Treatment of landfill leachate wastewater by electrocoagulation process using an aluminium electrode was investigated in a batch electrochemical cell reactor. Response surface methodology based on central composite design was used to optimize the operating parameters for the removal of % color and % total organic carbon (TOC) together with power consumption from landfill leachate. Effects of three important independent parameters such as current density ( X ), inter-electrode distance (X ) and solution pH (X ) of the landfill leachate sample on the % color and % TOC removal with power consumption were 2 3 investigated. A quadratic model was used to predict the % color and % TOC removal with power consumption in different experimental conditions. The significance of each independent variable was calculated by analysis of variance. In order to achieve the maximum % color and % TOC removal with minimum of power consumption, the optimum conditions were about current density (X )—5.25 A/dm , inter-electrode distance (X )—1 cm and initial solution of effluent pH ( X )—7.83, 1 2 3 with the yield of color removal of 74.57%, and TOC removal of 51.75% with the power consumption of 14.80 kWh/m . Electrocoagulation process could be applied to remove pollutants from industrial effluents and wastewater. Keywords Electrocoagulation · Landfill leachate · Color and TOC removal · Power consumption · Central composite design Introduction inorganic compounds, toxic and heavy metals (Fernandes et al. 2015). Due to its complex and recalcitrant composi- Increases in world population and new patterns of consump- tion, the sanitary landfill leachate represents a significant tion have resulted in huge production of wastes that are usu- source of pollutants. Discharge of the landfill leachate into ally discarded in sanitary landfills, since this is relatively the environment can have a detrimental effect on aquatic simple and inexpensive (Azni 2009). Landfill leachate waste - life, cause infertility of soil and mutagenic effect on humans water can be generated from precipitation, surface run-off, as well as affecting the ecological balance. The treatment infiltration or intrusion of groundwater percolating though of landfill leachate wastewater is difficult due to the dis - the landfill (Li et al. 2011). Various types of pollutants can charge standards, variable composition and its high pollut- be found in sanitary landfill leachate such as organic and ant load. Several treatment methods have been used to treat the landfill leachate, such as biological processes (Li et al. 2017; Zhang et al. 2016; Robinson. 2017), membrane pro- * Perumal Asaithambi cesses (Ahn et al. 2002), coagulation and flocculation meth - drasaithambi2014@gmail.com ods (Wang et al. 2015; Liu et al. 2012), flotation methods Esayas Alemayehu (Adlan et al. 2011), adsorption and chemical precipitation esayas16@yahoo.com (Hur and Kim 2000; Erabee et al. 2017), osmosis (Iskander Faculty of Civil and Environmental Engineering, Jimma et al. 2017), chemical oxidation (Derco et al. 2010), Fenton Institute of Technology, Jimma University, Po Box 378, and electrochemical (Vallejo et al. 2012), advanced oxi- Jimma, Ethiopia dation techniques Hu et al. 2011; Zhang et al. 2012; Chys Department of Chemical Engineering, Faculty et al. 2015) and electro-Fenton (Zhang et al. 2014). How- of Engineering, University of Malaya, 50603 Kuala Lumpur, ever, these methods are found to have certain shortages such Malaysia Vol.:(0123456789) 1 3 69 Page 2 of 12 Applied Water Science (2018) 8:69 as operating cost, transfer of one phase to another, lower Chemical reaction that takes place in the aqueous pollutant removal efficiency and decreasing the process medium: performance. Thus, it is essential to design and develop an 3+ − economic and effective treatment method for removing pol- Al + OH → Al(OH) 3(s) (3) aq (aq) ( ) lutants from landfill leachate wastewater. Overall reaction is given by: Electrochemical processes have shown high effectiveness in eliminating persistent pollutants from landfill leachate 3+ + Al + 3H O → Al(OH) + 3H (4) wastewater (Ricordel and Djelal 2014; Panizza et al. 2010) 2 (l) 3(s) (aq) (aq) and have some advantages such as energy efficiency, ver - Based on the literature review, many studies on the elec- satility and cost-effectiveness (Juttner et al. 2000). Among trocoagulation process were carried out by varying one the electrochemical methods, electrocoagulation process factor while the other factors are kept constant (Chopra appears to be the most effective substitution for the conven- and Sharma 2013; Sharma and Chopra 2017). However, tional coagulation and flotation process as it can deal with this approach consumes more time and response surface pollutants with a variety of compositions (Wang et al. 2009; methodology (RSM) can be an alternative to overcome Butler et al. 2017). this problem. Most