Abstract
The efficiency and performance of lead oxide nanoparticles loaded activated carbon (PbO-NP-AC) which was fully characterized by different techniques including FTIR and SEM analysis were described. The influence of variables including pH, contact time, MO concentration and mass of adsorbent was investigated and optimized by artificial neural network-partial swarm optimization (ANN-PSO). At optimal conditions predicted by ANN-PSO, the coefficient of determination (R2) and mean square error (MSE) which corresponds to test data was 0.9685 and 0.00093, respectively. The maximum removal percentage (approximately 98%) was observed at conditions set at: 0.02g of PbO-NP-AC, 15mgL−1 of MO at pH2.0 following mixing and stirring for 30min. The experimental data were efficiently adopted by Langmuir model at all conditions with maximum adsorption capacity of 333.33mgg−1. Kinetic studies at various adsorbent mass and initial MO concentrations reveal that maximum MO removal was achieved within 15min, while experimental data follow the pseudo-second-order rate equation in addition to intraparticle diffusion model.
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