Automating Root Cause Analysis of Refinery Incidents via Generative Deep Learning and Data Analytics

Abstract

Refinery disruptions are known for their significant economic and environmental repercussions. Identifying the underlying reasons behind these incidents quickly and with precision poses a significant but often complex and time- intensive challenge. This is primarily due to the need to compile various pieces of evidence. The document delves into how deep learning and data analytics can be harnessed to automate the analysis of the root causes behind refinery disruptions. I apply data analytics to detect critical trends and patterns within the incident data, providing additional context for the model. By integrating these advanced artificial intelligence techniques, my comprehensive approach seeks to enhance the analysis performed by human experts, dramatically slash the time required for investigating incidents, and promote more secure and dependable refinery operations. This data-centric strategy further supports the ongoing refinement of the model as new data are gleaned over time. Through this blend of cutting-edge techniques, I am pioneering a path towards minimizing the impact of refinery disruptions by enabling faster, more accurate root cause analysis.