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- W2360803331 abstract "A group contribution model using artificial neural networks was established to predict the flash points of organic compounds.A set of 32 molecular groups and flash point were used as input and output parameters of neural networks,respectively,to study the correlation of molecular structures with flash points of organic compounds.The dataset was composed of 258 organic compounds,and randomly divided into a training set(216) and a testing set(42).The optimal condition of the neural networks was obtained by adjusting various parameters by trial-and-error.The results of the simulation with the final optimum BP neural networks [32-4-1] showed that the predicted flash points are in good agreement with the experimental data,with the absolute mean absolute error of 6.22K,and the absolute mean relative error of 2.24%,which are superior to those of traditional group contribution method.The proposed model can be used not only to reveal the quantitative relation between flash points and molecular structures but to predict the flash points of organic compounds for chemical engineering." @default.
- W2360803331 created "2016-06-24" @default.
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- W2360803331 date "2007-01-01" @default.
- W2360803331 modified "2023-09-23" @default.
- W2360803331 title "Prediction of Flash Point of Organic Compounds by Group Contribution Method using Artificial Neural Networksst" @default.
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