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Original article
13 (
9
); 7032-7044
doi:
10.1016/j.arabjc.2020.07.009

Statistical optimizing of electrocoagulation process for the removal of Cr(VI) using response surface methodology and kinetic study

,
a Department of Chemical Engineering, V.G.E.C. – Chandkheda, Gujarat Technological University, Ahmedabad, Gujarat 382424, India
b Department of Chemical Engineering, L.D.C.E. – Navaranpura, Gujarat Technological University, Ahmedabad, Gujarat 380015, India

⁎Corresponding authors. sunilpatel.juet@gmail.com (Sunil R. Patel), sachinparikh@hotmail.com (Sachin P. Parikh)

Received: , Accepted: ,
© The Author(s) 2020
Disclaimer:
This article was originally published by Elsevier and was migrated to Scientific Scholar after the change of Publisher.

Peer review under responsibility of King Saud University.

Abstract

Abstract

  • Copper electrode introduced for removal of Cr(VI) and found good results.

  • Optimization using response surface methodology employing central composite design yielded good results.

  • The effect of initial pH 2–7 was negligible on Cr(VI) removal efficiency.

  • Increase of current density accelerated the Cr(VI) removal percentage.

  • Electrocoagulation followed first-order kinetic model.

Abstract

Optimization of electrocoagulation (EC) using copper electrode in terms of Cr(VI) removal from simulated waste water was executed by applying surface methodology and kinetic study. In this research, electrocoagulation process was applied to evaluate the outcome of operational parameters such as initial Cr(VI) concentration, pH, electrode distance, current density and supporting electrolyte (NaCl) concentration for the removal of Cr(VI). The experimental results showed that current density of 41.32 A/m2, electrode distance of 1.4 cm, initial pH of 5.65, time of electrocoagulation of 40 min and initial conductivity 0.21 ms are the optimal operating parameters to attain 93.33% removal efficiency of Cr(VI) ions from simulated waste water. The high value of R2 = 98.15 and R2adj = 96.49 show that fitted model confirms a good agreement with the real and predicted Cr(VI) removal percentage. It was concluded that Cr(VI) ion removal follows the first-order kinetic model by kinetic study of EC process.

Keywords

Cr(VI) removal
Electrocoagulation
Copper electrode
Kinetics
Optimization
Response surface methodology
1

1 Introduction

Electroplating industry, leather industry, mineral processing, metallurgical operations, mining industries, paints, pigments, glass manufacturing etc. are notable for producing waste water containing chromium as Cr(III) and Cr(VI), which are commonly utilized in different modern procedures. Cr(VI) is more dangerous than Cr(III) with the maximum daily allowable dosage of nearly 2.5 mg of chromium(VI) per kg of body weight. In humans, maximum concentrations are generally found in hilar lymph hubs and lungs, trailed by spleen, liver, and kidneys (WHO Guidelines for Drinking-Water Quality, 1996). There are several methods available for the removal of chromium from waste water such as adsorption (Aoudj et al., 2017a; Hashem et al., 2020), boisorption (Rezaei, 2016), membrane separation (Wang et al., 2019), ion exchange (Korak et al., 2017), coagulation or precipitation (Golbaz et al., 2014), photocatalysis (Deng et al., 2019), electrocoagulation (Aoudj et al., 2015; Cheballah et al., 2015; El-Taweel et al., 2015; Mahmad et al., 2016; Oden and Sari-Erkan, 2018; Zongo et al., 2009), etc. All these techniques have their own set of advantages and limitations. For example, the advantage of adsorption is the rapid removal of Cr (VI) from water. But high adsorbent requirement, generation of secondary pollutant and less efficiency are its limitations (Deng et al., 2019). Biological process takes longer times for treatment and does not work efficiently. Photocatalysis and membrane separation are expensive methods (Aoudj et al., 2017b). Among these techniques, electrocoagulation (EC) offers benefits like simplicity, ease of operation, elimination of secondary chemical requirement and minimal sludge production (Aoudj et al., 2017a; Nandi and Patel, 2017).

Electrocoagulation has been fruitfully used for the removal of heavy metals such as boron (Dolati et al., 2017), arsenic (Kobya et al., 2011), lead (Mansoorian, Mahvi, and Jafari, 2014), cadmium (Xu et al., 2017), zinc (Chen et al., 2018), nickel (Beyazit, 2014; Oden, 2020), copper (Escobar et al., 2006; Oden, 2020), iron (Hashim et al., 2017), mercury (Nanseu-Njiki et al., 2009). At present, extensive research work has been carried out on electrocoagulation using iron, aluminum, zinc and other metal alloy (Ali Maitlo et al., 2019; Kim et al., 2020; Vasudevan et al., 2011) These electrodes consume more energy as compared to copper electrode. In current state, EC with copper electrode is a better option as compared to iron, aluminum, zinc and other metal alloy electrodes to remove heavy metal ions due to less power consumption (Prajapati et al., 2016). So EC with copper electrode is a better option to remove Cr(VI), due to less electrical energy consumption which is the main draw of the present work.

