The Role of Machine Learning in Predicting and Preventing Corrosion in Industrial Pipelines

Abstract

Industrial pipelines are susceptible to corrosion, leading to leaks, equipment failures, and environmental hazards. This paper investigates the application of machine learning algorithms to predict corrosion rates in pipelines based on factors such as material properties, operating conditions, and environmental data. The study develops a machine learning model that can accurately identify pipeline segments at high risk of corrosion, enabling proactive maintenance and prevention measures.