The
xylitol molecule is an important building block that can be
used in the production of such interesting chemicals as ethylene glycol
and 1,2-propylene glycol. The development of productive processes
that enable this transformation depends on various experimental and
theoretical information. In order to supply part of this demand, this
work sought to study the solubility of xylitol in binary liquid solutions
formed by water, ethylene glycol, and 1,2-propylene glycol in the
temperature range between 293.15 and 323.15 K, covering the entire
molar composition range of the solution. The Jouyban–Acree,
NRTL, and UNIQUAC models were used in the correlation of experimental
data, and the mUNIFAC model was applied in the prediction of experimental
data. In addition, an artificial neural network associated with molecular
descriptors was developed to simulate the data. Xylitol showed solubility
in the pure components with decreasing values in the following order:
water, ethylene glycol, and 1,2-propylene glycol. The solubility in
binary solutions had intermediate values according to the intermediate
concentration values. The models used proved capable of correlating
or predicting the experimental data. The artificial neural networks
had a satisfactory performance in the data simulation, and the best
observed architecture used four layers of the type 7-3-3-1.