A Methodology for Designing Octane Number of Fuels Using Genetic Algorithms and Artificial Neural Networks
DOI: 10.1021/acs.energyfuels.1c04052 Contribution In this research project, I made significant progress in three key areas. Firstly, I developed precise artificial neural networks (ANNs) for predicting Research Octane Number (RON) and Motor Octane Number (MON), achieving an impressive R2 of 0.99 for both, along with low mean absolute error (MAE) values. Secondly, I harnessed the power of genetic algorithms, significantly enhancing the optimization process by systematically reducing high octane component usage. Lastly, my advisory role in developing the polygonal method further refined the optimization process. My multifaceted contributions have led to enhanced fuel blending accuracy and efficiency, providing practical benefits to refineries by reducing costs and minimizing quality issues. My role in the manuscript writing process ensured that these complex ideas were clearly communicated, highlighting the practical applications and implications of our research in a real-world context. ...