Multiple machine learning models for prediction of CO₂ solubility in potassium and sodium based amino acid salt solutions

In this work, we developed artificial intelligence-based models for prediction and correlation of CO₂ solubility in amino acid solutions for the purpose of CO₂ capture. The models were used to correlate the process parameters to the CO₂ loading in the solvent. Indeed, CO₂ loading/solubility in the solvent was considered as the sole model's output. The studied solvent in this work were potassium and sodium-based amino acid salt solutions. For the predictions, we tried three potential models, including Multi-layer Perceptron (MLP), Decision Tree (DT), and AdaBoost-DT. In order to discover the ideal hyperparameters for each model, we ran the method multiple times to find out the best model. R² scores for all three models exceeded 0.9 after optimization confirming the great prediction capabilities for all models. AdaBoost-DT indicated the highest R² Score of 0.998. With an R² of 0.98, Decision Tree was the second most accurate one, followed by MLP with an R² of 0.9.