Abstract
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.
Highlights
The atmospheric and vacuum distillation unit has a large impact on the overall operation and processing capacity of the refinery as the first processing unit
Crude naphtha usually contains much Cl ions and sulfur ions, which forms a corrosive environment of HCl-H2S-H2O at high temperatures, causing corrosion attack on the naphtha and gas system of atmospheric distillation tower
The pH value, Cl ion concentration, Fe ion concentration, and sulfide concentration of the sewage discharged from the top of the atmospheric tower are selected as training data, and the average corrosion rate is used as the output data
Summary
The atmospheric and vacuum distillation unit has a large impact on the overall operation and processing capacity of the refinery as the first processing unit. Crude naphtha usually contains much Cl ions and sulfur ions, which forms a corrosive environment of HCl-H2S-H2O at high temperatures, causing corrosion attack on the naphtha and gas system of atmospheric distillation tower. The corrosion prediction for naphtha and gas pipelines on top of atmospheric distillation tower is of great significance to the production and maintenance of the devices. In the actual production environment, the influence of various factors on corrosion is complicated and it is difficult to establish an accurate mathematical model. The. BP neural network can determine the mathematical model for corrosion through its own strong learning ability and multi-factor cooperation ability and predict the corrosion rate. The BP neural network was optimized by genetic algorithm, improving the prediction accuracy of corrosion rate and it provides reference for corrosion detection [2]
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