Abnormal Situational Prevention: Improve Situational Awareness
Abnormal Situation Management
This panel session will explore how operating companies leverage industry good practices and advanced computing technology to swiftly process predictive analytics. It will also cover the use of high-performance HMI graphics and effective alarm management to enhance situational awareness.
Participants will:
· Learn how analytics (predictive, process conditions nearing an operating limit, reliability focused machine learning, corrosion prediction tied to IOWs, etc.) can assist with identifying pre-abnormal conditions and take appropriate actions to avoid an abnormal situation (prevention).
· Learn how high-performance HMI graphics can improve situational awareness to detect abnormal situations before alarms occur and perform required action(s) faster to avoid consequences.
· Understand the aspect of dealing with abnormal situations is alarm management. It is essential to maximize the time available for operators to respond while keeping the number of alarms triggered at a minimum. Preventing abnormal situations before the alarms activate keeps the console operators focused on the operation without having to manage an abnormal event.
See related presentations on the AFPM Safety Portal at https://safetyportal.afpm.org/
Moderator:
Tim Olsen, Emerson Automation Solutions
Speakers:
Carlos Acosta, Phillips 66
Vance Flosenzier, INVISTA
David Lee, User Centered Design Services, Inc.
Marsha Wisely, Athion
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The Benefits of Good Alarm Management
Abnormal Situation Management
Moderator:
Tim Olsen, Emerson Automation Solutions
Speakers:
Steve Gill, HF Sinclair Corporation
Troy Jones, Motiva Enterprises LLC
Cristina Pirataie, Puffer-Sweiven
Speakers
Session Start End
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Implementing a Refinery Diesel Pool Optimizer Using Neural Networks and Reinforcement Learning
Artificial Intelligence, Advanced Process Controls
Facilitator:
Atique Malik, AIControl LLC
Speaker:
Yangdong Pan, Delek US
Toni Adetayo, Imubit, Inc.
Multi-unit optimization has long been a complex issue in the oil and gas industry. Despite efforts using first-principle models or empirical approaches, challenges persist. However, the emergence of machine learning and AI technologies offers an alternative solution. In particular, AI-based process control technology has shown promise for multi-unit optimization. This session will delve into an example using a distillate system optimizer to understand how these AI models address large-scale optimization challenges and how parent and child models collaborate.
Participants will learn:
- The strength of the technology and its high flexibility of handling core issues
- How the technology deals with the availability of individual units, and cooperates with unit controllers from other advanced control technologies
- How to sustain the technology’s performance and benefits
- A site-wide AI adoption strategy
Speakers
Session Start End
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Implementing a Refinery Diesel Pool Optimizer Using Neural Networks and Reinforcement Learning
Artificial Intelligence, Advanced Process Controls
Facilitator:
Atique Malik, AIControl LLC
Speaker:
Yangdong Pan, Delek US
Toni Adetayo, Imubit, Inc.
Multi-unit optimization has long been a complex issue in the oil and gas industry. Despite efforts using first-principle models or empirical approaches, challenges persist. However, the emergence of machine learning and AI technologies offers an alternative solution. In particular, AI-based process control technology has shown promise for multi-unit optimization. This session will delve into an example using a distillate system optimizer to understand how these AI models address large-scale optimization challenges and how parent and child models collaborate.
Participants will learn:
- The strength of the technology and its high flexibility of handling core issues
- How the technology deals with the availability of individual units, and cooperates with unit controllers from other advanced control technologies
- How to sustain the technology’s performance and benefits
- A site-wide AI adoption strategy
Speakers
Session Start End
-