Climate change poses an urgent threat, demanding immediate and scalable methods to reduce carbon dioxide (CO₂) emissions. While renewable energy sources like solar and wind are the long-term solution, many industrial processes—such as electricity production, cement, steel, and chemical production—still rely heavily on fossil fuels. An immediate interim strategy is the use of Post-Combustion Carbon Capture and Storage (PCCS) in these sectors. The PCCS process involves capturing CO2 from the high-concentration exhaust gas stream from these operations in an absorber that dissolves it in a chemical solvent before the CO2-free gas is released to the atmosphere. The captured CO2 is then separated from the solvent by heating is compressed for storage or utilization, and the clean solvent is recycled for re-use in the absorber.
Limitations of Current Carbon Capture Technology
The greatest drawbacks of current PCCS methodology are the high energy cost for reheating the CO2-loaded solvent and the solvent’s susceptibility to degradation and its corrosion of the equipment vessels. Conventional techniques for discovering improved solvents involve a slow trial-and-error process of identifying existing or synthesizing potential solvents and experimentally testing them. This approach is time-consuming and expensive, limiting the number of solvents that can be investigated.
Quantum Mechanics: Prediction by a Computer-Implemented Molecular-Based Model
Quantum mechanics offers a transformative solution to accelerate the testing process by enabling researchers to simulate the interactions between the molecules of CO₂ and those of a potential solvent at the molecular level, obviating the need for experiments. Quantum mechanical calculations can predict these interactions under various operating conditions, and feed this information into atomistic simulation algorithms subsequently into rigorous thermodynamic models to predict the CO2 solubility in the solvent. The most promising solvent candidates identified by this theoretically based methodology are then validated by experimental testing.
Artificial Intelligence Accelerating Development
Artificial intelligence (AI), particularly Machine Learning (ML) models like neural networks, can play a pivotal role in revolutionizing carbon capture technologies. AI can analyze large datasets of potential solvents based on their molecular characteristics to predict their effectiveness in capturing CO₂. By learning from extensive quantum-mechanically-based molecular simulation data, AI can accelerate the discovery process by forecasting the behavior of untested solvents based only on the structural properties of their constituent molecules. This bypasses the computationally intensive quantum and atomistic calculations. AI effectively bridges the gap between molecular-level understanding and practical application, accelerating the entire development process.
A Predictive, Multidisciplinary Methodology
By integrating quantum mechanics, AI, atomistic simulations, and thermodynamic modeling, researchers at the University of Guelph in Guelph, Ontario Canada through its Carbon Capture initiative have developed and are implementing this unique predictive methodology to discover improved CO2 capture solvents.
This multidisciplinary approach offers an effective method to reduce CO₂ emissions, particularly in industries that are difficult to decarbonize. As PCCS technologies continue to evolve, they will play a crucial role in combating climate change and ensuring a safer, more sustainable future.
Teamwork is Important
The University of Guelph project is funded by the Natural Sciences and Engineering Research Council of Canada under its Alliance program and led by Prof. William R. Smith. Collaborators include Delta Cleantech of Regina Saskatchewan and Natural Resources Canada laboratories in Ottawa ON and in Varennes Quebec. The project also involves international collaborators in Mexico, Germany and Switzerland. Critical to the Project’s success are graduate students and postdoctoral fellows. The current team is shown below. Please contact Prof. Smith if you’re interested in joining us!
The University of Guelph NSERC Alliance CO2 Capture Research Group as of December 2024. From left: Sean Geddes, Dr. Tasneem Kausar, William Rutherford, Dr. William Smith, Thomas Seeger, and Kamal Aslam. Absent is Prof. Mihai Nica of the Department of Mathematics and Statistics, who is also a member of the group.
