Abstract:
Essential oils contain a complex mixture of organic compounds with unique scent
profiles and therapeutic effects. The increasing market demand for essential oils and
their components has led to a growing interest in optimizing batch distillation processes
for their fractionation. Since experimental approaches are time-consuming and resourceintensive,
researchers are resorting to modeling and simulation methods to improve
these separations. To achieve accurate simulations, thermodynamic property data are
crucial but challenging to obtain experimentally. Therefore, predictive methods have been
proposed to estimate these properties at different temperatures. This study proposes a
pathway to develop machine learning models for vapor pressure prediction in essential oil
fractionation simulations using data calculated through such predictive methods. The
models are trained using the data so calculated and then validated using experimental
data. Thirteen machine learning algorithms are employed, and their performance is
evaluated using various criteria. The performance of these machine learning models are
then compared with that of traditional interpolation techniques. The results demonstrate
that machine learning models provide a greater overall accuracy of vapor pressure
predictions than interpolation methods. Ensembled machine learning models are found to
be effective for some compounds but not superior to using its best performing singular
algorithm-based machine learning model counterpart. Though the proposed pathway
focuses on vapor pressure prediction for constituents in cinnamon leaf oil, it can also be
used to predict properties like enthalpy of vaporization and specific heat capacity relevant
to essential oil fractionation, for other types of essential oils as well.
