Application of artificial neural network and response surface methodology for the removal of crystal violet by zinc oxide nanorods loaded on activate carbon: kinetics and equilibrium study

Abstract

The present work discusses the removal of crystal violet dye from aqueous solution by ultrasound assisted adsorption using zinc oxide nanorods loaded on activate carbon as an adsorbent. The said adsorbent was prepared and characterized using field emission scanning electron microscopy, X-ray diffraction and Fourier transform infrared analysis. The important process parameters, such as initial crystal violet concentration (8–24 mg/L), solution pH (3.0–7.0), adsorbent doses (0.005–0.025 g), and sonication time (2–6 min) were optimized using design of experiments. The optimum removal efficiency of crystal violet onto adsorbent was determined as 99.82% at pH 7.0, 0.025 g adsorbent dosage, 24 mg/L initial crystal violet concentration and 5.0 min sonication time. Analysis of variance showed a high coefficient of determination (R2 = 0.992). The present analysis suggests that the predicted values are in good agreement with experimental data. Also, the artificial neural network model was used for predicting removal (%) of crystal violet dye based on experimental data. Equilibrium data was fitted well with the Langmuir model having maximum adsorption capacity of 113.64 mg/g. The adsorption of crystal violet followed the pseudo-second order kinetic model. This study clearly showed that response surface methodology was one of the appropriate methods to optimize the operating conditions.