Optimizing stabilization of waste-activated sludge using Fered-Fenton process and artificial neural network modeling (KSOFM, MLP)

• Published in: Environmental science and pollution research international Vol. 21; no. 11; pp. 7177 - 7186
• Main Authors: Badalians Gholikandi, Gagik, Masihi, Hamidreza, Azimipour, Mohammad, Abrishami, Ali, Mirabi, Maryam
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer-Verlag 01.06.2014; Springer Berlin Heidelberg; Springer Nature B.V
• Subjects: Activated sludge; Algorithms; Aquatic Pollution; Atmospheric Protection/Air Quality Control/Air Pollution; Chemical oxygen demand; Computer simulation; Correlation; Current density; Detention time; Earth and Environmental Science; Ecosystem management; Ecotoxicology; Efficiency; Electrodes; Environment; Environmental Chemistry; Environmental Health; Environmental science; Environmental studies; Graphite; Heavy metals; Hydrogen peroxide; Hydrogen Peroxide - chemistry; Hydrogen-Ion Concentration; Investigations; iron; Iron - chemistry; Laboratories; Mathematical models; Neural networks; Neural Networks (Computer); Optimization; Organic loading; Oxidation; Power supply; prediction; Predictions; Research Article; Retention time; Sewage - chemistry; Sludge; Stabilization; Statistical analysis; Sulfuric acid; Suspended solids; Waste Disposal, Fluid - methods; Waste Water; Waste Water Technology; Wastewater treatment; Water Management; Water Pollution Control; Water treatment; Iran; Waste-activated sludge; Sludge stabilization; Kohonen self organizing feature maps (KSOFM); Multi-layer perceptron (MLP); Artificial neural network; Fered-Fenton process; VSS removal
• ISSN: 0944-1344; 1614-7499; 1614-7499
• DOI: 10.1007/s11356-014-2633-1

Sludge management is a fundamental activity in accordance with wastewater treatment aims. Sludge stabilization is always considered as a significant step of wastewater sludge handling. There has been a progressive development observed in the approach to the novel solutions in this regard. In this research, based on own initially experimental results in lab-scale regarding Fered-Fenton processes in view of organic loading (volatile-suspended solids, VSS) removal efficiency, a combination of both methods towards proper improving of excess biological sludge stabilization was investigated. Firstly, VSS removal efficiency has been experimentally studied in lab-scale under different operational conditions taking into consideration pH [Fe²⁺]/[H₂O₂], detention time [H₂O₂], and current density parameters. Therefore, the correlations of the same parameters have been determined by utilizing Kohonen self-organizing feature maps (KSOFM). In addition, multi-layer perceptron (MLP) has been employed afterwards for a comprehensive evaluation of investigating parameters correlation and prediction aims. The findings indicated that the best proportion of iron to hydrogen peroxide and the optimum pH were 0.58 and 3.1, respectively. Furthermore, maximum retention time about 6 h with a hydrogen peroxide concentration of 1,568 mg/l and a current density of 650–750 mA results to the optimum VSS removal (efficiency equals to 81 %). The performance of KSOFM and MLP models is found to be magnificent, with correlation ranging (R) from 0.873 to 0.998 for the process simulation and prediction. Finally, it can be concluded that the Fered-Fenton reactor is a suitable efficient process to reduce considerably sludge organic load and mathematical modeling tools as artificial neural networks are impressive methods of process simulation and prediction accordingly.