The effects of blending diisopropanolamine and 2-amino-2-methyl-1-propanol for carbon dioxide capture process: Comparative study using thermodynamic modeling and artificial neural network predictions
Year: 2025
Type: article
Abstract: Amine-based absorption processes represents a promising strategy for CO 2 capture. This study systematically examined the CO 2 solubility, absorption behaviors, and regeneration energy ( Q regen ) of aqueous blended-amine solutions of diisopropanolamine (DIPA) and 2-amino-2-methyl-1-propanol (AMP) solutions. CO 2 solubility was experimentally measured across five blending ratios (30:0:70 to 0:30:70 (w/w)), four temperatures of (313–383 K), and CO 2 partial pressure up to 450 kPa. The data were well-correlated with both the electrolyte non-random two-liquid (e-NRTL) thermodynamic model and the artificial neural network (ANN) model. The model-physical alignment score was suggested as a novel metric to ensure consistency with fundamental absorption behaviors. While the thermodynamic model provided mechanistic insights, the ANN model offered a simpler, computationally efficient approach. Both models successfully generalized with a reduced dataset, reinforcing their applicability in absorbent screening. AMP exhibited superior CO 2 solubility, whereas DIPA offered benefits of lower pH and reduced heat of absorption. The absorption behavior of aqueous blended-amine solutions was characterized by prioritized AMPH + formation, leading to an AMP-dominated system at low CO 2 loading ratios. The analysis of Q regen , including sensible, reaction, and latent heats, revealed that the lowest Q regen was achieved from the single AMP system. The addition of 5 wt% DIPA resulted in only an 8 % increase in Q regen , still achieving a 33.4 % reduction compared to MEA . These findings highlight aqueous DIPA-AMP solutions as energy-efficient and chemically stable absorbents.
Source: Journal of Cleaner Production
Language: English
Subfield: Mechanical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $4,660
Award: G3270186386
OpenAlex ID: W4409253678
