Abstract
The current research is concerned with the adsorption behavior of chromium (IV) ions in an aqueous solution using a novel ferrochrome slag/polyaniline nanocomposite (FeCr-PANI) adsorbent. The effect of process parameters such as temperature, pH solution, initial Cr (VI) ions concentration, adsorbent dosage, and contact time on the adsorption process is experimentally investigated in this study. Furthermore, we have trained a multilayer artificial neural network (ANN) using the experimental data of various process parameters to successfully predict the adsorption behavior of chromium (IV) ions onto the FeCr-PANI adsorbent. The ANN model was trained using the Lavenberg-Marquardt algorithm and ten neurons in the hidden layer and was able to estimate the % removal efficiency of chromium (IV) under the influence of different process parameters (
R
2
= 0.991). Initial solution pH was observed to have a significant influence on the % removal efficiency. The % removal efficiency was found to be high at 97.10% for the solution with pH 3 but decreased to 64.40% for the solution with pH 11. Cr (VI) % removal efficiency was observed to increase with an increase in solution temperature, adsorbent dosage, and contact time. However, the % removal efficiency was found to decrease from 96.9 to 54.8% with increasing the initial dye concentration from 100 to 400 ppm. Furthermore, the adsorption capacity increased from 9.69 to 21.93 mg/g with an increase in the initial concentration from 100 to 400 ppm, as expected. The Langmuir isotherm model exhibited the best fit with the experimental data (
R
2
= 0.9977). The maximum adsorption capacity was found to be 22.523 mg g
−1
at 298 K. The experimental data fitted well with the pseudo-second-order kinetic model.
Graphical abstract