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Abstract
Acid gas chemical absorption with aqueous amine solvents is an important industrial technology for gas processing and CO2 capture. Vapor-Liquid Equilibrium (VLE) reflects the efficiency of solvents and is essential to model the thermodynamics of the absorption and solvent regeneration process. In this study, several machine learning (ML) approaches were used to develop VLE models for acid gas absorption in aqueous methyldiethanolamine (MDEA) and piperazine (Pz), namely CO2-MDEA-H2O, H2S-MDEA-H2O, CO2-H2S-MDEA-H2O and CO2-Pz-H2O systems. New experimental data are presented for the CO2-MDEA-H2O and H2S-MDEA-H2O ternary systems, and they are used to compare the accuracy of the ML models to an earlier reported activity coefficient (e-NRTL)-based thermodynamic model. For the quaternary system CO2-H2S-MDEA-H2O, the ML models and the physical model are compared using experimental data from literature, because the physical model of the quaternary system is only trained on the ternary experimental systems. The results indicate that the optimal ML approach is not systematically more accurate than the physics-based model.
Supplementary materials
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supplementary information
Description
Additional data is provided via Supporting Information. These are: MLP model’s hyperparameters values for model training (Table S1); the ranges of variation of amine concentration and temperature (Table S2); example of unphysical fluctuation in ML-predicted VLE curve from literature (Figure S1); example of the test set selection in CO2-Pz-H2O (Figure S2); examples of models’ predictions (Figures S3-S4); interpretation of parameters for sigmoidal shape curve (Figure S5); flow diagram of the experiment apparatus used in the study (Figure S6); example of the fitted VLE curves for CO2-Pz-H2O (Figure S7). A separate file containing training and test sets for ML is provided.
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