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This paper investigates the relationships among the important process parameters that impact Post-combustion Carbon Capture (PCC) carbon dioxide (C O 2) separation. A better understanding of the complex relationships among those parameters can support optimization and performance enhancement of the separation process. Being able to precisely predict the process parameters will enable the operator to determine the current state of the process, forecast any potential changes or events, and adjust process parameters to enhance the plant's performance. With the objective of studying the process parameters' correlations in the amine-based PCC process, we modeled the multi-year historical production data of the Clean Energy Technologies Research Institute (CETRi) (formerly known as the International Test Center for PCC or ITC) in Regina, Saskatchewan, Canada, using a Decision Forest approach. The model validation process revealed that the Decision Forest model produced higher predictive accuracy than previous efforts. The Decision Forest models we developed also represent knowledge about the importance of parameters involved in the capture process, and such knowledge is useful for further optimization of the capture process in the future. • Modeling the parameters of the Carbon Capture process with machine learning (ML). • The ML models used Decision Forest with Gradient Boosting and Oblique Split. • The ML models achieved superior predictive accuracy compared to past efforts. • The ML models support interpretability by visualizing weights and prediction paths. [ABSTRACT FROM AUTHOR]