Removal of reactive orange 16 with nZVI-activated carbon/Ni: optimization by Box-Behnken design and performance prediction using artificial neural networks

Abstract

Purpose

The purpose of this paper is to analyze nano zero-valent iron (nZVI)-activated carbon/Nickel (nZVI-AC/Ni) by a novel method. The synthesized adsorbent was used to degrade reactive orange 16 (RO 16) azo dye.

Design/methodology/approach

The optimum conditions for the highest removal of RO 16 dye were determined. Characterization of nZVI-AC/Ni was done by scanning electron microscopy, Fourier-transform infrared spectroscopy, X-ray diffraction and energy-dispersive X-ray spectroscopy. The nZVI-AC/Ni were used for the removal of dye RO 16 and the parameters affecting were discussed such as pH, adsorbent dosage, contact time and concentration of dye. To investigate the variables and interaction between them, an analysis of variance test was performed.

Findings

The characterization results show that the synthesis of nZVI-AC/Ni caused no aggregation of nanoparticles. The maximum dye removal efficiency of 99.45% occurred at pH 4, the adsorbent dosage = 0.1 gL-1 and the dye concentration of 10 mgL-1. Among various algorithms of feed-forward backpropagation neural network, Levenberg–Marquardt with mean square error (MSE) = 9.86 × 10^–22 in layer = 5 and the number of neurons = 9 was selected as the best algorithm. On the other hand, the MSE of the radial basis function model was 0.2159 indicating the good ability of the model to predict the percentage of dye removal.

Originality/value

There are two main innovations. One is that the novel nZVI-AC/Ni was prepared successfully. The other is that the optimized conditions were obtained for the removal of RO 16 dye from an aqueous solution. Furthermore, to the best of the knowledge, no study has ever investigated the removal of RO 16 by nZVI-AC/Ni produced.

Citation

Seyedi, M.S., Sohrabi, M.R., Motiee, F. and Mortazavinik, S. (2022), "Removal of reactive orange 16 with nZVI-activated carbon/Ni: optimization by Box-Behnken design and performance prediction using artificial neural networks", Pigment & Resin Technology, Vol. 51 No. 5, pp. 463-476. https://doi.org/10.1108/PRT-03-2021-0025