Predicting burst pressure of defected pipeline with Principal Component Analysis and adaptive Neuro Fuzzy Inference System

Pipeline is an important and valuable infrastructure for transporting oil and gas which expanding a long distance and working in a corrosive environment. Consequently, corrosion becomes one of the most critical threads for metal material pipeline. The high internal pressure in an oil and gas pipeline is the additional factor leading to the high risk of bursting. Various models predicting the burst pressure of defected pipeline have been developed in literature. However, evaluating burst pressure of defected pipe is a nonlinear mechanical problem with the appearance of the stress concentration, accuracy of the existing models is not high and the issue still open. The application of data-driven approach with soft computing and machine learning has been a potential and promising approach. This paper investigates the application of Adaptive Neuro Fuzzy Inference System (ANFIS) and a data transforming technique for dimension reduction and noise elimination, the Principal Component Analysis (PCA). The PCA has demonstrated its ability in noise removal for the database and ANFIS provides an improvement in the accuracy of the prediction. The developed model is the combination of ANFIS and PCA, the ANFIS-PCA model, has overwhelmed other existing models by archiving the correlation of determination at 0.9919 and the Root Mean Square Error decreases to 0.9883 MPa. Observations on the difference network configurations and number of epochs also provided. © 2020