RSM and ANN methodologies in modeling the enhanced biodiesel production using novel protic ionic liquid anchored on g-CN@FeO nanohybrid.

Herin, a new nanohybrid acid catalyst was fabricated for the efficient biodiesel production. At the first, magnetic porous nanosheets of graphitic carbon nitride (g-CN@FeO) was prepared and then functionalized with sulfonic acid. Next, the preparation of the catalyst was completed by mixing this surface modified support with n-methyl imidazolium butyl sulfonate zwitterion to achieve non-covalent immobilized acidic ionic liquid on g-CN@FeO support. The catalyst underwent characterization through various techniques such as H and C NMR, FTIR, SEM, TEM, TGA, EDX and BET which revealing that the magnetic support loaded acidic ionic liquids via a robust charge interaction effect enabling the one-pot production of biodiesel from low-quality oils. Furthermore, the catalyst could be simply recovered using a permanent magnet and reused multiple times without a significant decline in catalytic activity. Consequently, the solid catalyst based on ionic liquids holds promise for the sustainable and eco-friendly production of biodiesel from low-quality oils. Furthermore, Response Surface Methodology (RSM) and Artificial Neural Networks (ANN) were used to model the yield and various process parameters. The findings underscore the enhanced predictive capabilities of ANN in comparison to RSM.