EC procedure utilizes a direct current (DC) power source interfacing metal terminals dipped in waste water which results in the disintegration of metal anodes into wastewater and produces metal hydroxides which act as an adsorbent.

The main reaction occurring at the anode is electro-dissolution:

(1)
M(s) → Mn+(aq) + ne−

Water electrolysis can happen at the cathode and anode. (Suárez-Escobar et al., 2016):

(2)
Oxidation at anode: 2H2O(l) → 4H+(aq) + O2(g) + 4e−
(3)
Cathode (reduction): 2H2O(l) + 2e− → H2(g) + 2OH−(aq)
(4)
Overall reaction: Mn+ + nOH− → M(OH)n(s)

Optimization of the electrocoagulation process using response surface methodology (RSM) has been used for optimization of various operating parameters (Bhatti et al., 2011; Güçlü, 2015; Kobya et al., 2013; Kumari et al., 2019; Xu et al., 2016). The performance of electrocoagulation depends on operating parameters such as concentration of metal ions, pH, current density, electrolysis time, electrode distance and supporting electrolyte. So, enormous number of experiments are needed and hence, statistical technique like response surface methodology was utilized for electrocoagulation optimization of Cr(VI) ions removal from waste water. Literature suggests that electrocoagulation using aluminum & iron electrodes have been used so far for removal of Cr(VI) ions from waste water (Khan et al., 2019; Mahmad et al., 2016). However, to the best of our knowledge, Cr(VI) ions removal from aqueous solution by electrocoagulation process using copper electrode has not been reported in literature. Subsequently, utilizing copper cathode in electrocoagulation process can be seen as latest technique and a potential research area for Cr(VI) ions removal from waste water.

The present work aims to study the effect of Cr(VI) concentration (C0), pH, electrode distance (ED), current density (CD) and supporting electrolyte (NaCl) concentration (Cs) and optimization of parameters using response surface methodology in combination with the central composite design for the removal of Cr(VI). Power consumption was calculated for EC process along with analysis of sludge produced by EC process. Additionally, kinetic analysis has been performed to check the effect of different process variables on EC process.

2

2 Materials and methods

2.1

2.1 Experimental setup and procedure

All experiments were performed in batch mode using 500 ml volume of artificial solution at room temperature (25 ± 2 °C) as shown in Fig. 1. Copper plate (15 cm × 4.6 cm × 0.1 cm) purchased from the market (Ahmedabad, Gujarat, India) was utilized as an electrode with an useful area of 72.6 cm2 (0.00726 m2). Both electrodes were made of copper. The anode and cathode were linked with a DC power supply (Aplab, India, Model: L-1285). An artificial water solution containing Cr(VI) ion was set up by dissolving required quantity of Potassium dichromate (K2Cr2O7) (Nice Chemicals Pvt. Ltd, India, 99%) in distilled water. pH was balanced by utilizing H2SO4 or NaOH. NaCl was used for maintaining the conductivity of solution. Summary of different process variables studied for the Cr(VI) removal are shown in Table 1. All the experiments were performed two times and the experimental error was around 3%. Cr (VI) removal efficiency was computed using the following Eq. (5):

(5)
C r V I r e m o v a l % = C 0 - C t C 0 × 100 where C0 is the initial Cr(VI) concentration (mg/L), Ct is the concentration of Cr(VI) at any time t.
Electrocoagulation cell.
Fig. 1
Electrocoagulation cell.
Table 1 Different process variables studied for the Cr(VI) removal.
Effect of variable studied Deviation in variable Constant variable
pH 2, 4, 6, 8, 10, 12 Cr(VI) concentration = 100 mg/L, Current = 0.3 A, ED = 1.4 cm, conductivity = 0.21 ms
Cr(VI) concentration(mg/l) 50, 75, 100, 125, 150 Current = 0.3 A, ED = 1.4 cm, pH = 5.65, conductivity = 0.21 ms
Current(A) 0.1, 0.15, 0.20, 0.25, 0.30 Cr(VI) concentration = 100 mg/L, Current = 0.3 A, ED = 1.4 cm. pH = 5.65, conductivity = 0.21 ms
Dose of electrolyte (NaCl) (g/l) 0.2, 0.4, 0.6, 0.8, 1.0 Cr(VI) concentration = 100 mg/L, Current = 0.3 A, pH = 5.65, ED = 1.4 cm.
Inter electrode distance (ED) (cm) 0.7, 1.4, 2.1, 2.8, 3.5 Cr(VI) concentration = 100 mg/L, Current = 0.3 A, pH = 5.65, conductivity = 0.21 ms
Time of operation (minute) 40

pH and conductivity were measured by digital pH meter (Century, India, CP901) and conductivity meter (Chemiline, India, CL220) respectively. Concentration of Cr(VI) ions was decided colorimetrically (Standard Methods, method no.3500-Cr D) (Bhatti et al., 2009). 1, 5-Diphenylcarbazide (molecular weight 242.28 g/mol) was utilized as complexometric reagent for Cr(VI) estimation at 540 nm using double beam UV–visible spectrophotometer (Make: Shimadzu Corporation Kyoto, apan, Model: UV-1800 240 V).

