KeyLogic develops process simulation flowsheets and applies data science tools, such as uncertainty quantification, design of experiments, and ML, to help design better carbon-capture processes. Our work reduces the time and cost to maximize learning from experiments in pilot plants. In addition, we support the modeling and analysis of low viscosity, water-lean solvents with polarity swing regeneration that are anticipated to have favorable CO2 capture costs. Our expertise in applying uncertainty quantification to identify critical solvent data gaps for solvent-based CO2 capture systems, will reduce the time and cost to develop advanced materials for carbon capture.
Data Science for an Improved Carbon-Capture Process
