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- W4387235710 abstract "Abstract Oil and Gas remain critical components of the world's energy supply mix and there is an increasing need for maximizing energy efficiency and minimizing the environmental impact associated with hydrocarbons (1). To address the trilemma of sustainability issues, including resources depletion, energy and oil diminution as well as ecosystem degradation, Abqaiq Plants, where over 5% of global oil production is processed, has developed a novel 3D interface with machine learning (ML) applications that drives a multi-objective optimization of crude oil stabilizing process to improve efficiency and GHG while maximizing economic and environmental sustainability (1). Machine Learning (ML) Model was developed and trained to understand optimal operating ranges of crude oil stabilization units and identify abnormalities and performance degradation. Bearing in mind the operation of world's largest array of stabilization columns, a tool was developed and embedded in a 3D platform to identify optimal feed distribution and energy usage in real-time. A second advanced data analytics tool was developed in-house for shell and tube (S&T) heat exchanger to predict leaks in real time to avoid product contamination and was embedded within the same platform. This 3D Visualization platform is used to display analysis outcome in a user-friendly display. Utilizing in-house expertise in designing tools to achieve objective in an accelerated manner as part of an agile process to support high performance and scalability." @default.
- W4387235710 created "2023-10-02" @default.
- W4387235710 creator A5068705179 @default.
- W4387235710 date "2023-10-02" @default.
- W4387235710 modified "2023-10-16" @default.
- W4387235710 title "Multi-Objective Optimization of Crude Oil Stabilizing Process Using Data-Driven Models" @default.
- W4387235710 cites W4221102566 @default.
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- W4387235710 doi "https://doi.org/10.2118/216011-ms" @default.
- W4387235710 hasPublicationYear "2023" @default.
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