Modeling of reactive orange 12 (RO 12) adsorption onto gold nanoparticle-activated carbon using artificial neural network optimization based on an imperialist competitive algorithm

Add time:07/19/2019    Source:sciencedirect.com

This work devoted to the investigation of adsorption of reactive orange 12 (RO-12) by gold nanoparticles loaded with activated carbon (Au-NP-AC), which in high efficiency by routine manner was synthesized in our laboratory. Generally, in batch adsorption procedure, the effect of variables, including adsorbent mass, initial RO-12 concentration, and contact time on its removal percentage was optimized by the application of artificial neural networks and based on an imperialist competitive algorithm. This novel adsorbent by small amount (0.02 g) really is applicable to the removal of the high amount of dye (RO 12) in a short time (< 20 min). The optimum variables for adsorption of RO 12 onto gold nanoparticle-activated carbon were 0.02 g adsorbent mass, 10 mg L− 1 initial RO-12 concentration, 20 min contact time and pH 1. The kinetic of proposed adsorption processes efficiently followed, pseudo-second-order and intra-particle diffusion kinetic models. The equilibrium data of the removal process strongly follow the Langmuir monolayer adsorption with high adsorption capacity. The adsorption capacity of Au-NP-AC for the removal of RO-12 was found to be 714.3 mg g− 1. The comparison of the results obtained using the proposed models showed that the ANN model is better than the MLR model for the prediction of reactive orange 12 adsorption onto gold nanoparticles loaded on activated carbon. The coefficient of determination (R2) and mean squared error (MSE) for the optimal ANN model with 9 neurons at hidden layer were obtained to be 0.9720 and 0.0007, respectively.

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