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This work presents the application of the artificial neural networks, trainedand structurally optimized by genetic algorithms, for modeling of crudedistillation process at PKN ORLEN S.A. refinery. Models for the mainfractionator distillation column products were developed using historical data.Quality of the fractions were predicted based on several chosen processvariables. The performance of the model was validated using test data. Neuralnetworks used in companion with genetic algorithms proved that they canaccurately predict fractions quality shifts, reproducing the results of thestandard laboratory analysis. Simple knowledge extraction method from neuralnetwork model built was also performed. Genetic algorithms can be successfullyutilized in efficient training of large neural networks and finding theiroptimal structures.

Application of artificial neural networks and genetic algorithms for crude fractional distillation process modeling