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
Panelists:
William Bandy, Marathon Petroleum Corporation
Delbert Grotewold, Veolia Water Technologies & Solutions
Nathan Jannasch, Chevron U.S.A, Inc.
Matthew Kawabe, Topsoe, Inc.
Kamyar Keyvanloo, Phillips 66
Many new facilities were commissioned recently to produce renewable diesel and SAF from renewable feedstocks to reduce the carbon intensity of transportation fuels while taking advantage of state and federal credits. Operating experience in this technology has been gathered by operators and their suppliers. This session will capture some of that expertise and share it with the audience in a traditional Q&A format. An experienced panel of Renewables experts from Operating and Technology companies will answer member supplied questions on operations, technology, reliability and lessons learned. Those in attendance are sure to gain valuable knowledge on this important topic.
Refineries have increased focus on processing unfamiliar opportunity crude oils to improve profitability. However, the introduction of these new crude slates brings uncertainties that can impact refinery operations. One major challenge is crude incompatibility due to asphaltene instability, which affects desalters, emulsion stability, preheat trains and furnace fouling.
For decades, the industry has recognized the importance of tracking asphaltene stability. Suppliers and refineries employ diverse equipment to assess feeds stability including benchtop microscopy and titration equipment. Unfortunately, those methods can require time-consuming offsite laboratory testing.
Now, utilizing a portable, handheld near-infrared (NIR) analyzer, crude oil tanks and other refinery feeds can be assessed for stability and blending suitability with near instantaneous results. NIR spectra from crude oil samples are collected and compared against a database of crudes and crude unit feeds with known asphaltene stability parameters. Asphaltene stability parameters are modeled and reported with a stability assessment.
In the presentation, we will delve into the fundamentals of these available tools, showcasing real-world case studies where they have been successfully applied for crude blends and crude pretreatment to improve and optimize desalter operations and crude preheat train fouling.
Facilitator:
Oscar Brown, Valero Energy Corporation
1. Case Study: Boosting Hydrocracking Performance with Celestia™ Catalyst to Double Run Length and Enable Cold-Flow Improvement with MIDW
Presenters:
Christy Anderson, Ketjen Corporation
Mitchel Loescher , ExxonMobil Corporation
2. High throughput testing enables tailored, optimized, and confident catalyst selection for CITGO's Unicracker
Presenters:
Carmen Angelini, CITGO Petroleum Corporation
Ioan-Teodor Trotus, hte GmbH
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