Application of artificial neural networks and genetic algorithms for crude fractional distillation process modeling
This work addresses process modeling for refinery operations, but it is incremental as it applies existing methods to a specific industrial dataset.
The researchers tackled modeling crude distillation processes by developing artificial neural networks optimized with genetic algorithms to predict fraction quality, achieving accurate predictions that matched standard laboratory analysis results.
This work presents the application of the artificial neural networks, trained and structurally optimized by genetic algorithms, for modeling of crude distillation process at PKN ORLEN S.A. refinery. Models for the main fractionator distillation column products were developed using historical data. Quality of the fractions were predicted based on several chosen process variables. The performance of the model was validated using test data. Neural networks used in companion with genetic algorithms proved that they can accurately predict fractions quality shifts, reproducing the results of the standard laboratory analysis. Simple knowledge extraction method from neural network model built was also performed. Genetic algorithms can be successfully utilized in efficient training of large neural networks and finding their optimal structures.