Gazi, Mustafa Oladipo, Akeem Adeyemi Ojoro, Zainab Eniola Gulcan, Hayrettin Ozan High-Performance Nanocatalyst for Adsorptive and Photo-Assisted Fenton-Like Degradation of Phenol: Modeling Using Artificial Neural Networks <p>High-performance activated carbon-zinc oxide (Ac–ZnO) nanocatalyst was fabricated via the microwave-assisted technique. Ac–ZnO was characterized and the results indicated that Ac–ZnO is stable, had a band gap of 3.26 eV and a surface area of 603.5 m<sup>2</sup>g<sup>−1</sup>, and exhibited excellent adsorptive and degrading potentials. About 93% phenol was adsorbed within 550 min of reaction by Ac–ZnO. Impressively, a complete degradation was achieved in 90 min via a photo-Fenton/Ac–ZnO system under optimum conditions. An artificial neural network (ANN) model was developed and applied to study the relative significance of input variables affecting the degradation of phenol in a photo-Fenton process. The ANN results indicate that increases in both H<sub>2</sub>O<sub>2</sub> and Ac–ZnO dosage enhanced the rate of phenol degradation. The highest rate constant at the optimum conditions was 0.093 min<sup>−1</sup> and it was found to be consistent with the ANN-predicted rate constant (0.095 min<sup>−1</sup>).</p> Activated carbon;Artificial neural network;Fenton-like degradation;Phenol;ZnO nanoparticles 2017-05-16
    https://tandf.figshare.com/articles/journal_contribution/High-Performance_Nanocatalyst_for_Adsorptive_and_Photoassisted_Fenton-Like_Degradation_of_Phenol_Modeling_Using_Artificial_Neural_Networks/4802134
10.6084/m9.figshare.4802134.v2