Energy consumption and relative efficiency improvement of Photo-Fenton - Optimization by RSM for landfill leachate treatment, a case study.

The main objective of this study is to investigate the application of Photo-Fenton process to treat landfill leachate with minimum energy consumption and maximum COD removal efficiencies, simultaneously. Accordingly, an operational assessment of Photo-Fenton process was conducted in terms of variables, namely oxidation pH, [H2O2]/[Fe2+] molar ratio, and Fe2+ dosage. The Central Composite Design (CCD) based on Response Surface Methodology (RSM) was applied for statistical analysis and optimization of target parameters. To assess the rate of energy consumption in Photo-Fenton process, the EEM parameter (Electric Energy consumed per organic Mass removed) was introduced, and the same Fenton experiments were performed to compare the results. Applying UV light in the Fenton process (i.e. Photo-Fenton process) increased COD removal efficiencies up to 10%. The results showed that the Photo-Fenton treatment is capable of removing COD by over 80% of the initial COD (i.e. 17,200 mg/L of AradKooh landfill leachate), applying 195-265 mM iron concentration, [H2O2]/[Fe2+] molar ratio of 15.50-20.55, and the oxidation pH value of 3.75-5.55 (other conditions were oxidation time of 30 min, coagulation pH of 8, and coagulation time of 25 min). The regeneration of Fe(II) from Fe(III) by UV light irradiation resulted in a larger degradation of COD than that of conventional Fenton process, and also reduced the amount of iron catalyst consumption (approximately 25% reduction observed). Furthermore, the cost of energy in Photo-Fenton process could be covered considering lower amounts of sludge generated than conventional Fenton treatment (note that the cost evaluations in current paper were based on batch studies and should be confirmed on full-scale system).

Comparative study of ANN and RSM for simultaneous optimization of multiple targets in Fenton treatment of landfill leachate.

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.

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

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]/[Fe(2+)] ratio and Fe(2+) dosage. According to the statistical analysis, all the proposed models were adequate (with adjusted R(2) of 0.9116-0.9512) and had considerable predictive capability (with prediction R(2) 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]/[Fe(2+)] ratio of 17.72 and [Fe(2+)] of 195 mM, which led to 69% COD removal, 2.4 (l sludge/consumed mole Fe(2+)) 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]/[Fe(2+)] ratio and Fe(2+) dosage had significant influence on COD removal, while Fe(2+) dosage and [H2O2]/[Fe(2+)] ratio had remarkable effects on SIR and ORSR responses, respectively.

Application of quadratic regression model for Fenton treatment of municipal landfill leachate.

The effectiveness of Fenton process in municipal landfill leachate treatment, as a pre- or post-treatment approach, has been demonstrated. However, no general recommendations of universal validity could be made in the term of optimized conditions affecting Fenton process. At the first stage of this study, collected leachate samples from Aradkooh site, Tehran, Iran, were investigated using one-factor-at-a-time method to find out optimum coagulation pH and flocculation time values. Subsequently, the obtained results in addition to data issued previously by the authors were employed to develop a predictive model of the true response surface, namely chemical oxygen demand (COD) removal efficiency. Finally, the main parameters of Fenton procedure, i.e. initial pH, [H(2)O(2)]/[Fe(2+)] molar ratio, Fe(2+) dosage, and coagulation pH were optimized taking advantage of the above-mentioned quadratic model. The derived second-order model included both significant linear and quadratic terms and seemed to be adequate in predicting responses (R(2)=0.9896 and prediction R(2)=0.6954). It was found that the interaction between initial pH and Fe(2+) dosage has a significant effect on COD removal. While, the optimal [H(2)O(2)]/[Fe(2+)] molar ratio was independent of ferrous ion dosage. The optimum conditions for the maximum COD removal of 50.76% for the parameters of initial pH, [H(2)O(2)]/[Fe(2+)] molar ratio, Fe(2+) dosage, and coagulation pH were found to be 5.8, 8.0, 22,500 mg/L, and 8.7 respectively.