Prediction of liquid phase equilibria for aqueous mixture of butyric acid with linear alcances: A mathematical modeling

A GMDH type-neural network was used to predict liquid phase equilibrium data for the {water + butyric acid +linear alcanes (n-hexane, n-heptane and n-octane)} ternary systems at T=298.2 K. In order to accomplish modeling, the experimental data were divided into train and test sections. The data set was divided into two parts: 70% were used as data for ‘‘training’’ and 30% were used as a test set. The predicted values were compared with those of experimental values in order to evaluate the performance of the GMDH neural network method. The results obtained by using GMDH type neural network are in excellent agreement with the experimental results

کلیدواژه ها:

LLE data ، Ternary mixture ، Genetic algorithm ، GMDH-type NN