Kinetic study was carried out for EC process available in the literature (Das and Nandi, 2019; Nandi and Patel, 2013b, 2017). The rate of change of Cr (VI) ion concentration can be expressed as a first order kinetic model according to Eqs. (6)–(8)

(6)
dC dt = - k C
(7)
∫ C 0 C d C C = - k ∫ 0 t d t
(8)
l n C 0 C = k t where k represent rate constant (minute−1), C is Cr(VI) concentration at any time ‘t’, C0 is initial Cr(VI) concentration. The specific electrical energy consumption (SEEC) was estimated in kWh/ (kg Cu) and was calculated using the following Eqs. (9)–(11) (Das and Nandi, 2019; Nandi and Patel, 2017).
(9)
φ = Δ M exp e r i m e n t a l Δ M theoritical × 100
(10)
Δ M theoritical = M · I · t EC n · F
(11)
SEEC = n × F × U 3600 × M × φ where φ indicates the current efficiency, F indicates Faraday constant (F = 96,487 C mol−1), n indicates the moles of electron, U indicates the voltage in volt, tEC indicates the time of EC (minute), I indicates the current intensity (A) and M is the molecular weight of copper (g mol−1).

2.2

2.2 Statistical design

The response surface methodology (RSM) in blend with central composite design (CCD) was utilized for optimizing process parameters of the electrocoagulation process for Cr(VI) removal efficiency. In this work, the impact of three important operational parameters viz: initial pH (X1), current density (X2) and the initial concentration of Cr(VI) ions on the removal efficiency were explored. Here, three variables have been introduced as RSM input factors where their experimental ranges in coded and real values are shown in Table 2.

Table 2 Real and coded values of autonomous factors utilized for design.
Process variables code Symbol Level
α = −1.68 −1 0 1 α = 1.68
Initial pH X1 2.64 4 6 8 9.36
Current density (A/m2) X2 4.41 13.77 27.54 41.32 50.68
initial concentration of Cr(VI) ions X3 58 75 100 125 142

Regression, graphical study, statistical investigation, and optimization of Cr(VI) removal were executed using Minitab 18 software. CCD with three variable consists of 20 experiments with eight cube points, six axial points (coded as −1.68 (−α) and +1.68 (+α)), and six center points in the cube (Nayak and Vyas, 2019). A total of 20 experiments were performed as presented in Table 3. The estimation of α for rotatability relies upon the quantity of points in the factorial portion of the design (Behbahani et al., 2011).

Table 3 Experimental percentage and predicted percentage of Cr(VI) removal efficiency.
Exp. no. pH code value (uncoded value) Current density (A/m2) value (uncoded value) Initial concentration of Cr(VI) (mg/L) value (uncoded value) Yexp (%) Ypre (%) Error %
X1 X2 X3
1 −1(4) 1(41.32) 1(125) 80.3 83.7 4.3
2 1(8) 1(41.32) 1(125) 78.2 78.6 0.6
3 0(6) 0(27.54) 0(100) 73.1 74.4 1.7
4 1(8) −1(13.77) −1(75) 58.1 54.7 5.8
5 −1.689(2.64) 0(27.54) 0(100) 75.4 73.3 2.7
6 0(6) 0(27.54) 0(100) 76.8 74.4 3.2
7 0(6) 0(27.54) −1.68(58) 68.0 69.3 1.9
8 0(6) 0(27.54) 0(100) 75.3 74.4 1.2
9 1(8) 1(41.32) −1(75) 78.2 78.6 0.6
10 0(6) 0(27.54) 1.68(142) 70.1 68.7 2.0
11 −1(4) 1(41.32) −1(75) 93.2 94.4 1.3
12 −1(4) −1(13.77) 1(125) 46.9 46.5 0.8
13 1(8) −1(13.77) 1(125) 65.8 64.7 1.7
14 0(6) 0(27.54) 0(100) 73.7 74.4 0.9
15 0(6) 1.68(50.0) 0(100) 92.3 89.1 3.5
16 0(6) −1.68(4.44) 0(100) 34.5 37.6 9.1
17 −1(4) −1(13.77) −1(75) 47.6 47.2 0.8
18 0(6) 0(27.54) 0(100) 72.8 74.4 2.1
19 0(6) 0(27.54) 0(100) 74.9 74.4 0.7
20 1.68(9.36) 0(27.54) 0(100) 73.0 75.4 3.3

3

3 Results and discussions

3.1

3.1 Development of electrocoagulation models

The response of the experimental results were fitted to linear, linear and square, two-factor interaction and quadratic polynomial model using CCD. It was seen that the quadratic model was the best fit because of high F value, lower P-value, and high R2 as shown in Table 4.

