To overcome this limitation, the use of machine learning models, such as neural networks, has gained popularity due to their capability to model complex problems without the need for explicit physical representations . This data-driven method could serve as a viable alternative in cases where you lack access to certain properties of the fuel that are typically employed in conventional thermodynamic models, such as the activity coefficient parameters.