A PSO-RBF prediction method on flow corrosion of heat exchanger using the industrial operations data

Abstract

With the development of the petrochemical industry, corrosion problems of special equipment occur frequently. How to achieve accurate prevention and control of equipment flow corrosion problems become a key factor in ensuring the safety of equipment processes. This paper combines industrial field operation data, through the isolated forest algorithm for the screening of abnormal values, focusing on the analysis of the type of corrosion in the heat exchanger. A Particle Swarm Optimization optimized Radial Basis Function (PSO-RBF) prediction model was constructed to achieve fast prediction of ammonium crystallization temperature, and the Particle Swarm Optimization (PSO) algorithm was optimized using an adaptive weight optimization scheme. It shows that the optimized model error is within 3%, while the prediction accuracy is improved by 9.54%, and the coefficient of determination has also been improved. This paper also compares and analyzes several mature algorithms commonly used today, and the results show that the model has significant advantages in terms of prediction performance. In addition, the model can be applied to the condition inspection of petrochemical equipment, providing a practical guarantee for equipment operation, and reducing the risk of enterprise operation and maintenance.