Predictive Study of Flow-Accelerated Corrosion Characteristic Parameters Based on the Neural Network

Abstract

Corrosion of equipment by corrosive media is widespread in the processing of inferior crude oil. In hydroprocessing reactor effluent systems, corrosive media are very destructive to heat exchangers and air coolers during flow and cooling because of the high-temperature and -pressure environment. A fire and explosion in the air cooler or heat exchanger are highly likely when their tubes leak. Currently, there are no effective direct detection and prediction means to evaluate the corrosion risk in real time, creating significant hidden threats to the safe operation of the equipment. Therefore, this paper proposes a condition expansion method based on a Gaussian distribution. The distribution laws of characteristic corrosion parameters under various working conditions were studied, and the corrosion risk of the equipment was evaluated. A three-layer back-propagation neural network model is constructed to predict the characteristic corrosion parameters. After testing, the model is shown to have superior predictive accuracy and generalization performance. It can also meet the demand for real-time equipment corrosion prediction. The proposed method can serve an essential role in guiding engineers to take correct and timely prevention and control measures for different degrees of corrosion to reduce losses.