Corrosion Prediction for Naphtha and Gas System of Atmospheric Distillation Tower Based on Artificial Neural Network and Genetic Algorithm

摘要

The corrosion of low-temperature sections of a company's atmospheric and vacuum distillation unit was analyzed. Corrosion rate prediction model was established using BP neural network based on the corrosion detection data detected in the sewage on top of the tower over a period of time. In this model, the pH value, Cl ion concentration, Fe ion concentration and sulfide concentration of the sewage discharged from the top of the tower are taken as the input data, and the average corrosion rate as the output data, the results show that the prediction error is large. The BP neural network was optimized using the genetic algorithm. The optimized model could accurately predict the corrosion of the atmospheric unit at low temperatures. The corrosion rate prediction model was used to investigate the effect of each variable on the corrosion rate through the single factor change and the results could reflect the relationship between detected corrosion data and corrosion rate in the sewage on top of the atmospheric tower.