The use of antibiotics and growth hormones in human and veterinary medicine has a significant effect on the quality of surface and groundwater. The relevance of an electrocoagulation (EC) process for the removal of an antibiotic selected because of its wide application, high solubility in water, high residual toxicity and an absence of biodegradability, was examined in this study.Metal hydroxides generated during EC were used to remove tetracycline (TC) from aqueous solution. The knowledge regarding the removal mechanism of this substance has not been investigated up to now. Experiments were carried out in a batch electrochemical reactor using aluminum electrodes. The removal of TC was relatively fast and equilibrium was reached within 15 min. The effects of the main operating parameters were examined and showed that irrespective of the initial concentration and for pH ranging from 3 to 10, maximum removal efficiency remained close to 99%; while a sharp decrease was recorded at pH 2 (10 % removal).The results of this study also showed that the removal of TC from water was strongly affected by the current intensity. The mechanism of electrocoagulation was modeled using isotherm models and showed that the Sips isotherm matched satisfactorily experimental data, suggesting monolayer coverage of adsorbed molecules and assumed a quasi-Gaussian distribution energy owing to the high correlation also found for the Toth model. In addition, adsorption kinetic studies showed that the EC process followed a pseudo-second-order kinetic model at the various current densities, pH and initial antibiotic concentrations considered.
In this study, chromate adsorption onto red peanut skin (RPS) was investigated in a fixed-bed column; FTIR, PZC, SEM, DLS, and BET were used to evaluate its adsorption properties. The experiments were conducted to determine the effect of physical parameters, including the inlet initial Cr(VI) concentration (100–500 mg L−1), bed height (10–20 cm), and feed flow rate (13.59–23.45 mL min−1). They were carried out to predict breakthrough curves. The adsorption capacity coefficients were determined using the most widely used Bohart–Adams model. It was tested to fit experimental data, for a better understand the dynamic behavior, and for further optimize column performance. It was found that the Cr(VI) uptake decreases when increasing the flow rate and that high chromate concentration and bed height consequently increase the column’s life span. A high column adsorption capacity can be achieved with a higher Cr(VI) concentration due to the higher driving force. The results indicated that the Bohart–Adams model provides a good description (R2 > 0.98) of the experimental data of the Cr(VI)’s removal from the aqueous solution on the RPS suggesting that the surface diffusion is the rate-limiting step in the continues adsorption process.. Breakthrough adsorption capacity is crucial for comparing RPS with other similar materials. Indeed, possible mechanisms have been suggested for illustrating adsorption onto RPS. The obtained results showed significant potential of 26.23 mg g−1 of RPS on Cr(VI) elimination at a natural pH of 5.35. Furthermore, this global investigation allowed for the design of a promising low-cost material for the future scale-up of cleaning wastewater polluted by metal and determine the properly conditions for operating column adsorption. This material provides an economical, efficient means of eliminating pollutants, thus meeting the main aims of the UN Sustainable Development Goals (UN SDGs).
In this study, the photocatalytic degradation of oxytetracycline (OTC) in aqueous solutions has been studied under different conditions such as initial pollutant concentrations, amount of catalyst, and pH of the solution. Experimental results showed that photocatalysis was clearly the predominant process in the pollutant degradation, since OTC adsorption on the catalyst and photolysis are negligible. The optimal TiO 2 concentration for OTC degradation was found to be 1.0 g/L. The apparent rate constant decreased, and the initial degradation rate increased with increasing initial OTC concentration with the other parameters kept unchanged. Subsequently, data obtained from photocatalytic degradation were used for training the artificial neural networks (ANN). The Levenberg-Marquardt algorithm, log sigmoid function in the hidden layer, and the linear activation function in the output layer were used. The optimized ANN structure was four neurons at the input layer, eighteen neurons at the hidden layer, and one neuron at the output layer. The application of 18 hidden neurons allowed to obtain the best values for R 2 and the mean squared error, 0.99751 and 7.504e-04, respectively, showing the relevance of the training, and hence the network can be used for final prediction of
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