Aniceto, Jose P. S. published the artcileMachine learning models for the prediction of diffusivities in supercritical CO2 systems, Application of Benzyl acetate, the main research area is carbon dioxide supercritical system machine learning diffusivity.
The mol. diffusion coefficient is fundamental to estimate dispersion coefficients, convective mass transfer coefficients, etc. Since exptl. diffusion data is scarce, there is significant demand for accurate models capable of providing reliable diffusion coefficient estimations In this work we applied machine learning algorithms to develop predictive models to estimate diffusivities of solutes in supercritical carbon dioxide. A database of exptl. data containing 13 properties for 174 binary systems totaling 4917 data points was used in the training of the models. Five machine learning algorithms were evaluated and the results were compared with three commonly used classic models. The best results were found using the Gradient Boosted algorithm which showed an average absolute relative deviation (AARD) of 2.58% (pure prediction). This model has five parameters: temperature, d., solute molar mass, solute critical pressure and solute acentric factor. For the same dataset, the classic Wilke-Chang equation showed AARD of 12.41%. The developed model is provided as command line program.
Journal of Molecular Liquids published new progress about Acentric factor. 140-11-4 belongs to class esters-buliding-blocks, name is Benzyl acetate, and the molecular formula is C9H10O2, Application of Benzyl acetate.
Referemce:
Ester – Wikipedia,
Ester – an overview | ScienceDirect Topics