Characterizing CO2 capture with aqueous solutions of LysK and the mixture of MAPA + DEEA using soft computing methods

Abstract

Accurate data in the field of CO2-capture using new high potential absorbents as alternatives to the traditional ones is of great interest within scientific and engineering communities. In this direction, two robust modeling strategies, viz. Stochastic Gradient Boosting (SGB) tree and Genetic Programming (GP) are used to 1) predict the solubility of CO2 in aqueous potassium lysinate (LysK) solutions as a function of temperature, partial pressure of CO2, and the mass fraction of LysK; and 2) predict the solubility of CO2 in the mixture of MAPA + DEEA aqueous solutions as a function of temperature, partial pressure of CO2, and the concentration of MAPA and DEEA based on previously published data. The efficiency and precision of the proposed models are checked graphically and statistically. Results show that both proposed models are competent in accurate and reliable predictions (R2 > 0.98 and RMSE < 0.06). However, the SGB models are superior to the GP models. Additionally, the proposed models are compared to the modified Kent-Eisenberg model for predicting the CO2 solubility in LysK solutions, and shown to have better outputs.