Table 4 CCD response of models for finest fit with experimental results.
Response Model Sum of squares Degree freedom (DF) Mean Square F-value P-value R2
Cr(VI) removal % Linear 3194.94 3 1064.98 24.19 0.000 81.94
Linear + Square 3450.82 6 575.14 16.67 0.000 88.50
Linear + Interactio 3571.43 6 595.24 23.60 0.000 91.59
Quadratic 3827.31 9 425.26 59.11 0.000 98.15

Response of Cr(VI) removal, Y evaluated by following Eq. (12)

(12)
Cr ( V I ) r e m o v a l ( Y ) = 74.43 + 0.605 X 1 + 15.282 X 2 - 0.173 X 3 - 0.084 X 1 2 - 3.903 X 2 2 - 1.905 X 3 2 - 5.812 X 1 ∗ X 2 + 2.663 X 1 ∗ X 3 - 2.488 X 2 ∗ X 3

It was concluded from Eq. (12) and Table 3 that the Cr(VI) removal percentage from simulated waste water can be fitted well with the developed quadratic model. In Eq. (12) the terms with negative sign shows antagonistic effect, while the terms with positive sign show the synergistic impact on the Cr(VI) removal efficiency. It can be seen from analysis of variance of Table 5, that quadratic model is fitted well with the experimental values due to its p- value less than 0.0001. The F-value of 59.11 indicates that the CCD test confirmed with applied quadratic model. The lack of fit with p-value 0.047, that is greater than 0.001, suggests that lack of fit is not significant (Nayak and Vyas, 2019). The terms X2, X22, X1X2, X1X3, X2X3 and X32 having p-values <0.05 show that the model were significant (Zhu et al., 2016). The CV value of 3.80% indicates a high precision and a good agreement of reliability with the experimental values. In addition R2, R2adj and R2pred having values of 0.9815, 0.9646 and 0.8770, respectively, indicated that the predicted model and experimental values are in good agreement (Ahmadzadeh et al., 2017).

Table 5 Estimated regression coefficients and model summary statistics for percentage of Cr(VI) removal.
Source Coefficient Coefficient
p-value		 SS DF MS F-value P-value
Model 3827.31 9 425.26 59.11 0.0001
b0(74.43) 0.000
pH, X1 b1(0.605) 0.424 5.00 1 1.65 0.19 0.670
Current density, X2 b2(15.282) 0.000 3189.52 1 3188.41 372.81 0.0001
Cr(VI) concentration, X3 b3(−0.173) 0.816 0.41 1 14.62 1.71 0.220
X12 b11(−0.084) 0.907 0.10 1 0.24 0.03 0.869
X22 b22(−3.903) 0.000 219.51 1 241.09 28.19 0.0001
X32 b33(−1.905) 0.022 52.31 1 54.87 6.42 0.030
X1X2 b12(−5.812) 0.000 270.28 1 274.95 32.15 0.0001
X1X3 b13(2.66) 0.019 56.71 1 69.03 8.07 0.018
X2X3 b23(−2.488) 0.025 49.50 1 51.51 6.02 0.034
Residual – – 71.95 10 7.19 – –
Lack of fit – – 60.39 5 12.08 5.23 0.047
Pure-error – – 11.55 5 2.31 – –
Total – – 3899.26 19 – – –
Std. Dev. – – 2.68231 R2 – – – 98.15
Mean – – 70.41 R2adj – – – 96.49
C.V. – – 3.80 R2pred – – – 87.70

Fig. 2a presents the real Cr(VI) removal percentage vs. predicted Cr(VI) removal percentage. For high-quality agreement with actual value, the predicted Cr(VI) removal percentage should lie near the Y = X line. Fig. 2b shows the probability plot of the residuals and it shows a reasonably straight line. Fig. 2c shows the goodness of model fit and suggests the minimum value of residual for predicted data. It was seen that the majority of the standard residuals are within the interval of ±5.00. The residuals above said limit cause significant or a potential error in model (Nayak and Vyas, 2019).

Predicted Vs. actual Cr(VI) removal %.
Fig. 2a
Predicted Vs. actual Cr(VI) removal %.
Normal probability plot of residual.
Fig. 2b
Normal probability plot of residual.
Residual Vs. fits plot.
Fig. 2c
Residual Vs. fits plot.

Figs. 3a and 3b show the interaction impact of pH and current density in the form of surface and contour plot, respectively. It can be shown from surface and contour plot that initially, Cr(VI) removal percentage enhanced with the increase in current density and decrease in pH. It was observed that Cr(VI) removal percentage increased in acidic pH and reduced in basic pH. The reason can be explained by two main reactions which take place under strong acidic conditions. (i) H2 ↑ gas is evolved at copper interface dissolving Cu2+ in solution as per Eq. (18). (ii) Reduction of Cr(VI) by Cu0 is also accelerated under acidic condition as per Eq. (20) (Cheballah et al., 2015; Prajapati et al., 2016; Prasad et al., 2011). The copper hydroxide (Eq. (4)) and Chromium hydroxide (Eq. (21)) led to the final removal of Cr(VI) from the solution (Díaz et al., 2015; Sahu et al., 2014).