of the previous studies only focused on Electrocoagulation is a simple process in terms of its the performance of electrocoagulation process such as % equipment setup and easy-to-handle methodology, high COD and % color removal (Saravanan et al. 2010; Janpoor efficiency with production of less sludge (Kalyani et al. et al. 2011), but did not emphasize on the % TOC removal 2009; Sharma and Chopra 2017). It can be operated at with power consumption. It was important to determine the ambient temperature and pressure. Electrocoagulation is an power consumption of electrocoagulation process in order electrolytic process involving the dissolution of the sacrifi- to determine its operating cost and feasibility. The objective cial anodes, made of aluminium (Al), upon application to of this research work is to identify the optimum operating a current between the two electrodes to supply ions to the parameters for the removal of pollutants from landfill lea- wastewater, allowing suspended, emulsified or dissolved chate using central composite design (CCD). contaminants to form agglomerates (Fernandes et al. 2015). RSM is used to optimize the parameters chosen for the The coagulating ions are produced in situ and the successive electrocoagulation process. It is a regression analysis used stages for current theory of electrocoagulation are described to predict the value of dependent variable based on the con- as follows: firstly, the formation of coagulants induced by trolled values of independent variables. Numerous experi- the electrolytic oxidation of the sacrificial anode followed by ment combinations can be generated within a short period of generation of metal hydroxides; secondly, destabilization of time, thus allowing researchers to know whether the tested the contaminants and particulate suspension and breaking of parameter has a significant impact on the research work (Liu emulsions; and lastly, the aggregation or coalescence of the et al. 2012; Butler et al. 2017). In many technical fields, it destabilized phases to form larger and separable agglomer- is common that the output variable (Y) exists with a set of ates (Moreno-Casillas et al. 2007). Hydrogen (H ) bubbles predicted variables or the input variables ( X , X , X , … X ) . 1 2 3 k that evolve from the cathode surface are adsorbed onto the The output variable is a function of input variable together suspended particles. The separation of the solid matter is with the error presence in the model, usually written as achieved either by flotation upon the adsorption of H bub- Y = f X , X , X ,… X +∈ , w h e re f is the unknown sur- 1 2 3 k bles, or allowing the solid to settle down due to its higher face response which is normally described by a first-order or density which the buoyant force produced by the H bubbles second-order polynomial, while ∈ is the error in the model. is insufficient to lift the suspended solid (Zodi et al. 2009). Generally, the first- and second-order models are given as The mechanism of electrocoagulation process depends on in Eqs. (5) and (6): the chemistry of the aqueous medium, especially the con- ductivity. The mechanism of ion formation is proposed as in Eqs. (1)–(4) below using aluminium electrode (Fernandes Y = + X +∈ (5) o j j et al. 2015). j=1 Anodic reaction: k k k−1 k 3+ − Al → Al + 3e 2 s (1) ( ) (aq) Y = + X + X + X X +∈ (6) o j j jj ji j i i j=1 j=1 j=1 i=2 Cathodic reaction: − − where X and X are coded independence variables and , i j j 2H O + 2e → H + 2OH 2 (l) 2(g) (2) (aq) , and ( i = 1, 2,… , k; j = 1, 2,… , k) are the regres- jj ji sion coefficients. A first-order model is used to describe 1 3 Applied Water Science (2018) 8:69 Page 3 of 12 69 the flat surface, while the curve surface is described by a Table 2 Characterization of landfill leachate wastewater second-order model, or also known as a quadratic model. A Parameter Value quadratic model is often adequate for RSM in most cases. COD (mg/L) 7225 Besides, the knowledge of statistical fundamentals, regres- TOC (mg/L) 4000 sion modeling techniques and optimization methods is Absorbance (Au) 4.534 required in fitting the response surface model. pH 8.1 The main objective of optimization was to maximize the Color Dark brown % color and % TOC removal while minimizing the power Smell Pungent ammonia smell consumption by varying operating parameters such as cur- Temperature (°C) 33 rent density (X ), inter-electrode distance (X ) and initial 1 2 Turbidity (NTU) 230 pH (X ). Design of Expert (DoE) Software (11) was used to optimize and study the combined effect of three selected parameters. Each independent variable was coded at three (Al) electrodes with dimension of 16 cm × 6 cm were used levels between − 1 and + 1, where the variables current den- sity (X ), inter˗electrode distance (X ) and initial effluent pH for both anode and cathode. The effective electrode surface 1 2 area was 48 cm and the inter-electrode distance between (X ) were set in the range of 1.05–6.25A/dm , 1.0–4.0 cm, and pH 5–11, respectively, as indicated in Table 1. an anode and cathode was varied from 1 to 4 cm. The pH of the landfill leachate was measured by pH meter (Elico; Model LI120) and varied from pH 5 to 11 using H SO and 2 4 NaOH solutions. The electrodes were connected to a direct Materials and methods current (DC) power supply (APLAB Ltd; Model L1606) with aid of crocodile clips for supplying constant current Materials density, varying from 1.05 to 6.25 A/dm . 