Process (computing)

Engineering

Extreme learning machine

Multivariate statistics

Metallurgy

Materials science

Petroleum engineering

Operating system

Computer science

Regression analysis

Mechanical engineering

Environmental engineering

Physics

Multivariate adaptive regression splines

Nuclear engineering

Pipeline (software)

Astronomy

Pitting corrosion

Mars Exploration Program

Kriging

Nonparametric regression

Environmental science

Artificial intelligence

Pipeline transport

Corrosion

Artificial neural network

Forensic engineering

Machine learning

Process Safety and Environmental Protection

The main objective of this paper is to develop accurate novel frameworks for the estimation of the maximum pitting corrosion depth in oil and gas pipelines based on data-driven techniques. Thus, different advanced approaches using Artificial Intelligence (AI) models were applied, including Artificial Neural Network (ANN), M5 Tree (M5Tree), Multivariate Adaptive Regression Splines (MARS), Locally Weighted Polynomials (LWP), Kriging (KR), and Extreme Learning Machines (ELM). Additionally, a total of 259 measurement samples of maximum pitting corrosion depth for pipelines located in different environments were extracted from the literature and used for developing the AI-models in terms of training and testing.Furthermore, an investigation was carried out on the relationship between the maximum pitting depths and several combinations of probable factors that induce the pitting growth process such as the pipeline age, and the surrounding environmental properties. The results of the proposed AI-frameworks were compared using various criteria. Thus, statistical, uncertainty and external validation analyses were utilized to compare the efficiency and accuracy of the proposed AI-models and to investigate the main contributing factors for accurate predictions of the maximum pitting depth in the oil and gas pipeline.

Advanced intelligence frameworks for predicting maximum pitting corrosion depth in oil and gas pipelines