Artificial neural network and Bees Algorithm for removal of Eosin B using Cobalt Oxide Nanoparticle-activated carbon: Isotherm and Kinetics study

The objective of this work is the study of adsorption of Eosin B by cobalt oxide nanoparticle loaded on activated carbon (Co2O3‐NP‐AC). This new material with high efficiency in a routine manner was synthesized in our laboratory, and its surface properties such as surface area, pore volume, and functional groups were characterized with different techniques such X‐ray diffraction, Brunauer, Emmett, and Teller, and scanning electron microscopy analysis. The effect of solution pH, adsorbent dosage (0.005–0.02 g), contact time (0.5–30 min), and initial concentration of dye (30–80 mg L−1) on the adsorption process was investigated. Thus, Langmuir, Freundlich, Tempkin, and D–R isothermal models are applied for fitting the experimental data, and the data well presented by Langmuir model with a maximum adsorption capacity of 588.2 mg g−1 at 25°C. Kinetic studies at various adsorbent dosage and initial Eosin B concentration show that maximum Eosin B removal was achieved within 18 min of the start of every experiment at most conditions. The combination of pseudo‐second‐order rate equation and intraparticle diffusion model (with removal more than 99%) is usable to explain the experimental data of adsorption process at all conditions. The influences of parameters including initial dye concentration, adsorbent dosage (g), and contact time on Eosin B adsorption onto cobalt oxide nanoparticles loaded on AC were investigated by multiple linear regression (MLR) and artificial neural network (ANN), and the influences of variables were optimized using Bees Algorithm. Comparison of the results obtained using introduced models showed the ANN model is better than the MLR model for prediction of Eosin B removal using cobalt oxide nanoparticles loaded on AC. Using the optimal ANN model, the coefficients of determination (R2) were 0.9965 and 0.9936; mean squared error values were 0.00015 and 0.00029 for training and testing data, respectively. © 2014 American Institute of Chemical Engineers Environ Prog, 34: 155–168, 2015