Abstract
In this work, artificial neural networks are used to represent advanced oxidation processes, such as electrochemical oxidation, electro-Fenton, and photoelectro-Fenton, for the degradation of two dyes. The effect of treatment time, initial dye concentration, and current density on the degradation percentage is studied. Additionally, a network is developed to include discrete variables, such as treatment type and dye type, as input features, enabling it to predict the system's performance across different technologies and pollutants. Operating conditions are optimized using the universal ANN as a surrogate model and the adaptive differential evolution algorithm to maximize dye removal efficiency. According to the results, after optimizing the architecture of the artificial neural networks using Bayesian optimization, deviations of 3.9% or less are obtained for purple RL removal predictions, while for Green A, deviations of 8% or less are obtained. These models may serve as a basis for more robust representations in the future, including the effects of electrochemical device size, among other relevant variables.