Abstract:
The design and optimization of unleaded gasoline blending processes play a crucial role in meeting the growing demand for high-quality fuels and ensuring compliance with environmental regulations. This work focuses on developing an optimization program for blending unleaded gasoline at the Skikda Refinery, to achieve maximum product quality and operational efficiency.
To develop the optimization program, a comprehensive dataset is collected and prepared, encompassing the properties of different gasoline components, including density, octane number, vapor pressure, and blending constraints. The artificial neural network model is then trained and validated using this dataset, ensuring accurate predictions and reliable optimization results.
The knowledge gained from this work contributes to the advancement of utilizing artificial intelligence techniques in refining operations, particularly in the field of gasoline blending.
