Amino acid salt (AAs)
aqueous solutions have recently exhibited
a great potential in CO
2
absorption from various gas mixtures.
In this work, four hybrid machine learning methods were developed
to evaluate 626 CO
2
and AAs equilibrium data for different
aqueous solutions of AAs (potassium sarcosinate, potassium
l
-asparaginate, potassium
l
-glutaminate, sodium
l
-phenylalanine, sodium glycinate, and potassium lysinate) gathered
from reliable references. The models are the hybrids of the least
squares support vector machine and coupled simulated annealing optimization
algorithm, radial basis function neural network (RBF-NN), particle
swarm optimization–adaptive neuro-fuzzy inference system, and
hybrid adaptive neuro-fuzzy inference system. The inputs of the models
are the CO
2
partial pressure, temperature, mass concentration
in the aqueous solution, molecular weight of AAs, hydrogen bond donor
count, hydrogen bond acceptor count, rotatable bond count, heavy atom
count, and complexity, and the CO
2
loading capacity of
AAs aqueous solution is considered as the output of the models. The
accuracies of the models’ results were verified through graphical
and statistical analyses. RBF-NN performance is promising and surpassed
that of other models in estimating the CO
2
loading capacities
of AAs aqueous solutions.
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