Thermodynamic properties of binary, ternary and quaternary mixtures: N‑butyl acetate- n-hexanol - n,n-dimethylacetamide-water. Modeling using regression algorithms and optimization using socially-inspired evolutionary algorithms

Background

The design of chemical plants requires knowledge of the thermodynamic properties of the fluids involved.

Methods

In our research, we performed experimental measurements of the refractive index and density of binary, ternary and quaternary systems containing n‑butyl acetate, n-hexanol, n,n-dimethylacetamide and water, at atmospheric pressure and the following temperatures: 293.15 K, 303.15 K, 313.15 K and 323.15 K. They were used to determine the excess molar volume, which was correlated with mole fractions, normalized temperature and refractive index, using artificial neural networks and other models obtained with regression algorithms, optimized with different socially-inspired evolutionary algorithms.

Significant Findings

The best results were achieved with decision tree regression and the queuing search optimization algorithm. The present approach is based on the fact that there are few similar studies in the literature; in addition, the models obtained can complement/supplement the experimental data through predictions.