This study explores a novel approach to the geometric optimization of coiled flow inverters (CFIs) aimed at enhancing biodiesel production efficiency. By simulating nine distinct CFI geometries using advanced computational fluid dynamics (CFD) and genetic algorithms (GA), this research introduces innovative methods for optimizing fluid flow characteristics. The CFD model yielded essential hydrodynamic data and friction factors, while oil conversion percentages for biodiesel were derived from existing literature. The integration of CFD results with experimental data significantly informed the GA optimization process, marking a key advancement in the field. Two new correlations were developed to predict friction factors and oil conversion percentages based on the coil length-to-diameter ratio, Reynolds number, and the number of 90° bends. This study uniquely identifies optimal geometries through a GA-based multi-objective approach, effectively balancing oil conversion and friction factor. Additionally, it delves into the trade-offs between improving oil conversion and the resultant increase in pressure drop, highlighting the intricate complexities of fluid flow in CFIs and their implications for biodiesel production efficiency.
