Journal Article

CO2 Corrosion Rate Prediction for Submarine Multiphase Flow Pipelines Based on Multi-Layer Perceptron

Shi, Lihong, Wang, Guoqing, Wang, Changquan

2022

Overview

The implementation of corrosion detection in submarine pipelines is difficult, and a combined PCA-MLP prediction model is proposed to improve the accuracy of corrosion prediction in submarine pipelines. Firstly, the corrosion rate of a submarine multiphase flow pipeline in the South China Sea is simulated by the De Waard 95 model in the multiphase flow transient simulation software OLGA and compared with the actual corrosion rate; then, according to the corrosion data simulated by OLGA, principal component analysis (PCA) is used to reduce the dimensionality of the corrosion factors in the pipeline, and the multiple linear regression model (MLR), multi-layer perceptron neural network (MLPNN), and radial basis function neural network (RBFNN) were optimized. The PCA-MLPNN model has an average relative error of 3.318%, an average absolute error of 0.0034, a root mean square error of 0.0082, a residual sum of squares of 0.0020, and a coefficient of determination of 0.8609. Compared with five models, including MLR, MLPNN, RBFNN, PCA-MLR, PCA-MLPNN, and PCA-RBFNN, PCA-MLPNN has higher prediction accuracy and better prediction performance. The above results indicate that the combined PCA-MLPNN model has a more reliable application capability in CO2 corrosion prediction of submarine pipelines.

• Publisher: MDPI AG
• Subject: Accuracy / Algorithms / Boundary conditions / Carbon dioxide / Corrosion / Corrosion rate / Discriminant analysis / Errors / Fuzzy logic / Independent variables / Modelling / multi-layer perceptron / Multilayer perceptrons / Multilayers / Multiphase flow / Natural gas / Neural networks / Neurons / Pipelines / Prediction models / principal component analysis / Principal components analysis / Radial basis function / Random variables / Regression analysis / Regression models / Simulation / Stainless steel / submarine pipeline / Submarine pipelines / Support vector machines / Underwater pipelines / Underwater structures