Speakers:
Brent Railey, Chevron Phillips Chemical Company LP
The two most controversial and potentially useful aspects of applied AI reside in Generative AI and Reinforcement Learning. Generative AI has applications in technical training, procedures and guided application development. Reinforcement Learning has applications in nonlinear closed loop optimization. Both areas will be of interest to refinery engineers and managers. This session is an extension of AI 101 which presented a broader view of the areas of application for AI.
Artificial Intelligence (AI) is a transformative technology that is presently influencing, or will eventually influence, nearly every facet of our personal and professional lives. Although much of the information highlights the benefits of AI, it also raises concerns about potential negative impacts and unintended consequences. It's crucial to prepare the workforce for AI to reduce disruptions. Leaders will share their experiences with AI deployments, the methods used to ensure success, and lessons learned for future AI initiatives.
Take Aways:
Awareness of potential impacts of AI on personnel and culture
Methods and techniques employed to achieve and sustain positive acceptance of AI
Insights into how AI is impacting jobs and job roles and defining new valued skill sets
Moderator:
Brent Railey, Chevron Phillips Chemical Company LP
Speakers:
Holly Fitch, Marathon Petroleum Corporation
Tyler Harnos, Big West Oil, LLC
Siva Lakshmanan, DeepHow
In this session, we will discuss in detail the definition of Normalization of Deviance and how it has contributed to major accidents, including both Space Shuttle disasters. Examples will be provided of Normalization of Deviance in operating and maintaining process plants, as well as in our daily lives.
Participants will:
Gain an understanding of how to stop Normalization of Deviance
Learn how to identify and correct this behavior
Take this concept home to apply with their families
After more than 50 years of root cause analysis (RCA), you might expect problem solving to be a clear and straightforward process. It’s not. You can find yourself caught in unproductive debates. “Is that a contributing factor, or a causal factor?” “That’s a primary cause, but it’s not the main cause.” The result is frustration, inefficiency, and solutions that allow a repeat of the same problems.
A better approach to RCA is one built on evidence-based cause-and-effect relationships. The steps are simple: define the problem, explain why it happened, and identify solutions to reduce risk. Its focus is on having more reliable work processes, learning, and improving, which helps engage the frontline and minimize blame.
This session will explain the pitfalls of drifting from scientific problem solving and features a case study demonstrating how first-principles RCA leads to tangible improvements in reliability and human performance.
Participants will learn:
Four common errors that arise when explaining why an incident occurred
The biggest misconception about RCA
Why effective RCA does not require any proprietary techniques, terminology, or software
How RCA can be scaled for low-risk incidents and expanded as needed for higher-impact events.
How to reduce “human error” by involving those who perform the work
Moderator:
Bill Clark, Phillips 66
Speakers:
Bill Clark, Phillips 66
Mark Galley, ThinkReliability
Speakers:
Chris Harrison, Marathon Petroleum Corporation
This session will discuss the project development and workflow, implementation, and application maintenance of an Imubit DLPC that was used to reduce giveaway on drum cycles on a delayed coker unit. The DLPC was successful in reducing the number of cycles in which the target level was not achieved in the fixed cycle time period. This resulted in an overall reduction in the number of barrels given away in each cycle. The application has achieved great acceptance by Operations.
Participants will:
Gain an understanding of the Imubit DLPC application to reduce giveway on Coker Drum cycles
Gain an understanding of the workflow for an Imubit DLPC project
Gain an understanding of unique challenges and lessons learned from the project