title: Comparative study of ANN and RSM for simultaneous optimization of multiple targets in Fenton treatment of landfill leachate.
abstract: In this study, two modeling methods, namely response surface methodology (RSM) and artificial neural networks (ANN), were applied to investigate the Fenton process performance in landfill leachate treatment. For this purpose, three targets were used to cover different aspects of post-treatment products such as supernatant and sludge: mass content ratio (MCR) and mass removal efficiency (MRE). It was observed that coagulation was dominant mechanism in all responses. The proposed models were evaluated based on correlation coefficient (R2), root mean square error (RMSE) and average error (AE) and both models seemed satisfactory. However, the better results of 0.97-0.98 for R2, 1.45-1.86 for RMSE and 2-4% for error, indicated relative superiority of ANN compared to RSM. In addition, it was revealed that [H2O2]/[Fe2+] mole ratio had the greatest effect in the targets, while Fe dosage and pH had lower ones. Finally, to investigate the predictive performance of both models, some additional experiments were conducted in expected optimum conditions that resulted to 27% sludge MCR, 14% effluent MCR, and 56% MRE. The results showed low deviation from predicted values with maximum errors of 8% and 9% for RSM and ANN, respectively. Though in most cases, ANN error values were lower than RSM values. Also, it was proved that setting RSM prior to ANN (as a feeding tool) improves the predictive capability of ANN significantly.
authors.affiliation: K. N. Toosi University of Technology, Department of Civil Engineering, 1996715433 Tehran, Iran.
authors.forename: Mohammad Reza
authors.initials: MR
authors.lastname: Sabour
authors.affiliation: K. N. Toosi University of Technology, Department of Civil Engineering, 1996715433 Tehran, Iran. Electronic address: alahyar_amiri@mail.kntu.ac.ir.
authors.forename: Allahyar
authors.initials: A
authors.lastname: Amiri
mesh_terms: D006861:Hydrogen Peroxide; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D012722:Sewage; D014874:Water Pollutants, Chemical
chemical_list: D012722:Sewage; D014874:Water Pollutants, Chemical; D006861:Hydrogen Peroxide
keywords: Advanced oxidation processes (AOPs); Artificial neural networks (ANN); Mass content ratio; Prediction improvement; Response surface methodology (RSM)