A combined radical and non-radical oxidation processes for efficient degradation of Acid Orange 7 in the homogeneous Cu(II)/PMS system: important role of chloride

Environmental Science and Pollution Research - Trace copper ion (Cu(II)) in water and wastewater can trigger peroxymonosulfate (PMS) activation to oxidize organic compounds, but it only works under...     

On the performance of Fe and Fe,F doped Ti-Pt/PbO2 electrodes in the electrooxidation of the Blue Reactive 19 dye in simulated textile wastewater

The electrochemical performance of pure Ti–Pt/β-PbO₂ electrodes, or doped with Fe and F (together or separately), in the oxidation of simulated wastewaters containing the Blue Reactive 19 dye (BR-19), using a filter-press reactor, was investigated and then compared with that of a boron-doped diamond electrode supported on a niobium substrate (Nb/BDD). The electrooxidation of the dye simulated wastewater (volume of 0.1 l, with a BR-19 initial concentration of 25 mg l⁻¹) was carried out under the following conditions: current density of 50 mA cm⁻², volume flow rate of 2.4 l h⁻¹, temperature of 25 °C and electrode area of 5 cm². The performances of the electrodes in the dye decolorization were quite similar, achieving 100% decolorization, and in some cases 90% decolorization was achieved by applying only ca. 0.3 A h l⁻¹ (8 min of electrolysis). The reduction of the simulated wastewater organic load, monitored by its total organic carbon content (TOC), was greater for the Ti–Pt/β-PbO₂–Fe,F electrode obtained from an electrodeposition bath containing 1 mM Fe³⁺ and 30 mM F⁻. In this case, after 2 h of electrolysis the obtained TOC reduction was 95%, while for the pure β-PbO₂ and the Nb/BDD electrodes the reductions were 84% and 82%, respectively.     

The use of artificial neural network (ANN) for the prediction and simulation of oil degradation in wastewater by AOP

The application of advanced oxidation process (AOP) in the treatment of wastewater contaminated with oil was investigated in this study. The AOP investigated is the homogeneous photo-Fenton (UV/H₂O₂/Fe⁺²) process. The reaction is influenced by the input concentration of hydrogen peroxide H₂O₂, amount of the iron catalyst Fe⁺², pH, temperature, irradiation time, and concentration of oil in the wastewater. The removal efficiency for the used system at the optimal operational parameters (H₂O₂?=?400 mg/L, Fe⁺²?=?40 mg/L, pH?=?3, irradiation time?=?150 min, and temperature?=?30 °C) for 1,000 mg/L oil load was found to be 72 %. The study examined the implementation of artificial neural network (ANN) for the prediction and simulation of oil degradation in aqueous solution by photo-Fenton process. The multilayered feed-forward networks were trained by using a backpropagation algorithm; a three-layer network with 22 neurons in the hidden layer gave optimal results. The results show that the ANN model can predict the experimental results with high correlation coefficient (R ²?=?0.9949). The sensitivity analysis showed that all studied variables (H₂O₂, Fe⁺², pH, irradiation time, temperature, and oil concentration) have strong effect on the oil degradation. The pH was found to be the most influential parameter with relative importance of 20.6 %.