Preparation and application of meso-adsorbent NiFe2O4 for the ultrasound-enhanced removal of dye pollutant in water and wastewater: A multivariate study

Document Type : Research Article


1 Department of Chemistry, Faculty of Sciences, University of Sistan and Baluchestan, Zahedan 98135-674, Iran

2 Department of Materials Engineering, Faculty of Engineering, University of Sistan and Baluchestan, Zahedan, Iran


In this study, we present a new combustion method for the preparation of meso-adsorbent NiFe2O4 powders. SEM, XRD, and BET were used for the characterization of adsorbents. BET measurements confirmed a specific surface area (SSA) of 87.7 m2.g-1, a total pore volume (PV) of 0.2377 cm3.g-1, and a mean pore size (PS) of 10.841 nm. The mean crystallite diameter of the adsorbent using the Scherrer equation was 10 nm. Also, the response surface methodology and artificial neural network models were used for modeling, optimization, and prediction of responses for removing methyl violet from water and wastewater in lab-scale batches. To study absorption, a four-factor central composite design was used to select the best experimental condition for ultrasonic-assisted adsorption of methyl violet dye. The adjusted R2 of 0.9931 and the predicted R2 of 0.9813 are very close, indicating the compatibility of the experimental results with the quadratic model. According to the results, optimum conditions were set at an ultrasonic time of 231 s, 13.5 mg of adsorbent, a dye concentration of 2.0 mg.L-1, and a pH = 7.9. Also, the learning rule of Levenberg–Marquardt was used for ANN Modeling. According to the proposed ANN, the value of the root mean square error (RMSE) was 2.562, and the value of the correlation coefficient (R2) was 0.986. Also, removal efficiencies of 96.8% and 95.57% were obtained for the tap water and wastewater, respectively.

Graphical Abstract

Preparation and application of meso-adsorbent NiFe2O4 for the ultrasound-enhanced removal of dye pollutant in water and wastewater: A multivariate study


  • Herein, we present a new combustion method for the preparation of meso-adsorbent NiFe2O4 powders.
  • SEM, XRD, and BET analysis were used to characterize absorbency.
  • To increase the adsorption efficiency, surface methodology (RSM) and artificial neural network (ANN) methods were used to optimize, model and predict the responses.


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