3 g/L NaCl was added in the solution to improve the electrical conductivity Landfill leachate wastewater was collected from Jeram sanitary landfill, Selangor, Malaysia. Various parameters of the solution and a magnetic stirrer was used at 500 rpm to increase the probability of particle collision to improve such as pH, chemical oxygen demand (COD), total organic carbon (TOC), color and odor were analyzed and tabulated the efficiency of the electrocoagulation process. After the required experimental condition, sample was taken after 1 h in Table 2. COD was measured by closed reflux method using potassium dichromate (Spectr oquant TR320); TOC of electrolysis time and the filtered using filter paper. Then, the sample was immediately analyzed for color and TOC was measured using the TOC analyzer (TOC-LCSH/CPH) and color was determined using UV/Vis spectrophotometer removal. The removal of the color was determined using the UV/Vis spectrophotometer (Spectroquant TR320) and (Spectroquant Pharo 300). Chemicals such as K Cr O , 2 2 7 NaCl, H SO , NaOH, etc., were used and supplied from TOC was determined using the TOC analyzer (TOC-LCSH/ 2 4 CPH). YEW SII SIE lab analytics supplies, Malaysia. Methods DC power supply Volt Amp Experimental setup for the electrocoagulation process is shown in Fig. 1. Experiment was carried out in a batch reac- + tor with a capacity of 500 mL (YEW SII SIE lab analytics Sampling supplies, Malaysia). Initial COD concentration of the landl fi l port leachate wastewater was diluted into 1500 ppm. Aluminium Anode Cathode Water out Table 1 Coded and actual values of the variables of the design of Effluent experiments for the electrocoagulation process Electrocoagulation cell Variable Unit Factor Levels Water in Magnetic bar stirrer − 1 0 1 Magnetic stirrer Current density A/dm X 1.05 3.65 6.25 Inter-electrode distance cm X 1.0 2.50 4.0 Initial effluent pH – X 5 8 11 Fig. 1 Experimental setup for the electrochemical process 1 3 69 Page 4 of 12 Applied Water Science (2018) 8:69 where V is the cell voltage (V), I is the applied current (A), Analysis t is the electrolysis time (h) and V is the volume of waste- water used (m ). Color and TOC removal (%) The % color and % TOC removal were calculated using Eqs. (7) and (8): Results and discussion [Abs ] − [Abs ] Central composite design i t Color removal eﬃciency (%) = × 100 Abs (7) A 3-factor and 3-level CCD was used to optimize the oper- ating parameters of an electrocoagulation process on the where Abs and Abs are absorbance of samples at initial and i t responses such as the % color and % TOC removal effi- reaction time t for a corresponding wavelength λ . max ciency as well as the power consumption. The total number [TOC ] − [TOC ] of experiment combinations was 20, with 6 replications at i t TOC removal eﬃciency (%) = × 100 the design central to determine the pure error. The total num- TOC (8) ber of runs, experimental conditions, response of % color removal, % TOC removal and power consumption together where TOC is the initial of TOC and T OC is the TOC at i t with the predicted values are shown in Table 3. any reaction time, t (mg/L). Evaluation of experimental results with design Power consumption of experiments The power consumption for the removal of % color and % The % color removal (Y ), % TOC removal (Y ) and power TOC from landfill leachate using the electrocoagulation pro- 1 2 consumption (Y ) are the function of operating parameters cess was calculated using Eq. (9): such as current density (X ), inter-electrode distance (X ) 1 2 VIt kWhr P = , and initial pH (X ) at constant electrolysis time of 1 h. The (9) Table 3 Experimental design Run X X X Color removal (%) TOC removal (%) Power consumption 1 2 3 matrix and response based 3 (kWhr/m ) on the experimental runs and predicted values on the color A/dm cm – Actual Predicted Actual Predicted Actual Predicted removal (%), TOC removal 1 1.05 1 5 45.25 46.67 33.5 33.36 5.12 4.81 (%) and power consumption proposed by the CCD 2 6.25 1 5 70.05 70.63 48.25 48.34 15.50 14.54 3 1.05 4 5 41.5 39.97 21.35 21.46 15.75 14.16 4 6.25 4 5 58.75 58.13 35.75 35.64 36 38.18 5 1.05 1 11 40.5 41.57 27 27.02 3.21 3.18 6 6.25 1 11 60.15 62.13 38.5 38.30 10 10.58 7 1.05 4 11 34.4 34.27 18.25 18.06 9.5 9.46 8 6.25 4 11 50 49.03 28.5 28.54 35 34.30 9 1.05 2.5 8 50.5 49.68 39 39.20 4.5 