Abstract
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.
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