Surface plot: Removal %(Y) Vs pH (X1) and Current density (X2).
Fig. 3a
Surface plot: Removal %(Y) Vs pH (X1) and Current density (X2).
Contour plot: Removal %(Y) Vs pH (X1) and Current density (X2).
Fig. 3b
Contour plot: Removal %(Y) Vs pH (X1) and Current density (X2).

Figs. 3c and 3d show the interaction impact of concentration of Cr(VI) ions and current density in the form of surface and contour plot, respectively. It was observed from surface and contour plot that, Cr(VI) removal percentage increases with the increase in current density at lower concentration and Cr(VI) removal percentage decreases with the increase in concentration at constant current density. It was due to increase in the concentration of Cr(VI) at constant current density, which reduced the adsorption capacity of the Cu(OH)n rapidly. This leads to reduce the percentage removal of Cr(VI) (Zewail and Yousef, 2014). The explanation for decreasing removal percentage of Cr(VI) with the increase in its concentration is deducible from Faraday’s law. According to Faraday’s law (Eq. (13)), at constant current density, constant rate of Cu2+ is generated to the solution. So the Cu2+ generated at higher Cr(VI) concentrations are not enough to absorb all Cr(VI) ions from solution (El-Taweel et al., 2015).

Surface plot: Removal %(Y) Vs concentration (X3) and Current density (X2).
Fig. 3c
Surface plot: Removal %(Y) Vs concentration (X3) and Current density (X2).
Contour plot: Removal %(Y) Vs concentration (X3) and Current density (X2).
Fig. 3d
Contour plot: Removal %(Y) Vs concentration (X3) and Current density (X2).

3.2

3.2 Effect of current density on Cr(VI) removal

For the electrocoagulation process, the current density (CD) is a significant factor for controlling the reaction rate of the process (Hamdan and El-Naas, 2014). Fig. 4a shows that the initial concentration decreases from 100 mg/L to 6.64 mg/L (99.33% Cr(VI) removal) at CD of 41.32 A/m2 after 40 min of EC. It was observed that the optimum time was 40 min for EC treatment, as after 50 and 60 min there was not much change in Cr(VI) removal efficiency. It was observed that with increasing the current density from 13.77 to 41.32 A/m2, the Cr(VI) removal efficiency increased from 49.48% to 93.33%. The increases in removal percentage with increasing current density was due to the higher amounts of production of coagulant (Cu2+ ions) at higher CD which removes more quantity of Cr(VI) ions from the waste water (Hamdan and El-Naas, 2014). This is also explained by Faraday's law (Eq. (13)) which gives a correlation between current density and the quantity of electrode (anode) that dissolves in the electrocoagulation cell (Prajapati et al., 2016; Sahu et al., 2014).

(13)
m = M ( C D ) t nF where m represents the theoretical quantity of ion provided per unit area, F is the Faraday’s constant (96,487 C/mol), CD denotes current density, t represents time, n represents the number of electrons (for Cu, n = 2), and M represents the molecular weight of copper electrode, (for Cu, M = 63.54 g/mol). As the CD increases, the numbers of Cu2+ ions increase because m is directly proportional to CD. Therefore, at higher current density, the formation of coagulant (Cu2+ ions) increases. It resulted in to higher amount of Cr(VI) ions removal from the waste water (Aber et al., 2009).
(a) Effect of CD on on Cr(VI) ion removal with time. (b) Determination of the kinetic constants for different CD.
Fig. 4
(a) Effect of CD on on Cr(VI) ion removal with time. (b) Determination of the kinetic constants for different CD.

Kinetic study was carried out for EC process. It was observed that the better fitting was for 1st order model expressed in Eq. (6). Fig. 4b shows the plot of ln(C0/C) vs time for first order kinetic which is straight line for different CD. It was observed that CD values of 13.77, 20.66, 27.54, 34.43 and 41.32 A/m2 led to increase in rate of Cr(VI) ion removal (k) of 0.0176, 0.0215, 0.0342, 0.0392 and 0.0685 min−1 respectively. It can be seen from Fig. 4b that the values of k increase by increasing the CD. The reason was the presence of more quantity of Cu2+ ions in the waste water as explained previous. Eq. (14) was found to fit for rate constant k with CD.

