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Multi-response optimization of Fenton process for applicability assessment in landfill leachate treatment

Allahyar Amiri, Mohammad Reza Sabour
Waste management 2014 v.34 pp. 2528-2536
chemical oxygen demand, hydrogen peroxide, iron, landfill leachates, models, oxidation, pH, prediction, response surface methodology, sludge, statistical analysis, waste management
Fenton process, as a pretreatment method, was found to be effective in the primary treatment of mature/medium landfill leachate. However, the main problem of the process is the large amount of produced sludge that requires an accurate feasibility evaluation for operational applications. In this study, the response surface methodology was applied for the modeling and optimization of Fenton process in three target responses, (1) overall COD removal, (2) sludge to iron ratio (SIR) and (3) organics removal to sludge ratio (ORSR), where the latter two were new self-defined responses for prediction of sludge generation and applicability assessment of the process, respectively. The effective variables included the initial pH, [H2O2]/[Fe2+] ratio and Fe2+ dosage. According to the statistical analysis, all the proposed models were adequate (with adjusted R2 of 0.9116–0.9512) and had considerable predictive capability (with prediction R2 up to 0.9092 and appropriate adequate precision). It was found that all the variables had significant effects on the responses, specifically by their observed role in dominant oxidation mechanism. The optimum operational conditions obtained by overlay plot, were found to be initial pH of 5.7, [H2O2]/[Fe2+] ratio of 17.72 and [Fe2+] of 195mM, which led to 69% COD removal, 2.4 (l sludge/consumed mole Fe2+) of SIR and 16.5 (gCOD removed/l produced sludge) for ORSR in verification test, in accordance with models-predicted values. Finally, it was observed that [H2O2]/[Fe2+] ratio and Fe2+ dosage had significant influence on COD removal, while Fe2+ dosage and [H2O2]/[Fe2+] ratio had remarkable effects on SIR and ORSR responses, respectively.