Modeling CO2 loading capacity of triethanolamine aqueous solutions using advanced white-box approaches: GMDH, GEP, and GP

Abstract

AbstractThe equilibrium solubility of carbon dioxide (CO2) in the solvents is a key essential characteristic that has to be evaluated for successful absorption-based CO2 capture procedures. In this study, the CO2 loading capacity of triethanolamine (TEA) aqueous solutions was estimated using three famous white-box algorithms namely gene expression programming (GEP), genetic programming (GP), and group method of data handling (GMDH). For achieving the aim of this study, 258 data in a wide range of pressure, temperature, and amine concentration were collected from literature. Temperature, partial pressure of CO2, and amine concentration were used as input parameters. The results demonstrated that GMDH correlation is more accurate than GEP and GP with a determination coefficient (R2) of 0.9813 and root mean square error of 0.0222. The R2 values of 0.9713 and 0.9664 for the GEP and GP, respectively, demonstrated that the GEP and GP also showed accurate predictions. In addition, GMDH approach accurately predicted the anticipated trends of the CO2 loading in response to changes in the partial pressure of CO2 and temperature. The Pearson and Spearman correlation analyses were also incorporated in this research which showed that temperature and CO2 partial pressure have almost the same relative effect on CO2 loading, while amine concentration has the lowest effect on it.