A novel computational strategy to estimate CO2 solubility in brine solutions for CCUS applications

Estimation of CO2 solubility in brine is crucial to various CCUS (carbon capture utilization and storage) applications, especially for engineering design of the physical/chemical processes. In this work, we developed machine learning based workflow to calculate CO2 solubility in brine at various combinations of salt mixtures, pressure, and temperatures. Most importantly, the performance of predictive models and workflow were tested against extensive experimental observations and key features of brine components with significant contributions were determined.

Automated summary

In this study, a machine learning based workflow was developed to estimate the solubility of CO2 in brine under different salt mixtures, pressure, and temperatures. Extensive experimental observations were used to test the performance of predictive models and workflow, and key features of brine components with significant contributions were identified.