Predicting overall mass transfer coefficients of CO<sub>2</sub> capture into monoethanolamine in spray columns with hybrid machine learning

Ulderico Di Caprio, Min Wu, Florence Vermeire, Tom Van Gerven, Peter Hellinckx, Steffen Waldherr, Emine Kayahan, M. Enis Leblebici

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 (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 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 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 Π-theorem. Predicting KGa with a higher accuracy allows more control over operational parameters and better column designs.

Functional and Evolutionary Ecology
External organisation(s)
Katholieke Universiteit Leuven, University of Antwerp
Journal of CO2 Utilization
Publication date
Peer reviewed
Austrian Fields of Science 2012
204003 Chemical process engineering, 207101 Waste engineering, 102019 Machine learning
ASJC Scopus subject areas
Chemical Engineering (miscellaneous), Waste Management and Disposal, Process Chemistry and Technology
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