A Numerical Corrosion Rate Prediction Method for Direct Assessment of Wet Gas Gathering Pipelines Internal Corrosion

• Main Authors: Wenlong Jia, Quanke Yao, Xia Wu, Kexi Liao
• Format: Article
• Language: English
• Published: MDPI AG 2012-10-01
• Series: Energies
• Subjects: numerical prediction; Artificial Neural Network; wet gas; pipeline; Internal Corrosion Direct Assessment
• Online Access: http://www.mdpi.com/1996-1073/5/10/3892

The paper introduces a numerical internal corrosion rate prediction method into the internal corrosion direct assessment (ICDA) process for wet gas gathering pipelines based on the back propagation (BP), the genetic algorithm (GA) and BP, and the particle swarm optimization and BP artificial neural networks (ANNs). The basic data were collected in accordance with the terms established by the National Association of Corrosion Engineers in the Wet Gas Internal Corrosion Direct Assessment (WG-ICDA) SP0110, and the corrosion influencing factors, which are the input variables of the ANN model, are identified and refined by the grey relational analysis method. A total of 116 groups of basic data and inspection data from seven gathering pipelines in Sichuan (China) are used to develop the numerical prediction model. Ninety-five of the 116 groups of data are selected to train the neural network. The remaining 21 groups of data are chosen to test the three ANNs. The test results show that the GA and BP ANN yield the smallest number of absolute errors and should be selected as the preferred model for the prediction of corrosion rates. The accuracy of the model was validated by another 54 groups of excavation data obtained from pipeline No. 8, whose internal environment parameters are similar to those found in the training and testing pipelines. The results show that the numerical method yields significantly better absolute errors than either the de Waard 95 model or the Top-of-Line corrosion model in WG-ICDA when applying the approach to specific pipelines, and it can be used to investigate a specific pipeline for which the data have been collected and the ANN has been developed in WG-ICDA SP0110.