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Analysis of equilibrium CO 2 solubility in aqueous APDA and its potential blends with AMP / MDEA for postcombustion CO 2 capture
Author(s) -
Dey Anirban,
Mandal Bishnupada,
Dash Sukanta K.
Publication year - 2020
Publication title -
international journal of energy research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.808
H-Index - 95
eISSN - 1099-114X
pISSN - 0363-907X
DOI - 10.1002/er.5404
Subject(s) - solubility , aqueous solution , chemistry , surface tension , thermodynamics , work (physics) , partial pressure , response surface methodology , atmospheric temperature range , materials science , chemical engineering , chromatography , organic chemistry , physics , oxygen , engineering
Summary The utility of polyamine‐based solvent‐activators for the possible application in postcombustion CO 2 capture technology has drawn considerable attention recently owing to its higher loading capacity as well as superior kinetics. The current work involves a comprehensive experimental cum theoretical investigation on the equilibrium solubility of CO 2 pertaining to aqueous N ‐(3‐aminopropyl)‐1,3‐propanediamine and its blends with N ‐methyldiethanolamine and 2‐amino‐2‐methyl‐1‐propanol. The analysis was conducted within the operating temperature and CO 2 partial pressure range of 303.2‐323.2 K and 2‐200 kPa, respectively. Two different mathematical models based on nonrigorous approaches such as equilibrium based modified Kent‐Eisenberg (KE) model and a multilayer feedforward neural network model have been developed to correlate the CO 2 solubility data over a wide range of experimental conditions. Both the model predictions are well‐validated with the experimental results. The reaction scheme as well as the prevalence of important reaction products was further confirmed with qualitative 13 C NMR as well as ATR‐FTIR analysis. Apart from these some of the important thermally induced transport properties viz, density, viscosity, and surface tension of the aqueous single and blended systems were measured and correlated with various consistent empirical models such as Redlich‐Kister and Grunberg‐Nissan model while surface tension data are modeled using temperature‐based multiple linear regression technique.