Discoloration of azo dye Brown HT using different advanced oxidation processes.

In this study, known combinations of Advanced Oxidation Processes (AOPs, namely Electro-Fenton (EF), Photo-Electro-Fenton (PEF), Electro-Oxidation (EO), and EO/Ozone (O3) were compared for the discoloration of tannery industry azo dye Brown HT (BHT). The different AOPs were tested in a 0.160 L batch electrochemical stirred thank reactor using Boron Doped Diamond (BDD) electrodes. The influence of parameters such as the current density (j) and the initial BHT concentration were to exanimated on the efficiency of all the tested processes. The oxidation tendency of EF, and PEF were compared with those of EO and O3, based on their efficiency for BHT discoloration, which resulted as PEF > EF > EO > O3. The AOPs showing the best oxidation performance was PEF which, using Na2SO4 (0.05 M) electrolyte solution and Fe2+ (0.5 mM), pH 3.0, j = 71 mA cm-2, and 500 rpm process, achieved 100% discoloration and 80% chemical oxygen demand (COD) abatement after 60 min of treatment for two initial BHT concentrations (50 and 80 mg L-1). The process accounted for a current efficiency of 30% and energy consumption 2.25 kWh (g COD)-1 through the discoloration test. The azo dye gradually degraded, yielding non-toxic oxalic, oxamic, and glyoxylic acid, whose Fe(III) complexes were quickly photolyzed.

Artificial intelligence based model for optimization of COD removal efficiency of an up-flow anaerobic sludge blanket reactor in the saline wastewater treatment.

The complex non-linear behavior presented in the biological treatment of wastewater requires an accurate model to predict the system performance. This study evaluates the effectiveness of an artificial intelligence (AI) model, based on the combination of artificial neural networks (ANNs) and genetic algorithms (GAs), to find the optimum performance of an up-flow anaerobic sludge blanket reactor (UASB) for saline wastewater treatment. Chemical oxygen demand (COD) removal was predicted using conductivity, organic loading rate (OLR) and temperature as input variables. The ANN model was built from experimental data and performance was assessed through the maximum mean absolute percentage error (= 9.226%) computed from the measured and model predicted values of the COD. Accordingly, the ANN model was used as a fitness function in a GA to find the best operational condition. In the worst case scenario (low energy requirements, high OLR usage and high salinity) this model guaranteed COD removal efficiency values above 70%. This result is consistent and was validated experimentally, confirming that this ANN-GA model can be used as a tool to achieve the best performance of a UASB reactor with the minimum requirement of energy for saline wastewater treatment.