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A modeling approach was utilized to achieve efficient operational conditions for Alizarin removal from synthetic wastewater in a T‐type micromixer. Besides experimental work, the neuro‐fuzzy system and artificial neural network techniques were utilized for this purpose. Input parameters were the pH, the initial Alizarin concentration in the feed, the extractant volume percentage in the organic phase, and the fluid flow rate. Based on the obtained results, both models have high precision. However, the accuracy of the neural network for estimating the extraction percentage is higher compared to that of the neuro‐fuzzy model. The optimal values of the operating parameters were determined by the genetic algorithm technique and the extraction percentage value was obtained as about 99.4 %.

A semi‐active T‐type micromixer is designed to intensify micromixing by actuating magnetic nanoparticles (MNPs). Five permanent magnets in a zig‐zag arrangement are located next to the mixing channel of the micromixer to apply the magnetic field to the fluid flow. Micromixing performance is considered in terms of the segregation index (XS) by the Villermaux/Dushman reaction test. The effects of magnetic flux intensity (B = 380–500 mT), the concentration of MNPs (φ = 0.002–0.01 [w/v]), and flow rate ratios on XS and pressure drop are investigated. By increasing MNPs concentration from φ = 0.002–0.008 (w/v), XS decreased and the rise in φ up to 0.008 (w/v) has not been significant on XS. Maximum mixing efficiency (i.e., minimum XS = 0.0088) is achieved for B = 500 mT and φ = 0.01 (w/v). By applying the magnetic field, the mixing performance increased due to the motion of MNPs, but its negative effect is an increase in the pressure drop along the micromixer reactor. Generally, with the formation of MNPs barriers inside the mixing channel, the main fluid flows through these layers and creates the sinusoidal flow paths compared to no magnetic field conditions, and thus, a superior mixing efficiency could be attained.

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