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
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
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
The oil refining process generates a significant volume of wastewater that contains a suite of challenging contaminants, from hydrocarbons to heavy metals and organic compounds. Oil production at a refinery relies on efficient and reliable wastewater treatment as it is impossible for the facility to operate without treating its effluent. Issues with the wastewater treatment systems can result in production limitations, as well as significant environmental impacts and violations. These systems can be complex, and they are sensitive to upset conditions due to poor feed quality, unexpected contaminants, or equipment breakdown.
This presentation will discuss a significant upset condition that impacted both the primary and secondary wastewater treatment system at a large West Coast refinery. We will present the processes that allowed for detection of the upset, and the steps taken by the operations group, technical team, and vendor support that helped maintain target production rates and avoid any environmental violations or impacts. The mitigation steps allowed for storage and post-upset treatment of the problematic effluent streams. The technical team evaluated options for treatment and environmental compliance, and the water treatment vendor supplied specialty biological treatment technology to get the system back online as quickly as possible. All of this contributed to a successful upset response and the implementation of best practices that can help all refineries facing a wastewater oil contamination.
Moderator:
Dan Harbs, Veolia
Speakers:
Angela Wharton, Chevron U.S.A. Inc.
Kai Zhang, Chevron U.S.A. Inc.