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 mono-ethanolamine (MEA) solutions. The process is mostly studied in packed columns, for which many correlations have been offered to predict overall mass transfer coefficients (KGa). Spray columns are less prone to corrosion and were shown to enhance KGa. However, to the best of our knowledge, there are no models to predict KGa in spray columns. Hybrid modelling tools, a combination of machine learning techniques and first-principle in-formation, 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 pi-theorem, which is the most used state of the art technique to model KGa based on dimensionless numbers. SVMr and DTr showed higher accuracies among the trained models on the test set. SVMr can predict KGa 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 pi-theorem. Predicting KGa with a higher accuracy allows more control over operational parameters and better column designs.