Dates: 2022
Principal Investigator: Dr. Abdul Gani Abdul Jameel
Funded by: SDAIA-KFUPM Joint Research Center for Artificial Intelligence

Description: This work proposes an AI-driven system for fuel blending optimization in refineries, aiming to reduce costs, avoid off-spec products, and meet quality/emission standards. The approach integrates genetic algorithms (GA) and artificial neural networks (ANN), supported by a polygonal method to design optimal blending recipes using minimal high-value components. The system will be trained with hydrocarbons, fuel surrogates, and reference fuels, and can be customized for different refinery constraints. Additionally, it will support the design of low-soot fuels aligned with climate goals by optimizing blends for reduced particulate emissions.