data-driven corrosion prediction models in industrial operations

The rapid advancement of data-driven techniques has revolutionized the field of corrosion prediction, offering enhanced accuracy, reliability, and efficiency over traditional methods. this paper provides a comprehensive review of the latest developments in data-driven corrosion prediction models, emphasizing their application in industrial operations. it explores the transition from empirical and mechanistic approaches to advanced machine learning algorithms, including artificial neural networks (anns), support vector machines (svms), and ensemble models. the integration of hybrid optimization techniques, such as genetic algorithms (ga), particle swarm optimization (pso), and simulated annealing (sa), is highlighted for their ability to further refine predictive accuracy. case studies from recent research (2022-2024) demonstrate the practical applications and benefits of these models in predicting fatigue life, managing galvanic corrosion, and enhancing corrosion resistance through advanced coatings. despite significant progress, challenges such as data quality, model integration, and real-time application remain. future directions suggest focusing on improved data collection methods, hybrid models, and digital twin technology to provide real-time, proactive corrosion management. this paper underscores the potential of data-driven models to transform corrosion prediction and management, ultimately enhancing the safety, reliability, and longevity of industrial assets.