Optimization and comparison of machine learning methods in estimation of carbon dioxide loading in chemical solvents for environmental applications

In this study, we developed a variety of machine learning ensemble models for predicting and correlating CO₂ solubility in amino acid salt solutions containing different concentrations. The models were utilized to establish a relationship between process parameters and CO₂ loading in the solvent. Indeed, the solitary model output was the amount of CO₂ that was loaded into and dissolved in the chemical solvent. When it came to selecting estimators, we tried three different approaches to correlate the CO₂ loading. Bagging and boosting models, both of which are subclasses of ensemble techniques are used in these models. When using ensemble techniques, a number of weak models are combined to build a strong and robust model for prediction of solubility values. There are a variety of models that are utilized including random forests (RF), extreme randomized trees (ERT), and boosted K-NN (with Adaboost). We repeated the procedure multiple times in order to obtain the best model, from which we could then establish the right hyper-parameters for each one of the models. Following optimization, the R² scores for all three models above 0.9, suggesting that the models had high predictive performance. ERT had the highest R² score, which was 0.999, among all companies. R² of 0.992 was achieved by Random Forest, also we have Boosted KNN, which achieved an R² of 0.998.