Machine learning model incorporating physical features of sorbents for post combustion carbon capture

A power generation system comprising:

• a capture system for use in capturing carbon dioxide;
• a controller configured to operate the capture system;
• and
• a modeling system including a processor configured to:
• identify a model training data set, each instance of the model training data set identifying a sorbent used by the capture system, a carbon capture performance value for the sorbent, and a plurality of primary feature values associated with a plurality of primary features;
• generate one or more secondary features based on one or more of the plurality of primary features, each of the one or more secondary features being a combination of at least two of the plurality of primary features;
• determine a plurality of correlation magnitude values including a correlation magnitude value between each of the plurality of primary features and each of the one or more secondary features;
• identify a first subset of the model training data set based on the plurality of correlation magnitude values;
• determine a statistical significance for each instance of the first subset of the model training data set;
• identify a second subset of the model training data set based on the statistical significance, wherein the statistical significance for each instance of the second subset of the model training data set is below a pre-determined threshold;
• generate a transfer function based on the second subset of the model training data set;
• and
• determine, using the transfer function, one or more prospective sorbents to be used by the capture system based on the carbon capture performance value;
• wherein the controller operates the capture system using the one or more prospective sorbents determined by the modeling system.