5.0 10 6.25 2.5 8 70 69.04 51.75 51.93 28 26.91 11 3.65 1 8 73.5 68.46 46 46.23 16.15 20.02 12 3.65 4 8 55.3 58.56 35.25 35.40 36.4 36.56 13 3.65 2.5 5 60 60.16 42 42.05 28 28.68 14 3.65 2.5 11 55 53.06 35 35.33 21 24.35 15 3.65 2.5 8 64 64.59 47 46.87 30 28.66 16 3.65 2.5 8 64 64.59 47 46.87 30 28.66 17 3.65 2.5 8 64 64.59 47 46.87 30 28.66 18 3.65 2.5 8 64 64.59 47 46.87 30 28.66 19 3.65 2.5 8 64 64.59 47 46.87 30 28.66 20 3.65 2.5 8 64 64.59 47 46.87 30 28.66 1 3 Applied Water Science (2018) 8:69 Page 5 of 12 69 quadratic model regression equations were obtained from Y = 28.66 + 8.64X + 8.27X −2.17X + 3.57X X + 0.21X X 3 1 2 3 1 2 1 3 Design Expert Software as shown in Eqs. (10), (11) and 2 2 2 + 0.02X X − 10.39X −0.37X −2.14X 2 3 1 2 3 (12): (12) Experimental data were analyzed by sequential model Y = 64.59 + 9.68X − 4.95X − 3.55X − 1.45X X − 0.85X X 1 1 2 3 1 2 1 3 2 2 2 sum of squares and model summary statistics to obtain − 0.15X X − 5.23X − 1.08X − 7.98X 2 3 1 2 3 the most suitable models among various models such as (10) linear, interactive, quadratic and cubic. The results are Y = 46.87 + 6.36X −5.42X − 3.36X − 0.2X X − 0.93X X tabulated in Tables 4, 5 and 6 for the % color removal, % 2 1 2 3 1 2 1 3 2 2 2 TOC removal and power consumption, respectively. From + 0.74X X − 1.30X − 6.05X − 8.18X 2 3 1 2 3 Tables 4, 5 and 6, it can be seen that quadratic model (11) Table 4 Sequential model sum of squares and model summary statistics for percentage color removal (%) Sequential model sum of squares Source Sum of square df Mean square F value P value Prob > F Mean vs total 65,998.56 1 65,998.56 Linear vs mean 1308.07 3 436.02 7.97 0.0018 2FI vs linear 22.78 3 7.59 0.1158 0.9492 Quadratic vs 2FI 797.58 3 265.86 48.69 < 0.0001 Suggested Cubic vs quadratic 47.36 4 11.84 9.80 0.0084 Aliased Residual 7.25 6 1.21 Total 68,181.60 20 3409.08 Model summary statistics 2 2 2 Source Std. dev. R Adjusted R Predicted R PRESS Linear 7.39 0.5992 0.5240 0.2672 1599.81 2FI 8.10 0.6096 0.4295 − 1.9223 6379.45 Quadratic 2.34 0.9750 0.9525 0.8068 421.76 Suggested Cubic 1.10 0.9967 0.9895 − 3.0784 8903.33 Aliased df degree of freedom Table 5 Sequential model sum of squares and model summary statistics for percentage TOC removal (%) Sequential model sum of squares Source Sum of square df Mean square F value P value Prob > F Mean vs total 30,584.02 1 30,584.02 Linear vs mean 811.25 3 270.42 4.57 0.0170 2FI vs linear 11.52 3 3.84 0.0534 0.9830 Quadratic vs 2FI 934.10 3 311.37 6249.77 < 0.0001 Suggested Cubic vs quadratic 0.1545 4 0.0386 0.6743 0.6338 Aliased Residual 0.3437 6 0.0573 Total 32,341.39 20 1617.07 Model summary statistics 2 2 2 Source Std. dev. R Adjusted R Predicted R PRESS Linear 7.69 0.4616 0.3607 0.0406 1685.96 2FI 8.48 0.4682 0.2227 − 2.8880 6832.70 Quadratic 0.2232 0.9997 0.9995 0.9976 4.22 Suggested Cubic 0.2393 0.9998 0.9994 0.7597 422.31 Aliased df degree of freedom 1 3 69 Page 6 of 12 Applied Water Science (2018) 8:69 Table 6 Sequential model sum of squares and model summary statistics for power consumption, kWhr/m Sequential model sum of squares Source Sum of square df Mean square F value P value Prob > F Mean vs total 9862.57 1 9862.57 Linear vs mean 1477.19 3 492.40 8.64 0.0012 2FI vs linear 102.45 3 34.15 0.5488 0.6577 Quadratic vs 2FI 724.69 3 241.56 28.69 < 0.0001 Suggested Cubic vs quadratic 47.00 4 11.75 1.89 0.2308 Aliased Residual 37.22 6 6.20 Total 12,251.11 20 612.56 Model summary statistics 2 2 2 Source Std. Dev. R Adjusted R Predicted R PRESS Linear 7.55 0.6184 0.5469 0.3484 1556.40 2FI 7.89 0.6613 0.5050 − 0.9141 4571.90 Quadratic 2.90 0.9647 0.9330 0.8584 672.72 Suggested Cubic 2.49 0.9844 0.9507 − 18.1442 45,726.80 Aliased df degree of freedom 2 2 2 gives the highest R , adjusted R and predicted R values Adequacy of the model tested for % color removal, when compared to the other models after excluding the % TOC removal and power consumption cubic model. The cubic model cannot be used for further modeling of experimental data because it was found to The significance and adequacy of the model was analyzed be aliased. An aliased model was a result of insufficient by the analysis of variance (ANOVA) and the results for experiments run to independently estimate all the terms % color removal, % TOC removal and power consumption of the model. Thus, not all parameters can be estimated are given in Tables 7, 8 and 9, respectively. The F test of and it is unwise for further studying an aliased model. The the quadratic models gives a small P value (< 0.05), which highest order polynomial from the sequential model sum indicates that all the models were significant and could of squares, quadratic model, was selected for modeling be used to predict the outcome for the electrocoagulation the treatment of landfill leachate using electrocoagulation process. From Table 7, it can be seen that for the % of process where the additional terms