Empowering Piping Integrity with AI-Based Corrosion Prediction Tool

In this section

Back to Core Research Programme AMorph – 4D Printing for Field Morphing Structures Automation of Composite and Hybrid Joining Process Coaxial Wire Additive Manufacturing Processes and In-Line Monitoring Tools Development of Coded Excitation Technique for Non-Metallic Pipes Inspection Development of Digital Twin technology as a demonstrator for real-time integrity assessment of crack growth in tensile specimens Development of Friction Stir Welding (FSW) for Hydrogen Fuel Storage Tanks Development of Leak-Before-Break Hydrogen Storage Composite Pressure Vessels With Polymeric Liner Establishing Assessment Methods for Determining Safe Service Limits of High-Temperature Materials in Extreme Environments Fracture toughness in aggressive environments: effect of crack monitoring techniques on test results In-process detection and non-destructive testing of electron beam weld imperfections Internal Bore Coatings for Applications in Corrosion and Hydrogen Environment Joining of Additively Manufactured Materials Long term behaviour of polymeric liner materials/coatings in hydrogen service Techniques for High Temperature Ultrasonic Inspection of Arc Welding Ultimate Leverage Fund

Project Code: 35588

Start date and planned duration: August 2023, 5 months

Objective

Integration and Prioritization: Incorporate external corrosion data into the CorrosionWISE model to quantify damage severity and prioritize maintenance based on different corrosion levels.
Enhanced ML Model: Upgrade the model with industry standards for Fitness for Service assessments, providing a clear pass/fail verdict for corroded pipes.
Remaining Life Estimation: Develop algorithms to estimate the remaining life of even non-accessible pipes using external and internal corrosion rates.
Validation and Superiority: Validate the model's performance using real-world data and demonstrate its superiority over conventional corrosion assessment methods.
User-Friendly Interfaces: Provide user-friendly interfaces and visualizations to aid industry professionals in easy interpretation and decision-making.

Project Outline

The report's structure includes addressing corrosion challenges specifically in upstream facilities, discussing limitations of traditional inspection methods focusing on non-accessible pipes, introducing the CorrosionWISE model, proposing an external corrosion prediction model through a ML algorithm, and emphasizing benefits for the industry.

Industry Sectors

Oil and Gas Industry: The screening corrosion tool contributes to the oil and gas industry's potential for achieving operational excellence, enhancing asset integrity management.
SMEs and Upstream Production Companies: The main beneficiaries of the project are expected to be SMEs and Upstream production companies.
Universities: The concept can be extended to universities for similar modeling and software development purposes.
Engineering and Technology Companies: Collaborative parties are interested in integrating the application into their software and marketing it globally, indicating relevance to the engineering and technology sectors.

Benefits to Industry

CorrosionWISE, a cutting-edge ML model, predicts internal corrosion rates in piping systems with limited accessibility. The integration of an external corrosion prediction model enhances CorrosionWISE by accurately forecasting external corrosion rates, leading to improved analysis, remaining life estimation, and risk management.

In conclusion, the report showcases a groundbreaking advancement in corrosion assessment and asset management through the integration of advanced technology, data-driven insights, and a holistic approach. This innovative solution promises to revolutionize decision-making, optimize inspection schedules, and enhance the safety and efficiency of piping systems in diverse environments.