A New Neural Network Group Contribution Method for Estimation of Upper Flash Point of Pure Chemicals was written by Gharagheizi, Farhad;Abbasi, Reza. And the article was included in Industrial & Engineering Chemistry Research in 2010.Recommanded Product: Ethyl 3-ethoxypropanoate The following contents are mentioned in the article:
A new group contribution-based models is presented to predict the upper flash point temperature of pure compounds based on a large dataset (1294). This neural network model uses several occurrences of 122 chem. groups in a pure compound to predict its related upper flash point limit. The model squared correlation coefficient, average percent error, mean average error, and root-mean-square error over the main dataset (1294 pure compounds) were 0.99, 1.7%, 6, and 8.5, resp. This study involved multiple reactions and reactants, such as Ethyl 3-ethoxypropanoate (cas: 763-69-9Recommanded Product: Ethyl 3-ethoxypropanoate).
Ethyl 3-ethoxypropanoate (cas: 763-69-9) belongs to esters. Carboxylic acid esters of low molecular weight are colourless, volatile liquids with pleasant odours, slightly soluble in water. Cyclic esters are called lactones, regardless of whether they are derived from an organic or inorganic acid. One example of an organic lactone is γ-valerolactone.Recommanded Product: Ethyl 3-ethoxypropanoate
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Ester – an overview | ScienceDirect Topics