are significant and the color removal, the linear coefficient of the current den- model is not aliased. sity (X ), inter-electrode distance (X ) and initial pH (X ) 1 2 3 Table 7 ANOVA of the second- Source Sum of squares df Mean square F value P value Prob > F order polynomial equation for percentage color removal, (%) Model 2128.44 9 236.49 43.31 < 0.0001 Highly significant X 937.02 1 937.02 171.61 < 0.0001 Highly significant X 245.03 1 245.03 44.87 < 0.0001 Highly significant X 126.03 1 126.03 23.08 0.0007 Significant X X 16.82 1 16.82 3.08 0.1098 1 2 X X 5.78 1 5.78 1.06 0.3278 1 3 X X 0.1800 1 0.1800 0.0330 0.8596 2 3 75.27 1 75.27 13.79 0.0040 Significant 3.22 1 3.22 0.5894 0.4604 175.20 1 175.20 32.09 0.0002 Significant Residual 54.60 10 5.46 Lack of fit 54.60 5 10.92 Pure error 0.0000 5 0.0000 Cor total 2183.04 19 df degree of freedom 1 3 Applied Water Science (2018) 8:69 Page 7 of 12 69 Table 8 ANOVA of the second- Source Sum of squares df Mean square F value P value Prob > F order polynomial equation for percentage TOC removal (%) Model 1756.87 9 195.21 3918.22 < 0.0001 Highly significant X 405.13 1 405.13 8131.85 < 0.0001 Highly significant X 293.22 1 293.22 5885.58 < 0.0001 Highly significant X 112.90 1 112.90 2266.06 < 0.0001 Highly significant X X 0.3200 1 0.3200 6.42 0.0296 Significant 1 2 X X 6.84 1 6.84 137.39 < 0.0001 Highly significant 1 3 X X 4.35 1 4.35 87.34 < 0.0001 Highly significant 2 3 X 4.66 1 4.66 93.61 < 0.0001 Highly significant 100.73 1 100.73 2021.91 < 0.0001 Highly significant 183.89 1 183.89 3690.98 < 0.0001 Highly significant Residual 0.4982 10 0.0498 Lack of fit 0.4982 5 0.0996 Pure error 0.0000 5 0.0000 Cor total 1757.36 19 df degree of freedom Table 9 ANOVA of the second- Source Sum of squares df Mean square F value P value Prob > F order polynomial equation for power consumption, (kWhr/m ) Model 2304.33 9 256.04 30.40 < 0.0001 Highly significant X 746.84 1 746.84 88.69 < 0.0001 Highly significant X 683.43 1 683.43 81.16 < 0.0001 Highly significant X 46.92 1 46.92 5.57 0.0399 Significant X X 102.10 1 102.10 12.12 0.0059 Significant 1 2 X X 0.3445 1 0.3445 0.0409 0.8438 1 3 X X 0.0032 1 0.0032 0.0004 0.9848 2 3 296.97 1 296.97 35.26 0.0001 Significant X 0.3700 1 0.3700 0.0439 0.8382 X 12.62 1 12.62 1.50 0.2490 Residual 84.21 10 8.42 Lack of fit 84.21 5 16.84 Pure error 0.0000 5 0.0000 Cor total 2388.54 19 df degree of freedom 2 2 and the quadratic coefficient of current density ( X ) and current density ( X ) were found to be significant. “Adeq 1 1 initial pH ( X ) were significant, with p value less than Precision” measures the signal-to-noise ratio; it was desir- 0.05. For the % TOC removal, it can be observed from able to obtain a value greater than 4. The signal-to-noise Table 8 that the linear coefficient of current density (X ), ratio was 22.01, 214.61 and 18.60 which is greater than inter-electrode distance (X ), initial pH (X ), interaction 4 for the % color removal, % TOC removal and power 2 3 effect of current density (X ) with inter-electrode distance consumption, respectively. Thus, the second-order model (X ), current density (X ) with initial pH (X ) and inter- can be used to navigate the design space. Adequacy check 2 1 3 electrode distance (X ) with initial pH (X ) and quadratic is crucial to make sure the approximation model can give 2 3 coefficient of current density (X ), inter-electrode distance adequate approximation to prevent poor and misleading 2 2 ( X and initial pH ( X ) were significant variables. For the result. 2 3 power consumption from Table 9, the linear effect of cur - rent density (X ), inter-electrode distance (X ) and initial 1 2 pH (X ), interaction effect of current density (X ) with 3 1 inter-electrode distance (X ) and the quadratic effect of the 1 3 69 Page 8 of 12 Applied Water Science (2018) 8:69 Experimental versus predicted The comparison between experimental and predicted value is shown in Table 3 and Fig. 2a–c. From Fig. 2, it can be seen that the model-predicted values matched the experi- mental data in which all the points are closed to the diagonal line. The ANOVA analysis showed that all the three quad- ratic models were significant (p < 0.05) and can be used to predict the % of color removal, % TOC removal, and also power consumption. The quality of predicted points was 2 2 verified by the R value, where the R values were 0.97, 0.99 and 0.96 for % of color removal, % TOC removal and power consumption, respectively. Combined effect of operating parameters for % color removal, % TOC removal and power consumption The effect of operating parameters in estimating the maxi- mum % color removal, % TOC removal and minimum of power consumption with respect to each variable and the impact of each operating parameter on the output are dis- cussed as following. The electrolysis time for the electroco- agulation process was 1 h and the initial COD concentration of