(14)
k = 2.32 × 10 - 4 × C D A m 2 R 2 = 0.890

3.3

3.3 Effect of initial Cr(VI) concentration

Fig. 5a. shows the impact of concentration of Cr(VI) ions on its removal at different concentrations (50, 75, 100, 125 and 150 mg/L). It was observed from Fig. 5a that the concentration of Cr(VI) decreases with time. As the initial Cr(VI) ion concentration increased from 50 to 150 mg/L, the removal efficiency decreased from 94.88% to 67.06%. Corresponding residual concentration increased from 2.04 to 49.81 mg/L. It was observed that, as the initial concentration of Cr(VI) increased, the removal efficiency decreased at constant current density. It was because of the equal amount of copper hydroxide complexes produced at constant current density and time. Therefore, the flocs generated at higher Cr(VI) ion concentration were inadequate to adsorb Cr(VI) ion from waste water (Zewail and Yousef, 2014). As discussed earlier, it is also explained by the Faraday’s law (Eq. (13)) that at constant current density, constant rate of Cu2+ is generated to the solution. So, the Cu2+ generated at higher Cr(VI) concentrations are not enough to absorb all Cr(VI) ions from solution (El-Taweel et al., 2015). From Fig. 5b it is seen that the concentration of Cr(VI) ion increases with decreasing rate of Cr(VI) ion removal (k). The reason was that the presence of lesser amounts of Cu2+ ions in the waste water as compared to Cr(VI) ion. Overall, the decrease in k with concentration of Cr(VI) was found to fit the following equation (15):

(15)
k = - 5.14 × 10 - 4 × C 0 m g L + 0.109 R 2 = 0.868
(a) Effect of concentration on Cr(VI) ion removal with time. (b) determination of the kinetic constants for different Concentration.
Fig. 5
(a) Effect of concentration on Cr(VI) ion removal with time. (b) determination of the kinetic constants for different Concentration.

3.4

3.4 Effect of electrode distance on Cr(VI) removal

Fig. 6a shows the reduction of Cr(VI) ion concentration at different electrode distance (ED). The electrode surface area and distance between electrodes is very significant for EC process. The voltage drop (ηIR) is given by Eq. (16) (Nandi and Patel, 2017):

(16)
η IR = I . d A . k where I = Current (A), d = distance between two electrode (m), A = Active surface area of electrode (m2), k = Specific conductivity (103 m·S/m). It was concluded from above Eq. (16) that at constant conductivity of the solution and surface area of electrode. The voltage drop increases with the increase of electrode distance. It resulted in to higher resistance between the two electrodes thus the electrical current reduces. As the electrical current reduces, the removal percentage of Cr(VI) decreases. It was observed from Fig. 6a, the removal of Cr(VI) ion increases with decreasing in electrode distance. The removal percentage decreased from 95.21% to 76.88%, when the electrode distance increased from 0.7 to 3.5 cm. As the ED increased, Cr(VI) ion removal percentage decreased because of interactions among Cu2+ ions and OH− ions produced from electrodes get delayed. Therefore rate of flocs generation decreases and interaction among these flocs and Cr(VI) ions decreases. So rate of Cr(VI) ions removal decreases (Das and Nandi, 2019). The linear plot of ln(C0/C) versus time for various ED demonstrated in Fig. 6b. The values of k for various ED were 0.0732, 0.0685, 0.0532, 0.0488 and 0.0381 min−1 for ED of 0.7, 1.4, 2.1, 2.8 and 3.5 cm, respectively. The increase in ED with decreasing in k was due to the lesser rate production of OH− at higher ED. The rate equation with ED as following Eq. (17):
(17)
k = - 0.0128 × E D c m + 0.083 R 2 = 0.962
(a) Impact of electrode distance (ED) on Cr(VI) ion removal with time. (b) Calculation of the rate constants for different electrode distance (ED).
Fig. 6
(a) Impact of electrode distance (ED) on Cr(VI) ion removal with time. (b) Calculation of the rate constants for different electrode distance (ED).

3.5

3.5 Effect of NaCl concentration on Cr(VI) removal and SEEC

Supporting electrolyte (NaCl) was used to enhance the conductivity of the solution. The conductivity of the solution plays the major role in the rate of removal and power consumption. Power consumption analysis plays an important role for industrial application of EC (Oden and Sari-Erkan, 2018). Experiments were carried out using various NaCl concentrations (Cs) and the results are shown in Fig. 7a. As the NaCl concentration increased from 0.2 g L−1 to 1.0 g L−1, the removal percentage increased from 67.94% to 84.29%. It can be seen that with increase in the supporting electrolyte concentration, the Cr(VI) ion removal efficiency increases. This was due to reduction in resistance among the electrodes, which reduced the voltage drop at constant current density. Furthermore, reduction in the voltage drop helps to reduce the energy consumption in EC process (Zewail and Yousef, 2014). Fig. 7b shows the variation of SEEC with various ED and current density after 40 min of EC. It was observed from Fig. 7b that, electrode distance increased from 0.7 cm to 3.5 cm. SEEC increased from 12.90 kWh/kg Cu to 63.20 kWh/kg Cu at CD 41.32 A/m2. Table 6 shows SEEC at various ED and current after 40 min of EC.

