Monitoring the effect of surface functionalization on the CO2 capture by graphene oxide/methyl diethanolamine nanofluids

Abstract

Different processes exist to capture carbon dioxide (CO2) and reduce its undesirable effects on the atmosphere. Stable suspensions of graphene oxide (GO) nanosheets in aqueous methyl diethanolamine (MDEA) solutions have recently attracted great attention as a potential CO2 absorption medium. Moreover, experimental analyses confirmed that GO surface functionalization positively affects the CO2 absorption by MDEA-based nanofluids. To the best of our knowledge, there are no mathematical models to investigate the effect of surface functionalization on the CO2 capture ability of GO-amine nanofluids. Artificial intelligence (AI) techniques are reliable methodologies for understanding behavior of even the most complex systems. Therefore, different AI models are designed to reveal the effect of GO surface functionalization on the CO2 capture ability of MDEA-based nanofluids. Our AI models use operating temperature, pressure, functionalized group, and GO dosage in the amine solutions to predict CO2 solubility in GO/MDEA nanofluids. The results confirm that the cascade feedforward (CFF) neural network is the most accurate AI paradigm for estimating CO2 solubility in aqueous GO/MDEA nanofluids in a wide range of operation conditions (i.e., AARD = 1.78%, MSE = 0.007, RMSE = 0.08, and R2 = 0.9906). Simulation results justified that the surface functionalization of the GO nanosheets by the NH2 group provides the most promising results for CO2 capture by the GO/MDEA nanosuspensions.