Efficient Cr(VI) Ion Removal Using Cerium Oxide‐Modified Zeolite and Artificial Neural Network‐Based Experimental Validation

Abstract Hexavalent chromium [Cr(VI)] in wastewater causes significant environmental pollution. This work aimed to synthesize cerium oxide‐modified zeolite (CeO 2 /zeolite) nanoadsorbents for effective wastewater remediation. The morphology, structure, and chemical states of CeO 2 /zeolite were characterized using FT‐IR, XRD, SEM, EDAX, HR‐TEM, and BET analyses. The nanoadsorbents were employed to remove Cr(VI) from aqueous solutions by batch adsorption; adsorption variables, including pH, Cr(VI) concentration, contact time, stirring speed, nanoadsorbent dosage, and temperature, were optimized using an artificial neural network model. Under optimal conditions (pH 2, nanoadsorbent dosage 0.2 g L −1 , contact time 60 min, initial Cr(VI) concentration 100 mg L −1 , stirring speed 100 rpm, temperature 303 K), a remarkable 99.9% removal efficiency was achieved for Cr(VI). The adsorption data, closely correlated with the Langmuir isotherm model ( R 2 = 0.9987), indicated a maximum adsorption capacity of 99.45 mg g −1 . The adsorption kinetics revealed an intraparticle diffusion process and were consistent with a pseudo‐second‐order model. Thermodynamic analysis showed that the adsorption process was endothermic and spontaneous. We trained a multilayer feed‐forward neural network with 6 input and 1 output parameters, along with 12 hidden neurons; its efficacy in predicting the Cr(VI) adsorption behavior on CeO 2 /zeolite was confirmed by comparing the predicted adsorption efficiency with the experimental results. Therefore, the CeO 2 /zeolite composite shows significant promise for the effective Cr(VI) removal from water.