(a) Effect of NaCl doses on removal of Cr(VI) ion removal efficiency. (b) Deviation in Cr(VI) ion removal percentage and SEEC with various CD and ED after 40 min of EC.
Fig. 7
(a) Effect of NaCl doses on removal of Cr(VI) ion removal efficiency. (b) Deviation in Cr(VI) ion removal percentage and SEEC with various CD and ED after 40 min of EC.
Table 6 Variation of SEEC with various ED and current.
SEEC (kWh/kg Cu) Removal efficiency of Cr ions %
Current
ED 0.30 A 0.20A 0.10A ED 0.30 A 0.20A 0.10A
0.7 12.90 9.13 3.81 0.7 95.21 78.0 55.0
1.4 20.00 15.95 8.40 1.4 93.33 75.09 49.49
2.1 34.34 24.24 11.79 2.1 88.51 71.00 44.50
2.8 49.48 32.09 13.89 2.8 84.93 69.00 41.20
3.5 63.20 41.25 16.97 3.5 76.88 61.00 30.80

Different researchers have inspected the removal of Chromium by EC technique. But, only some researchers have studied the energy consumption of the EC process. Table 7 shows the comparison of the SEEC of chromium removal by EC with various electrodes along with the current work.

Table 7 Comparison of Chromium ion removal from aqueous solutions by other researcher with the current work.
Sr. No. Electrode material Type of waste water Current density, Current or Voltage Removal Efficiency SEEC (KgWh/ kg Cr) Reference
1 Fe and Al electrode Aqueous solutions 200 A/m2 100% 22.07 with Fe and 59.34 with Al Cheballah et al. (2015)
2 Fe electrode Aqueous solutions 1 A 56.3% 0.02 El-Taweel et al. (2015)
3 Fe electrode Aqueous solutions 10.02 A/m2 94.97% 16.14 Zewail and Yousef (2014)
4 Al electrode Aqueous solutions 9.14 V 91% NA Zaroual et al. (2009)
5 Cu electrode Aqueous solutions 41.32 A/m2 95.21 12.90 Present study

3.6

3.6 Effect of pH on Cr(VI) removal

Many a times, it has been reported in the literature that the pH is most important parameter for electrocoagulation process. Experiments were performed using different initial pH and the results are shown in Fig. 8a. It was seen that as the pH increased from 2 to 12, the Cr removal efficiency decreased from 94.69 to 20.43%. It was observed that the removal of Cr (VI) was higher at lower pH (El-Taweel et al., 2015). The removal percentage of Cr (VI) at pH values equal to 2, 4 and 6, respectively were, 94.69%, 93.48% and 93.20%. It was seen that Cr(VI) removal percentage increased in acidic pH and decreased in basic pH. As discussed earlier, the reason can be explained by two main reactions taking place under strong acidic conditions. (i) H2 ↑ gas is evolved at copper interface dissolving Cu2+ in solution as Eq. (18). (ii) Reduction of Cr(VI) by Cu0 is also accelerated under acidic condition, Eq. (20) (Cheballah et al., 2015; Prajapati et al., 2016; Prasad et al., 2011).

(18)
2Cu + 4H+ → 2Cu2+ + 2H2
(19)
At cathode 4H2O(l) + 4e− → 2H2(g) + 4OH−(aq)
(20)
In solution 2HCrO4 + 3Cu0 + 14H+ → 2Cr3+(aq) + 3Cu2+(aq) + 8H2O
Effect of pH on removal of Cr(VI) ion removal efficiency.
Fig. 8a
Effect of pH on removal of Cr(VI) ion removal efficiency.

Followed by precipitation:

(21)
Cr3+ + 3OH− → Cr(OH)3

The Cu2+ and OH− ions are produced at the electrodes and react to form monomeric and polymeric species: Cu(OH)2+, CuOH2+, Cu2(OH)24+, Cu(OH)4−, Cu(H2O)2+, Cu(H2O)5OH2+, Cu(H2O)4(OH)2+, etc. which are finally transformed into copper hydroxide. This copper hydroxide and Chromium hydroxide (Eq. (19)) led to the final removal of Cr(VI) from the solution (Díaz et al., 2015; Sahu, Mazumdar, and Chaudhari, 2014). It was additionally seen that the pH of the solution changes throughout the electrocoagulation process and attained basic pH. This is because of the creation of metal hydroxides during the process (Aoudj et al., 2015). Chemical species diagram for Cu in aqueous solution as a function of pH is shown in Fig. 8b (Díaz et al., 2015).

Chemical species diagram for Cu in aqueous solution as a function of pH (Díaz et al., 2015).
Fig. 8b
Chemical species diagram for Cu in aqueous solution as a function of pH (Díaz et al., 2015).

