On March 15, 2021, Aniceto, Jose P. S.; Zezere, Bruno; Silva, Carlos M. published an article.Related Products of 321-28-8 The title of the article was Machine learning models for the prediction of diffusivities in supercritical CO2 systems. And the article contained the following:
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. The experimental process involved the reaction of 1-Fluoro-2-methoxybenzene(cas: 321-28-8).Related Products of 321-28-8
The Article related to carbon dioxide supercritical system machine learning diffusivity, Phase Equilibriums, Chemical Equilibriums, and Solutions: Fused Salts and other aspects.Related Products of 321-28-8
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Ether | (C2H5)2O – PubChem