chemistry focused text combining multiscale modelling with carbon capture, storage and utilisation methods for the first time
- Provides a structured overview of CCSU simulation techniques in conjunction with diverse examples of how these methods are applied across a range of materials and chemical transformations
- Includes recent developments in the application of machine learning to assist in the interpretation of results and bridge the gap between accuracy and the length and size scales of molecular simulations
- Discusses the economic and environmental impacts of CCSU technologies
Readership
Readership
Table of contents
Table of contents
1. Overview of Computational Methods for Modelling CO2 Capture
2. High-throughput and Machine Learning-Assisted Predictions of Materials for CO2 Capture
3. Computational Modelling and Simulation of CO2 Capture by Aqueous Amines
4. Modelling Potentials for Adsorption Processes Including CO2 Adsorption Capacity
5. Macroscale Models of CO2 Adsorption Processes
6. Modelling of ocean-based carbon capture Summary of Part I
Part II: Modelling CO2 Storage and Sequestration
7. Overview of Computational Methods for Modelling CO2 Storage and Sequestration
8. Modelling CO2 Mineralization to Carbonates
9. Modelling Geological Storage of CO2
10. Computational Fluid Dynamics (CFD) in CO2 Storage
11. Trapping Mechanisms and Long-term Fate of CO2
12. Machine Learning Models for Efficient CO₂ Storage Site Screening Summary of Part II
Part III: Modelling CO2 Conversion and Utilization
13. Overview of Computational Methods for Modelling CO2 Utilisation
14. Modelling Thermochemical CO2 Conversion
15. Modelling Electrochemical CO2 Reduction
16. Modelling Photochemical CO2 Conversion
17. Data-Driven Screening of (Photo)Electrocatalysts for CO₂ Conversion Using Machine Learning
18. Modelling CO2 Carbonation for Cementation Materials Summary of Part III
Part IV: The Economic and Environmental Impacts of Carbon Capture, Utilisation, and Storage
19. Assessing Sustainability in CCUS: Integrating Life-Cycle Analysis and Economic Modelling
Product details
Product details
- Edition: 1
- Latest edition
- Published: June 1, 2027
- Language: English
About the editors
About the editors
Devis Di Tommaso
Devis Di Tommaso is a Reader in Computational Chemistry at Queen Mary University, UK. His expertise is in the development and application of computational methods based on quantum, classical, and machine learning approaches, often complemented by spectroscopic techniques such as neutron scattering and THz spectroscopy. Using these tools, his group theoretically and computationally characterises molecular systems and inorganic materials. Their research focuses on electrocatalytic processes, CO2 mineralisation, and the development of atomistic and thermodynamic models for electrolyte solutions. In the realm of machine learning, the group develops graphical neural networks and approaches to accelerate molecular dynamics. He received his PhD from the University of Trieste, Italy in 2006, and following postdoctoral research positions at the Royal Institution of Great Britain and University College London in the UK, he moved to Queen Mary University of London in 2013, where he was promoted to Reader in 2024.