Multiscale Simulation Methods for CO2 Capture, Storage, and Utilisation

From Molecular Insights to Scalable Solutions in Carbon Management
Erscheinungsjahr 2027
ISBN: 978-0-443-45850-7
Verlag: Elsevier Science

Buch, Englisch, 512 Seiten, Format (B × H): 152 mm x 229 mm

As the urgency to address climate change grows, innovative solutions to reduce atmospheric CO2 are becoming essential. Multiscale Simulation Methods for CO2 Capture, Storage, and Utilisation: From Molecular Insights to Scalable Solutions in Carbon Management introduces advanced simulation methods for modelling the processes controlling CO2 capture, storage, and utilisation (CCSU), from the nano to the macro perspective. Whilst the current literature primarily focuses on the engineering aspects of CCSU, there is a notable lack of helpful learning-focused information on how CO2 conversion and utilization processes can be modelled, understood, quantified, and improved in an effective manner using a molecular-level approach. This book is designed to equip readers with the knowledge and tools to develop and optimize these solutions through advanced modelling techniques. It integrates the latest research and methodologies from traditional physics-based computational chemistry methods, machine learning techniques, and macroscale engineering approaches. It is particularly valuable for computational chemists who are primarily interested in modelling chemical reactions for CO2 conversion but have limited exposure to the engineering aspects and economic costs of these processes. Integrating the latest research and methodologies across multiple disciplines including computational chemistry, materials science, and chemical engineering, Multiscale Simulation Methods for CO2 Capture, Storage, and Utilisation: From Molecular Insights to Scalable Solutions in Carbon Management provides a valuable resource for guiding both academic research and practical applications.

Autoren/Hrsg.

Tommaso, Devis Di

Wolthers, Mariëtte

Fachgebiete

• Naturwissenschaften Chemie Chemie Allgemein Chemometrik, Chemoinformatik
• Naturwissenschaften Chemie Analytische Chemie Umweltchemie, Lebensmittelchemie
• Geowissenschaften Geologie Meteorologie, Klimatologie
• Geowissenschaften Geologie Petrologie, Mineralogie
• Naturwissenschaften Chemie Physikalische Chemie

Inhaltsverzeichnis

Part I: Modelling CO2 Capture
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

Über Autor(innen)

Tommaso, Devis Di
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.

Wolthers, Mariëtte
Mariëtte Wolthers is a Professor in Aquatic Geochemistry at the Department of Earth Sciences, Utrecht University, the Netherlands. Her main research focus is on the interaction between water and minerals (crystals, Earth Materials), investigating the influence of mineral formation/dissolution on water quality and vice versa. She explores mineral nucleation, growth, dissolution and surface reactivity, with and without impurity ions and at different rations of the crystal-building ions. Her approach is both experimental and theoretical; the tools she uses to study earth materials range from atomic scale computational chemistry and macroscale geochemical modelling to sophisticated analyses techniques such as electron microscopy, light scattering and synchrotron-based spectroscopy.