A new hybrid algorithm model for prediction of internal corrosion rate of multiphase pipeline

Abstract

Pipeline plays an important role in the oil and gas transportation industry. In recent years, more and more pipeline damages and breakdowns are caused by corrosion, which hurts the normal operation. Accurate prediction of the pipeline's corrosion rate is of great significance for the pipeline to operate safely and soundly. In this study, a hybrid intelligent algorithm method is proposed to predict the corrosion rate of the multiphase flow pipeline. The proposed model combines support vector regression (SVR), principal component analysis (PCA), and chaos particle swarm optimization (CPSO), named PCA-CPSO-SVR. PCA can reduce the data dimension and screen out the main variables of corrosion influencing factors. CPSO is utilized to optimize the hyperfine parameters in SVR, thus improving the prediction accuracy of the prediction model. The mean absolute error of the proposed model is 0.083, which is 18.6% lower than that of SVR. Compared with five benchmark models including linear regression (LR), artificial neural network (ANN), PCA-genetic algorithm-SVR, PCA-PSO-SVR, and De warred95(OLGA), the proposed model has higher prediction accuracy. According to the above results, PCA-CPSO-SVR has a good performance in the prediction of the corrosion rate of the multiphase flow pipeline.