the leachate is diluted to 1500 ppm to visualize a better and clearer result. Combined effect of current density (X ) and inter‑electrode distance (X ) The combined effect of current density (X ) and inter-elec- trode distance (X ) on % color and % TOC removal with power consumption was tested by varying X from 1.05 to 6.25 A/dm and X from 1 to 4 cm and the results are tabu- lated in Table 3 and plotted in Fig. 3a–c. From Fig. 3a, b, it can be observed that the % color removal and % TOC removal were increased as the current density increased, but after the optimum value, further increase in current den- sity does not help in improving the removal of % color and TOC (Kalyani et al. 2009). The increase in current density 3+ resulted in the production of large amount of Al ions via anodic metal dissolution, more H bubbles was formed at the cathode, which are profitable for the separation or flota- tion process (Ozyonar and Karagozoglu 2015). From Fig. 3c, it can be seen that the increase in current density caused an increase in the power consumption. This is because, an increase in current density caused an increase in cell voltage, Fig. 2 Plot for relationship between experimental and predicted value which had a direct impact on the power consumption of the for a % color removal, b % TOC removal and c power consumption electrochemical process. Since a proportional relationship was established between the current density and power con- sumption, it is necessary to identify the optimum value of current density to reduce the power consumption and operat- ing cost (Heidmann and Calmano 2008). 1 3 Applied Water Science (2018) 8:69 Page 9 of 12 69 as the inter-electrode distance increased from 1 to 4 cm at any value of current density in the range of 1.05–6.25 A/ dm . This is because there is an increased in ohmic voltage drop as the distance between the anode and cathode was increased (Khandegar and Saroha 2013). Besides, Faraday’s law also stated that the amount of oxidized metal decreased as the gap between the electrodes was increased. However, Fig. 3c shows that the power consumption increased as the inter-electrode distance increased. This was due to the fact that there is more resistance oe ff red when the electrodes gap increase and power consumption is directly proportional to the cell voltage (Ricordel and Djelal 2014). Combined effect of initial pH (X ) and current density (X ) Initial pH of the landfill leachate (X ) was adjusted in the range of pH 5–11 to investigate the impact of the pH on the % color removal, % TOC removal and power consump- tion. The result is given in Table 3 and plotted in Fig. 4a–c. From Fig. 4a, b, it can be seen that the % color and % TOC removal were increased at effluent pH from 5 to 7.5; how - ever, further increase in pH from 7.5 to 11 decreased the removal efficiency. This can be explained by the formation of aluminium species formed in the reaction. For the Al elec- trodes in acidic medium, monomeric hydroxometallic cat- ion Al(OH) is formed. At neutral medium, both polymeric hydroxometallic cations and metal hydroxides precipitates coexist while at higher pH or alkali medium, the net charge on the surface of the amorphous metal hydroxide precipitate changes from positive to negative and the polymeric cations will only remain in the solution. More OH can be formed in neutral condition compared to acidic and alkaline medi- ums in the electrocoagulation process (Modirshahla et al. 2007; Kobya et al. 2003). However, from Fig. 4c, it can be seen that the initial pH of the leachate had no impact on the power consumption for the electrocoagulation process. This is because the conductivity of the landfill leachate did not change as a result of pH adjustment; thus, 3 g/L of NaCl or mediator has been added in before starting the experiment. Optimization The main objective of this study is to determine the optimal operating parameters for the maximum % color and % TOC Fig. 3 Combined effect of current density (X ) and inter-electrode distance (X ) on a % color removal, b % TOC removal and c power removal with the minimum of power consumption from consumption landfill leachate wastewater using the electrocoagulation process. The results were optimized using the regression equation of RSM based on CCD. While optimizing, all the Inter-electrode distance (X ) was varied from 1 to 4 cm input variables such as current density (X ), inter-electrode 2 1 in order to study its effect on the % color removal, % TOC distance (X ) and initial pH (X ) were selected as within the 2 3 removal and power consumption. From Fig. 3a, b, it was seen range while the output variables such as % color removal and that the % color removal and % TOC removal was decreased % TOC removal were maximized with power consumption 1 3 69 Page 10 of 12 Applied Water Science (2018) 8:69 Fig. 5 Spectra of landfill leachate wastewater, recorded before and