8 | CONCLUSION
The first, second and third dissociation constants of eight amines of
importance in carbon capture operations, namely,
N-(2-aminoethyl)-1,3-propanediamine (n-2AOE13PDA),
2-Methyl-pentamethylene diamine (2-MPMDA); N,
n-dimethyldipropylenetriamine (DMAPAPA) and
3,3’-Diamino-n-methyldipropylamine (DAOMDPA),
Bis[2-(n,n-dimethylamino)ethyl]ether (2DMAOEE),
2-[2-(Dimethylamino)ethoxy]ethanol (DMAOEOE),
2-(Dibutylamino)ethanol (DBEA) and N-propylethanolamine (PEA) were
measured in a temperature range varying from 298.15 K to 313.15 K with 5
K increments. As expected, the dissociation constant values for all
studied amines decreased with increasing temperature. The study shows
that all amines studied have a higher pKa than
MEA. 2-MPMDA, with two primary amino groups, had the highest
pKa1 and the second
pKa 2. It should be an amine of
great interest in carbon capture operations. DAOMDPA, with two primary
and a tertiary amino group, also had a high pKavalue. A tertiary amine, Methyldiethanolamine pKa values were the
lowest. Using computational chemistry calculations, the first protonated
positions for the studied amines were predicted and the results agreed
well with the literature.
In terms of modelling, the first pKa values of
the studied amines at 298.15K were estimated using the original and
modified PDS but also with the Qian-Sun-Sun-Gao (QSSG) method. This
method provided the best estimation of pKa values when compared to the
experimental data.
Finally, an artificial neural network (ANN) was developed to predict the
values of the dissociation constants for the temperature range studied
in this work. The input data included the molecular weight, critical
temperature, and pressure to identify the compounds as well as
temperature as pKa values are
temperature-dependent. In addition, the density, dynamic viscosity,
refractive index and surface tension were also used as inputs. The
predicted values were in very good agreement with the experimental
values. An optimum ANN architecture of 8-5-7-1 was selected, its
predicted outputs were in a good agreement with targets, with a
regression coefficient of 0.99424 and a mean squared error for training,
validation and testing of 2.20E-05, 0.0094 and 0.0078, respectively.
The full ANN model was further simplified and optimized by only
including the surface tension and the refractive index as inputs.