Abstract
A novel approach to modeling prediction of phase equilibrium is presented. The method, evolutionary polymorphic neural network (EPNN), is developed by the authors on the basis of artificial neural networks and evolutionary computing. The system poly(ethylene glycol) (PEG)/potassium phosphate/water at pH = 7 was selected to demonstrate the performance of the model. The results were favorable as compared to a traditional neural network modeling approach and the experimental data set. Seven distinct data sets of varying PEG molecular weights were used in this work. Of the seven, five were used for training, while the remaining two were employed as the test cases. Following the training, a networked symbolic equation system evolved, which, in addition to reproducing the data, can also be used to improve understanding of the phase diagram mechanism through the discovered parameters.
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