COURSE OVERVIEW

 

This practical and highly-interactive course includes various practical sessions and exercises. Theory learned will be applied using our state-of-the-art simulators 

 

This course is designed to provide participants with a detailed and up-to-date overview of Metallurgical Laboratory Failure Examination for Refinery. It covers the refinery material failures and the importance of failure analysis in safety and cost management; the principles of failure analysis; the step-by-step failure investigation process and  documentation and reporting practices; the metallurgical properties of refinery materials, microstructural influences on failure and the effects of temperature, pressure, and corrosive environments; the classification of failures, and failure analysis toolkit; the corrosion mechanisms in refineries. 

 

Further, the course will also discuss the high-temperature corrosion, hydrogen damage, wear and erosion mechanisms and stress corrosion cracking (SCC); the basics of fracture surface analysis and the overload, fatigue, and brittle fractures; the microstructural analysis, chemical analysis techniques, residual stress analysis, and nondestructive examination (NDE) for failure analysis; the root cause analysis methodologies covering fault tree analysis (FTA), ishikawa (fishbone) diagrams and five whys technique; and the mechanical failures in refinery equipment, welding and fabrication defects and failures in specific refinery units. 

During this interactive course, participants will learn the failure prevention strategies comprising of material selection and design considerations, process control and monitoring, inspection and maintenance best practices; the repair and remediation methods and developing failure analysis reports; the continuous improvement in asset integrity through learning from past failures, integrating failure analysis into asset management, and collaboration with design and operation teams and refinery best practices for integrity management; and the emerging trends in failure analysis consisting of AI and machine learning in failure prediction, advances in NDE technologies, industry 4.0 and digital twins and future challenges and opportunities.