Landfill leachate pretreatment by coagulation–flocculation process using iron-based coagulants: Optimization by response surface methodology

Liu, Xian; Li, Xiaoming; Yang, Qi; Yue, Xiu; Shen, Tingting; Zheng, Wei; Luo, Kui; Sun, Yi-Hu; Zeng, Guangming

doi:10.1016/j.cej.2012.06.012

Cited by 155 publications

(62 citation statements)

References 30 publications

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“…However, the removal rate of SS and color by IC were at par for all range of pH tested. A study by Liu et.al, shows that optimum pH (COD=68% removal, color= 93% removal) for IC in treating leachate is at pH8 with 10 g/L dose [14]. While for this study, almost the same removal of COD (66%) and color(95%) at the optimum pH of 6was observed.…”

Section: Influence Of Phsupporting

confidence: 60%

Removal of Color, Suspended Solids, COD and Ammonia from Partially Stabilize Landfill Leachate by Using Iron Chloride through Coagulation Process

Zin¹,

Aziz²,

Adlan³

et al. 2013

IJET

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Abstract-Sufficient treatment of leachate is vital before it is discharged into receiving water bodies. MLS leachate is characterized as partially stabilized leachate. Therefore, coagulation is a fair treatment to degrade pollutants in MLS leachate. The aim of this study was to evaluate the optimum coagulation conditions of dose and pH of iron chloride for MLS leachate by using jar test. The influence of iron chloride dose, resulted in percentage removal of SS (97% at 1500 mg/L), color (95% at 1500 mg/L), COD (66% at 2000 mg/L) and NH3-N(13% at 3500 mg/L). While theaffect of pH, SS (94% at pH6), color(95% at pH6), COD(66% at pH 6) and NH3-N(56% at pH 6) were observed. Optimum dose and pH at 1500 mg/L and 6 were selected for SS, color and COD.Meanwhile,dose 3500 mg/L and pH 12 were selected for the optimum coagulation condition for NH3-N removal. Overall, treatment of the MLS leachate by the coagulation process with addition of IC was effective for SS, color and COD but weak in reducing NH3-N.

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Section: Influence Of Phsupporting

confidence: 60%

Removal of Color, Suspended Solids, COD and Ammonia from Partially Stabilize Landfill Leachate by Using Iron Chloride through Coagulation Process

Zin¹,

Aziz²,

Adlan³

et al. 2013

IJET

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“…These results were in agreement with those of the current study and indicated the good performance of CaCl 2 compared to other coagulants. 3 , indicating the high rate of consumption of common coagulants in the treatment of wastewater (20). The results of the present study showed that the percentage of turbidity removal depended on the initial concentration of turbidity in MWFs.…”

Section: Discussionmentioning

confidence: 58%

Optimizing functions of coagulants in treatment of wastewater from metalworking fluids: Prediction by RSM method

Jamali

Moradnia

2018

Environ Health Eng Manag

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Background: Coagulation is a treatment procedure for metalworking fluids (MWFs). This study aimed to optimize coagulation using four coagulants and compare the results. Methods: In this research, the outputs of a coagulation procedure in chemical oxygen demand (COD) removal, turbidity, and the release of oil were investigated using four coagulants, ferric chloride (FeCl 3 ), ferric sulfate (Fe 2 (SO 4 ) 3 , calcium chloride (CaCl 2 ), and aluminum sulfate (Al 2 (SO4) 3 . To optimize the process, a central composite design (CCD) and response surface methodology (RSM) were used. Results: Under optimal conditions for using calcium chloride (coagulant concentration 4.2 g/L and pH 3.71), COD and turbidity removals and oil released were respectively 93%, 96.9%, and 31.8 mL. COD and turbidity removals and oil released using aluminum sulfate as a coagulant under optimal conditions (5.36 g/L, pH 4.64) were 75.7%, 89.9%, and 28.9 mL, respectively. With respect to ferric sulfate under optimal conditions (6.25 g/L, pH 3.4), COD and turbidity removals were 60% and 84.1%, respectively and oil released was 16.1 ml. COD and turbidity removals and the greatest amount of oil released by ferric chloride under optimal conditions (3.16 g/L, pH 3.5) were 54.4%, 84.7%, and 15.9 ml, respectively.

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“…1b). In a recent study, Liu et al (2012) reported an optimum pH of 8 and dose 10 g/l for 68.65 % reduction in COD. It was observed that coagulant dosage had a far significant effect on COD removal within the chosen range as compared to that of pH and reaction time.…”

Section: Resultsmentioning

confidence: 99%

Coagulation of landfill leachate by FeCl3: process optimization using Box–Behnken design (RSM)

Kumar

Bishnoi

2015

Appl Water Sci

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FeCl 3 coagulation was used to achieve maximum reduction of COD, phosphate, sulfate and color and process optimization (FeCl 3 dosage, pH, reaction time) was done by BBD-RSM. Responses were recorded in terms of EC, COD, phosphate, color and sulfate removal. Variables A (pH) and B (reaction time) were negatively related to removal of COD and phosphate, whereas, C (FeCl 3 dosage) was positive in case of COD removal and negative for phosphate removal. pH and coagulant dosage had negative relationship with color removal; however, reaction time showed positive relationship. In case of percent sulfate removal, variable A (pH) demonstrated negative relationship whereas B (reaction time) and C (FeCl 3 dosage) were found to be positively related. Numerical optimization of the model revealed a maximum reduction of 71, 93, 86 and 99.6 % COD, phosphate, color and sulfate at optimal FeCl 3 dosage = 3 g/l, pH 8, and reaction time = 95 min.

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Landfill leachate pretreatment by coagulation–flocculation process using iron-based coagulants: Optimization by response surface methodology

Cited by 155 publications

References 30 publications

Removal of Color, Suspended Solids, COD and Ammonia from Partially Stabilize Landfill Leachate by Using Iron Chloride through Coagulation Process

Removal of Color, Suspended Solids, COD and Ammonia from Partially Stabilize Landfill Leachate by Using Iron Chloride through Coagulation Process

Optimizing functions of coagulants in treatment of wastewater from metalworking fluids: Prediction by RSM method

Coagulation of landfill leachate by FeCl3: process optimization using Box–Behnken design (RSM)

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