Hyper-branched dendrimer-modified magnetic nanoparticles for removal of an anionic dye from water samples: Response surface methodology and artificial neural network modeling

The presence of toxic dyes in industrial wastewater represents a persistent environmental challenge, necessitating the development of efficient and recoverable adsorbents. This study presents a novel, magnetically recoverable nanoadsorbent for the removal of reactive red 33 (RR33) from wastewater. By grafting a hyperbranched dendrimer (trimesic acid/ethylenediamine) onto silanized magnetic nanoparticles, the resulting architecture provides abundant active sites, ensuring both exceptional dye adsorption capacity and rapid magnetic separation. The synthesized material was characterized using VSM, TGA, BET-BJH, FTIR, XRD, FE-SEM, and TEM techniques. Response surface methodology utilizing a central composite design, along with an artificial neural network incorporating feed-forward back-propagation learning, was employed to assess the effects of the input parameters—namely pH, contact time, and adsorbent dosage on the efficiency of dye removal. Optimal removal efficiency, reaching 91%, was attained under the conditions of pH 3, a 60 min contact time, and an adsorbent dosage of 0.015 g. The Levenberg-Marquardt algorithm produced better results than the scaled conjugate gradient method, achieving a lower mean square error and improved predictive performance. Modeling studies shown that the RR33 adsorption is a favorable, exothermic process that conforms to pseudo-second-order kinetics and the Langmuir isotherm, indicating monolayer coverage on the adsorbent surface. The nanoadsorbent maintained stable performance over at least six adsorption–desorption cycles. Furthermore, its applicability was validated in spiked industrial wastewater (10–40 mg L –1 ), achieving RR33 removal efficiencies of 49–85% under optimized conditions. The nanoadsorbent shows strong potential for efficient anionic dyes removal in water treatment.