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
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.