Modeling of phenol removal from water by NiFe2O4 nanocomposite using response surface methodology and artificial neural network techniques

This study demonstrates the synthesis, characterization, and modeling of nickel ferrite nanocomposite, NiFe2O4 (NFC) as an adsorbent for the phenol contaminated aqueous environment. The characterization of the prepared NFC was performed with X-ray diffraction spectroscopy (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM) and vibrating sample magnetometer (VSM) techniques. The optimization and modeling of phenol removal using NFC was done through central composite design (CCD) and effective parameters of CCD were measured as input variables including the amount of NFC, pH, contact time and initial phenol concentration. The predicted results showed that the adsorption process using NFC as adsorbent had the maximum phenol removal (~99%) under predicted optimal conditions (pH = 7.67, NFC dosage = 0.15 g at room temperature), which also corresponded to the experimental values. In addition, a multilayer feed-forward artificial neural network (ANN) model was used to obtain a speculative phenol removal model. The network was trained for six replications after selection of the best neuron number for hidden layer. The value of MSE trained network was found to be 6.01718e-3 along with regression coefficient (R2 = 0.9934) that indicated satisfactory relationship. Isothermal modeling of phenol adsorption onto NFC was performed using well-known Temkin, Freundlich and Langmuir models and it was clear from the higher R2 value of 0.961 that the Langmuir model was significantly followed by experimental data. The maximum Langmuir adsorption capacity was found to be 274.72 mg/g at the optimal conditions. The obtained results prove that NFC could be an effective adsorbent for elimination of phenol contaminant from aqueous environment.