Grant: Alliance Grants
University of Guelph
$200,745.00
Discovery of Improved CO2 Capture Solvents by a Combination of Predictive Molecular-Based Simulation and Artificial Intelligence Algorithms
EF 2023-24
Résumé
It is generally accepted that the planet is undergoing climate change. Although there is debate concerning thebest methodologies for its alleviation, CO2 emission from fossil fuel combustion is considered to be a majorcontributing factor. Whereas increased energy efficiency and renewable energy sources offer longer-term sustainable solutions to greenhouse gas reduction, Canada and most other countries are expected to continue to rely on fossil fuels to meet their anticipated energy demands over the next century. Reducing the resulting CO2 emissions is critical in meeting climate change and Net-Zero goals.Post-combustion Carbon Capture (PCCC) from concentrated point sources such as oil sands production,fossil-fuel-based electric power plants, and steel and cement plants by means of solvent-based reactiveabsorption is the most mature CO2 capturing technology currently available. The captured CO2 is either used to make useful products or stored in underground geologic locations. Solvent-based PCCC has been deployed in several test facilities in Canada and worldwide. Although it has the economic advantage that it can be retrofitted to existing industrial installations, its current inefficiencies and high cost inhibit its large-scale deployment. Solvents currently in use are based on technology first developed over 80 years ago, and there is a pressing need to develop improved solvents. Due to the enormous number of possible solvents, this cannot be achieved by means of experimental investigations alone. This project will combine a predictive molecular-based model with the development of computationally efficient artificial intelligence surrogate models. This will enable an efficient framework to both screen large databases of existing potential PCCC solvents and to discover the chemical structures of new solvent candidates, which will be verified by experimental studies.
Programme
Alliance Grants
Bénéficiaire
Emplacement
Ontario, Canada
Type de bénéficiaire
Academia
Type
Grant
Source
NSERC's Awards Data
Tel que publié
Application id
580863-2022
Name nom
Smith, William WR
Department département
Mathematics and Statistics
Organization id
24
Institution établissement
University of Guelph
Province en
Ontario
Province fr
Ontario
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2023
Competition year année de concours
2022
Award amount
200745
Program id
ALLRP
Program name en
Alliance Grants
Program name fr
Subventions Alliance
Group en
RESEARCH PARTNERSHIPS
Group fr
PARTENARIATS DE RECHERCHE
Committee code
96
Committee name en
RPP Internal Decision Cttee
Committee name fr
Cté de décision interne - PPR
Area of application code
300
Area of application group en
Energy resources
Area of application group fr
Ressources d'énergie
Area of application en
Energy resources (including production, exploration, processing, distribution and use)
Area of application fr
Ressources d'énergie (production, exploration, traitement, distribution et utilisation de l'énergie)
Research subject code
3155
Research subject group en
Theoretical physics and chemistry
Research subject group fr
Statistical mechanics and thermodynamics
Research subject en
Physique et chimie théoriques
Research subject fr
Mécanique statistique et thermodynamique
Application title
Discovery of Improved CO2 Capture Solvents by a Combination of Predictive Molecular-Based Simulation and Artificial Intelligence Algorithms
Keyword
activity coefficients; chemical engineering; chemical equilibrium; electrolyte solutions; electronic structure; molecular dynamics; solubility; solution chemistry; statistical mechanics; thermodynamics
Application summary
It is generally accepted that the planet is undergoing climate change. Although there is debate concerning thebest methodologies for its alleviation, CO2 emission from fossil fuel combustion is considered to be a majorcontributing factor. Whereas increased energy efficiency and renewable energy sources offer longer-term sustainable solutions to greenhouse gas reduction, Canada and most other countries are expected to continue to rely on fossil fuels to meet their anticipated energy demands over the next century. Reducing the resulting CO2 emissions is critical in meeting climate change and Net-Zero goals.Post-combustion Carbon Capture (PCCC) from concentrated point sources such as oil sands production,fossil-fuel-based electric power plants, and steel and cement plants by means of solvent-based reactiveabsorption is the most mature CO2 capturing technology currently available. The captured CO2 is either used to make useful products or stored in underground geologic locations. Solvent-based PCCC has been deployed in several test facilities in Canada and worldwide. Although it has the economic advantage that it can be retrofitted to existing industrial installations, its current inefficiencies and high cost inhibit its large-scale deployment. Solvents currently in use are based on technology first developed over 80 years ago, and there is a pressing need to develop improved solvents. Due to the enormous number of possible solvents, this cannot be achieved by means of experimental investigations alone. This project will combine a predictive molecular-based model with the development of computationally efficient artificial intelligence surrogate models. This will enable an efficient framework to both screen large databases of existing potential PCCC solvents and to discover the chemical structures of new solvent candidates, which will be verified by experimental studies.