Photocatalytic Degradation of Oxytetracycline in Aqueous Solutions with TiO2 in Suspension and Prediction by Artificial Neural Networks

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 TiO₂ 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 R² 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 photocatalytic degradation of OTC with suspended TiO₂.