Predicting overall mass transfer coefficients of CO₂ capture into monoethanolamine in spray columns with hybrid machine learning

In order to avoid the catastrophic effects of global warming, we need to reduce CO₂ emissions. Currently, the most mature technology to reduce large industrial CO₂ emissions is the absorption of CO₂ into aqueous monoethanolamine (MEA) solutions. The process is mostly studied in packed columns, for which many correlations have been offered to predict overall mass transfer coefficients (K_Ga). Spray columns are less prone to corrosion and were shown to enhance K_Ga. However, to the best of our knowledge, there are no models to predict K_Ga in spray columns. Hybrid modelling tools, a combination of machine learning techniques and first-principle information, showed remarkable capabilities in modelling complex systems. In this work, we applied hybrid modelling techniques benchmarking performances of four regressors: Ridge regression, decision tree regressor (DTr), support vector machine regressor (SVMr) and fully connected artificial neural network (ANN). We compared the performances of these modelling techniques with a model developed using the Buckingham Π-theorem, which is the most used state of the art technique to model K_Ga based on dimensionless numbers. SVMr and DTr showed higher accuracies among the trained models on the test set. SVMr can predict K_Ga within 6.4% error on the test set, whereas the Buckingham modelling approach resulted in 83 % error. The use of machine learning techniques resulted in predictive models with higher accuracies compared to the Buckingham Π-theorem. Predicting K_Ga with a higher accuracy allows more control over operational parameters and better column designs.