SUM-24-26 Implementing a Refinery Diesel Pool Optimizer Using Neural Networks and Reinforcement Learning

Description:

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