Combination of ANNs and heuristic algorithms in modelling and optimizing of Fenton processes for industrial wastewater treatment

Abstract

© 2022, The Author(s) under exclusive licence to Iranian Society of Environmentalists (IRSEN) and Science and Research Branch, Islamic Azad University.This study evaluated the COD removal performances of classical-Fenton and photo-Fenton Processes by different prediction models. To optimize both Fenton processes performed in batch reactors, the effects of H2O2 dose, Fe(II) dose, H2O2/Fe(II) rate, and contact time were determined as the independent variables of the prediction models. Besides response surface methodology, three neural networks were used to more reliably and effectively predict the behaviors of dependent variables at different values of relevant parameters. Multi-Layer Perceptron trained by Levenberg–Marquardt (MLP-LM), Multi-Layer Perceptron and Single Multiplicative Neuron models trained by Particle Swarm Optimization algorithm (MLP-PSO; SMN-PSO) were studied. Models’ prediction performances were evaluated by Root-Mean-Square Error (RMSE) and Mean Absolute Percent Error criteria. Regression analysis was applied to determine the performance of the best model. The results from both criteria indicated that SMN-PSO model produced the best predictive results in almost all cases. Moreover, the key process parameters were determined by applying the genetic algorithm to SMN-PSO model outputs. The optimized conditions achieved the optimum removal with over 99% desirability. The optimum Fe(II) dose was determined as 399.99 mg/L in both Fenton processes. H2O2 dose was found as 726.18 and 894.07 mg/L and removal efficiencies were achieved 86.50 and 87.49% for classical Fenton and photo-Fenton, respectively. As a result, it will be possible to simulate and improve the different Fenton processes and determine the optimum process parameters by the obtained data in the treatment of wastewater with similar characteristics without many experiments, which are difficult and costly.