COURSE OVERVIEW

This practical and highly-interactive course includes real-life case studies and exercises where participants will be engaged in a series of interactive small groups and class workshops. 

 

This course is designed to provide participants with a detailed and up-to-date overview of Artificial Intelligence in Crude Oil Processing. It covers the fundamentals of Al in oil refining and processing and the Al-powered data analytics for refining operations; the Al for feedstock characterization and quality prediction, distillation and fractionation optimization, process control and real-time monitoring; the Al for predictive maintenance of refinery equipment, heat exchanger and boiler efficiency optimization; and the Al in catalytic cracking and hydroprocessing optimization, pump and compressor health monitoring and pipeline and storage tank monitoring. 

 

Further, the course will also discuss the Al for crude oil blending optimization, hydrogen and sulfur recovery units (SRU) and desalting and water management in refining; the Al for refinery energy efficiency and emissions reduction and the aromatics, gasoline, and diesel production; the hazard identification in refineries and machine learning for accident prediction and prevention; the Al-powered real-time gas detection and leak prevention, Al-assisted emergency response and safety drills; and the Al-driven predictive analytics for greenhouse gas emission and machine learning for refinery air quality monitoring. 

 

During this interactive course, participants will learn the Al-powered real-time monitoring of volatile organic compounds (VOCs) and Al-assisted compliance with environmental regulations; the digital twin and Al in in advanced control systems and process automation, smart refinery management and Al-driven decision making; the  future Al trends in refining and crude oil processing; and the Al for Al-driven predictive analytics in oil refining, reducing operational costs and increasing efficiency.