Optimization and modeling of Tramadol hydrochloride degradation by the homogenous photo-Fenton-like system assisted by in situ H2O2 formation.

This study examines the removal efficiency of Tramadol hydrochloride (TR) and mineralization (chemical oxygen demand, COD) by the effective photoinduced Fenton-simulated system under artificial light (UVA). The Box-Behnken design was used to optimize the value of each parameter. The model yielded the following optimal parameters: [TR]0 = 10 mg, ratio ([Oxalate ]0/[Fe3+]0) = 100, initial pH = 2.83, and [Fe3 +]0 = 1.298 mg with effective TR removal (100%) and COD removal efficiency (72.82%). The presence of oxygen has a positive effect by increasing hydrogen peroxide production from 4.36 to 8.12 mg L-1 and by maximizing a change in Fe3+ speciation. The degradation kinetics of ΤR in the oxygen-saturated medium is four times faster than that in the normal aerated medium. The Kapp rate constants increased quickly from 5.72 × 10-2 to 20 × 10-2min-1. The percent COD removal increased to 87.46%, and the final pH increased from 5.31 to 6.23.

Application of neural network approach for modelling COD reduction from real refinery effluent by electrocoagulation.

The present study aims to investigate the feasibility of implementing the electrocoagulation (EC) process to treat Algiers refinery effluent. The electrocoagulation was performed by using scrap aluminum plate electrodes in monopolar-parallel mode. Several parameters, namely current density, reaction time, the electrolyte dose, and the initial chemical oxygen demand (COD) concentration were studied. The maximum removal of COD achieved was found to be 78.55%. Operating conditions at which maximum COD removal efficiencies were achieved at current density 8 mA/cm2, electrolyte dose 1 g/L, with 360 mg/L of initial COD concentration at working time of 40 min. An artificial neural network (ANN) was also utilized to determine predicted responses using neural networks for the 4-10-1 arrangement. The responses predicted by ANN were in alignment with the experimental results. The values of the determination coefficient (R2 = 0.978) and the root mean square error (RMSE = 21.28) showed good prediction results between the model and experimental data. Hence, the ANN model as a predictive tool has a great capacity to estimate the effect of operational parameters on the electrocoagulation process.

Degradation of Nystatin in aqueous medium by coupling UV-C irradiation, H2O2 photolysis, and photo-Fenton processes.

Oxidative degradation and mineralization of the antifungal drug Nystatin (NYS) was investigated using photochemical advanced oxidation processes UV-C irradiation (280-100 nm), H2O2 photolysis (UV/H2O2), and photo-Fenton (UV/H2O2/Fe3+). The effect of operating parameters such as [H2O2], [Fe3+], and [NYS] initial concentrations on degradation efficiency and mineralization ability of different processes was comparatively examined in order to optimize the processes. Photo-Fenton was found to be the most efficient process attaining complete degradation of 0.02 mM (19.2 mg L-1) NYS at 2 min and a quasi-complete mineralization (97%) of its solution at 5 h treatment while UV/H2O2 and UV-C systems require significantly more time for complete degradation and lower mineralization degrees. The degradation and mineralization kinetics were affected by H2O2 and Fe3+ initial concentration, the optimum dosages being 4 mM and 0.4 mM, respectively. Consumption of H2O2 during photo-Fenton treatment is very fast during the first 30 min leading to the appearance of two stages in the mineralization. The evolution of toxicity of treated solutions was assessed and confirmed the effectiveness of photo-Fenton process for the detoxification of NYS solution at the end of treatment. Application to real wastewater from pharmaceutical industry containing the target molecule NYS showed the effectiveness of photo-Fenton process since it achieved 92% TOC removal rate at 6-h treatment time.