after the electrocoagulation process at different electrolysis times experimentally, which is closed to the predicted result. From the expected and actual result, it can be said that there was good correlation between them which indicates that the cen- tral composite design could be effectively used to optimize the electrocoagulation process parameters. Instrumental analysis The absorption spectra of before and after treatment of electrocoagulation process were analyzed using the UV/Vis spectrophotometer (Spectroquant Phar o 300) to study the color removal rate from landfill leachate wastewater. The absorbance spectrum for the landfill leachate effluent had an absorbance peak at 284 nm which belongs to the coloring agent. From Fig. 5, it can be seen that there was reduction in absorbance of peak with increasing electrolysis time. It might be attributed that, the color of the landfill leachate wastewater was continuously reduced with increasing elec- trochemical reaction time. Conclusion Fig. 4 Combined effect of initial pH (X ) and current density (X ) on 3 1 This study investigated the removal of % color and % TOC a % color removal, b % TOC removal and c power consumption using electrocoagulation process in a real landfill leachate wastewater. An empirical relationship between the out- minimized. The optimized operating parameters are as fol- put and independent variables was obtained based on the lowing: current density (X )—5.25 A/dm , inter-electrode experimental data and it was expressed by the quadratic distance (X )—1 cm and initial pH (X )—7.83 with expected model using RSM. The results showed that, the maximum 2 3 result of color removal to be 74.57%, TOC removal of % color removal, % TOC removal and minimum of power 3 3 51.74% and 14.80 kWh/m for power consumption. A mean consumption were 74.57, 51.75 and 14.80 kWh/m , respec- value of 75.20% for color removal, 50.90% for TOC removal tively, obtained at the optimum conditions of current density 3 2 and 13.75 kWh/m for power consumption was obtained (X ) of 5.25 A/dm , inter-electrode distance (X ) of 1 cm 1 2 1 3 Applied Water Science (2018) 8:69 Page 11 of 12 69 Iskander SM, Zou S, Brazil B, Novak JT, He Z (2017) Energy con- and initial effluent pH of 7.83. Based on the experimental sumption by forward osmosis treatment of landfill leachate for results, an empirical relationship between the response and water recovery. Waste Manage 63:284–291 independent variables was obtained and expressed by the Janpoor F, Torabian A, Khatibikamal V (2011) Treatment of laundry second-order polynomial equation. The ANOVA analysis waste-water by electrocoagulation. J Chem Technol Biotechnol 86:1113–1120 showed a high coefficient of determination value, thus ensur - Juttner K, Galla U, Schmieder H (2000) Electrochemical approaches ing a satisfactory adjustment of the second-order regression to environmental problems in the process industry. Electrochim model with the experimental data. This technology could be Acta 45(15–16):2575–2594 used effectively for the removal of pollutants from industrial Kalyani KSP, Balasubramanian N, Srinivasakannan C (2009) Decol- orization and COD reduction of paper industrial effluent using effluents and wastewater. electro-coagulation. Chem Eng J 151(1–3):97–104 Khandegar V, Saroha AK (2013) Electrocoagulation for the treat- Open Access This article is distributed under the terms of the Crea- ment of textile industry effluent—a review. J Environ Manage tive Commons Attribution 4.0 International License (http://creat iveco 128:949–963 mmons.or g/licenses/b y/4.0/), which permits unrestricted use, distribu- Kobya M, Can OT, Bayramoglu M (2003) Treatment of textile waste- tion, and reproduction in any medium, provided you give appropriate waters by electrocoagulation using iron and aluminum elec- credit to the original author(s) and the source, provide a link to the trodes. J Hazard Mater 100(1–3):163–178 Creative Commons license, and indicate if changes were made. Li X, Song J, Guo J, Wang Z, Feng Q (2011) Landfill leachate treat- ment using electrocoagulation. Proc Environ Sci 10:1159–1164 Li YL, Wang J, Yue ZB, Tao W, Yang HB, Zhou YF, Chen TH (2017) Simultaneous chemical oxygen demand removal, meth- ane production and heavy metal precipitation in the biological References treatment of landfill leachate using acid mine drainage as sulfate resource. J Biosci Bioeng 124(1):71–75 Adlan MN, Palaniandy P, Aziz HA (2011) Optimization of coagula- Liu X, Li XM, Yang Q, Yue X, Shen TT, Zheng W, Zeng GM (2012) tion and dissolved air flotation (DAF) treatment of semi-aerobic Landfill leachate pretreatment by coagulation–flocculation pro- landfill leachate using response surface methodology (RSM). cess using iron-based coagulants: optimization by response sur- Desalination 277(1–3):74–82 face methodology. Chem Eng J 200–202:39–51 Ahn WY, Kang MS, Yim SK, Choi KH (2002) Advanced