Efficient micromixing is critical in chemical microreactors, yet predictive modeling remains challenging due to complex flow dynamics. This study investigates micromixing performance in micro-coiled flow inverter tubes (MCFIs) using artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFIS), and genetic algorithms (GA). Experimental data were collected for six MCFI geometries using the Villermaux–Dushman reaction, with the segregation index (Xs) as the primary measure of mixing efficiency. Model inputs included the number of 90° bends, flow rate ratio of alkaline to acid solutions (r = Qalkaline/Qacid), and acid concentration, while outputs were Xs and pressure drop (ΔP). ANN achieved the highest prediction accuracy (Xs: 4.217% MRE; ΔP: 1.30% MRE), outperforming ANFIS (Xs: 6.481%; ΔP: 1.91%) and GA (Xs: 12.43%; ΔP: 4.28%). GA-driven multi-objective optimization revealed optimal MCFI designs that balance Xs and ΔP, offering practical guidance for reactor design. The results highlight the potential of data-driven models for the accurate prediction and optimization of micromixing in complex microreactor geometries. Unlike previous studies that focused on single-objective or simulation-based optimization, this work integrates experimentally validated data with AI-driven surrogate modeling and multi-objective optimization to quantitatively explore the trade-off between micromixing efficiency and energy consumption in micro-coiled flow inverters.
