Comparative study of malachite green adsorption onto activated carbon prepared from Ziziphus wood and ZnO nanoparticles loaded AC: artificial neural network modeling and optimization

In this study activated carbon prepared from Ziziphus wood was modified by ZnO nanoparticles loaded on activated carbon (ZnO-NPs-AC) and checking its applicability for malachite green (MG) removal. The properties of ZnO-NPs-AC were identified by X-ray diffraction, transmission electron microscopy, field emission scanning electron microscopy, and Brunauer–Emmett–Teller. Response surface methodology and artificial neural network (ANN) model learned with Levenberg–Marquardt algorithm are used for modeling of MG adsorption onto present support. The mean squared error and coefficient of determination (R 2 ) for the optimal ANN model with eight neurons at hidden layer were 0.9999 and 0.00116 by AC and 0.9970 and 0.01789 by ZnO-NPs-AC for testing data, respectively. The optimum conditions were set as 0.02 and 0.015 g for ZnO-NPs-AC and AC are applicable for successful removal of MG (97.38% and 71.35%) in reasonable time (5 min for ZnO-NP-AC and 4.65 min for AC). The adsorption isotherm is efficiently explained by Temkin and Langmuir for AC and ZnO-NPs-AC adsorbent. The adsorption kinetics of MG excellently follows the pseudo- second-order kinetic model.