landfill lea- Modirshahla N, Behnajady MA, Kooshaiian S (2007) Investigation chate treatment using an integrated membrane process. Desali- of the effect of different electrode connections on the removal nation 149(1–3):109–114 efficiency of Tartrazine from aqueous solutions by electroco- Azni I (2009) What is the choice: land disposal or biofuel? In: Waste agulation. Dyes Pigm 74(2):249–257 Management. Universiti Putra Malaysia, pp 1–65. http://dx.doi. Moreno-Casillas HA, Cocke DL, Gomes JAG, Morkovsky P, Parga org/10.1016/S0026 -0576(96)94124 -0 JR, Peterson E (2007) Electrocoagulation mechanism for COD Butler EB, Hung YT, Mulamba O (2017) The effects of chemical removal. Sep Purif Technol 56(2):204–211 coagulants on the decolorization of dyes by electrocoagulation Ozyonar F, Karagozoglu B (2015) Treatment of pretreated coke using response surface methodology (RSM). Appl Water Sci wastewater by electrocoagulation and electrochemical peroxi- 7:2357–2371 dation processes. Sep Purif Technol 150:268–277 Chopra AK, Sharma AK (2013) Removal of turbidity, COD and Panizza M, Delucchi M, Sires I (2010) Electrochemical process BOD from secondarily treated sewage water by electrolytic for the treatment of landfill leachate. J Appl Electrochem treatment. Appl Water Sci 3:125–132 40:1721–1727 Chys M, Declerck W, Audenaert WTM, Van Hullea SWH (2015) Ricordel C, Djelal H (2014) Treatment of landfill leachate with high UV/H O, O and (photo-) Fenton as treatment prior to granu- 2 2 3 proportion of refractory materials by electrocoagulation: sys- lar activated carbon filtration of biologically stabilized landfill tem performances and sludge settling characteristics. J Environ leachate. J Chem Technol Biotechnol 90:525–533 Chem Eng 2(3):1551–1557 Derco J, Gotvajn AZ, Zagorc-Koncan J (2010) Pretreatment of Robinson T (2017) Removal of toxic metals during biological treat- landfill leachate by chemical oxidation processes. Chem Pap ment of landfill leachates. Waste Manage 63:299–309 64(2):237–245 Saravanan M, Pabmanavhan Sambhamurthy N, Sivarajan M (2010) Erabee IK, Ahsan A, Jose B, Manniruzzaman M, Aziz A, Ng AWM, Treatment of Acid Blue 113 Dye Solution Using Iron Electro- Idrus S, Daud NNN (2017) Adsorptive treatment of landfill coagulation. CLEAN Soil Air Water 38(5–6):565–571 leachate using activated carbon modified with three differ - Sharma AK, Chopra AK (2017) Removal of nitrate and sulphate ent methods. KSCE J Civ Eng. https ://doi.org/10.1007/s1220 from biologically treated municipal wastewater by electroco- 5-017-1430-z agulation. Appl Water Sci 7:1239–1246 Fernandes A, Pacheco MJ, Ciríaco L, Lopes A (2015) Review on the Vallejo M, San Román MF, Irabien A, Ortiz I (2012) Comparative electrochemical processes for the treatment of sanitary landfill study of the destruction of polychlorinated dibenzo-p-dioxins leachates: present and future. App Catal B 76–177:183–200 and dibenzofurans during Fenton and electrochemical oxidation Heidmann I, Calmano W (2008) Removal of Cr(VI) from model of landfill leachates. Chemosphere 90(1):132–138 wastewaters by electrocoagulation with Fe electrodes. Sep Purif Wang CT, Chou WL, Kuo YM (2009) Removal of COD from laun- Technol 61(1):15–21 dry wastewater by electrocoagulation/electroflotation. J Hazard Hu X, Wang X, Ban Y, Ren B (2011) A comparative study of UV– Mater 164(1):81–86 Fenton, UV–H O and Fenton reaction treatment of landfill 2 2 Wang C, Alpatova A, McPhedran KN, Gamal El-Din M (2015) leachate. Environ Tech 32(9):945–951 Coagulation/flocculation process with polyaluminum chloride Hur JM, Kim SH (2000) Combined adsorption and Chemical pre- for the remediation of oil sands process-affected water: perfor - cipitation process for pretreatment or post-treatment of landfill mance and mechanism study. J Environ Manage 160:254–262 leachate. Korean J Chem Eng 17:433–437 1 3 69 Page 12 of 12 Applied Water Science (2018) 8:69 Zhang H, Wu X, Li X (2012) Oxidation and coagulation removal Zodi S, Potier O, Lapicque F, Leclerc JP (2009) Treatment of the tex- of COD from landfill leachate by Fered-Fenton process. Chem tile wastewaters by electrocoagulation: effect of operating param- Eng J 210:188–194 eters on the sludge settling characteristics. Sep and Purif Technol Zhang DB, Wu XG, Wang Hui YS, Zhang H (2014) Landfill lea- 69(1):29–36 chate treatment using the sequencing batch biofilm reactor method integrated with the electro-Fenton process. Chem Pap Publisher’s Note Springer Nature remains neutral with regard to 68(6):782–787 jurisdictional claims in published maps and institutional affiliations. Zhang D, Vahala R, Wang Y, Smets BF (2016) Microbes in biologi- cal processes for municipal landfill leachate treatment: commu - nity, function and interaction. Int Biodeterior Biodegradation 113:88–96 1 3
Applied Water Science – Springer Journals
Published: Apr 23, 2018
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