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

Author(s)

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

Abstract

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.

Organisation(s)

Functional and Evolutionary Ecology

External organisation(s)

Katholieke Universiteit Leuven, University of Antwerp

Journal

Journal of CO2 Utilization

Volume

70

ISSN

2212-9820

DOI

https://doi.org/10.1016/j.jcou.2023.102452

Publication date

04-2023

Peer reviewed

Yes

Austrian Fields of Science 2012

204003 Chemical process engineering, 207101 Waste engineering, 102019 Machine learning

Keywords

ASJC Scopus subject areas

Chemical Engineering (miscellaneous), Waste Management and Disposal, Process Chemistry and Technology

Portal url

https://ucris.univie.ac.at/portal/en/publications/predicting-overall-mass-transfer-coefficients-of-co2-capture-into-monoethanolamine-in-spray-columns-with-hybrid-machine-learning(5e4a72c9-b7a6-4f6f-bafe-77dd7abafc80).html