3.7

3.7 Characterization of EC produced sludge

The elemental analysis of the sludge produced after electrocoagulation of Cr(VI) ions was done by FESEM- EDX. SEM analysis of flocs was done to examine the structural characteristics of the flocs produced after electrocoagulation. Fig. 9a demonstrates the FESEM image of the flocs and it can be seen that flocs are porous and homogeneous in structure. Homogeneous structure may indicate that the generated Cu(OH)2 flocs are coagulated well during EC process. Fig. 9b illustrates the EDX spectrum obtained from FESEM-EDX analysis and it shows that chromium, carbon, copper and oxygen are the elements present in the sludge. Presence of chromium ions confirms capture of chromium by copper flocks.

SEM image of EC produce sludge.
Fig. 9a
SEM image of EC produce sludge.
EDX spectrum of EC produced sludge.
Fig. 9b
EDX spectrum of EC produced sludge.

4

4 Conclusions

In this work, the experimental investigation of electrocoagulation to optimize various process parameters for efficient removal of Cr (VI) ions were studied by response surface methodology using central composite design. From experimental results it can be concluded that, Cr (VI) ion concentration value of 100 mg/L, CD value of 41.32 A/m2, pH value of 5.65 with ED of 1.4 cm and EC time of 40 min are ideal operating parameters of EC to achieve 93.33% removal of Cr (VI) ions from waste water. The experimental results shows that the impact of pH in the range 2–7 was insignificant on Cr(VI) removal efficiency and it favors acidic condition. It can be also concluded that at lower concentration of Cr (VI) ion, higher removal percentage is obtained at constant current density and time of electrocoagulation. Kinetic analysis was performed of EC process and it revealed that Cr (VI) ion removal follows a first-order kinetic model with respect to different operating parameters. The CD increased from 13.77 A/m2 to 41.32 A/m2 when the rate constant (k) was increased from 0.0176 min to 1 to 0.0685 min−1. EDX analysis of the sludge confirmed the capture of Cr ions from water by the copper hydroxide flocs. The polynomial model was developed with R2 = 0.981, which indicated high accuracy among observed and predicted results. The findings revealed that current density has a major influence on efficient removal of Cr (VI) ions.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

References

  1. , , , . Removal of Cr(VI) from polluted solutions by electrocoagulation: modeling of experimental results using artificial neural network. J. Hazard. Mater.. 2009;171(1–3):484-490.
    [Google Scholar]
  2. , . Removal of ciprofloxacin from hospital wastewater using electrocoagulation technique by aluminum electrode: optimization and modelling through response surface methodology. Process Saf. Environ. Prot.. 2017;109:538-547.
    [CrossRef] [Google Scholar]
  3. , , , , . Removal mechanism for chromium (VI) in groundwater with cost-effective iron-air fuel cell electrocoagulation. Sep. Purif. Technol.. 2019;213:378-388.
    [CrossRef] [Google Scholar]
  4. , . Simultaneous removal of chromium(VI) and fluoride by electrocoagulation-electroflotation: application of a hybrid Fe-Al anode. Chem. Eng. J.. 2015;267(Vi):153-162.
    [CrossRef] [Google Scholar]
  5. , . Kinetics and adsorption isotherm for the removal of fluoride and chromium (VI) from wastewater by electrocoagulation. Desalin. Water Treat.. 2017;82:262-270.
    [Google Scholar]
  6. , , , . Removal of fluoride, SDS, ammonia and turbidity from semiconductor wastewater by combined electrocoagulation–electroflotation. Chemosphere. 2017;180:379-387.
    [CrossRef] [Google Scholar]
  7. , , , . The use of Al, Cu, and Fe in an integrated electrocoagulation-ozonation process. J. Chem.. 2015;2015(1):1-7.
    [Google Scholar]
  8. , , , . Techno-economical evaluation of fluoride removal by electrocoagulation process: optimization through response surface methodology. Desalination. 2011;271(1–3):209-218.
    [CrossRef] [Google Scholar]
  9. , . Copper(II), Chromium(VI) and Nickel(II) removal from metal plating effluent by electrocoagulation. Int. J. Electrochem. Sci.. 2014;9(8):4315-4330.
    [Google Scholar]
  10. , . RSM and ANN modeling for electrocoagulation of copper from simulated wastewater: multi objective optimization using genetic algorithm approach. Desalination. 2011;274(1–3):74-80.
    [CrossRef] [Google Scholar]
  11. , , , . Electrocoagulation removal of Cr(VI) from simulated wastewater using response surface methodology. J. Hazard. Mater.. 2009;172(2–3):839-846.
    [Google Scholar]
  12. , . Simultaneous removal of hexavalent chromium and COD from industrial wastewater by bipolar electrocoagulation. Chem. Eng. Process. Process Intensif.. 2015;96:94-99.
    [CrossRef] [Google Scholar]
  13. , . Zinc removal from model wastewater by electrocoagulation: processing, kinetics and mechanism. Chem. Eng. J.. 2018;349:358-367.
    [CrossRef] [Google Scholar]
  14. , , . Removal of Fe (II) Ions from drinking water using electrocoagulation (EC) process: parametric optimization and kinetic study. J. Environ. Chem. Eng.. 2019;7(3):103116.
    [CrossRef] [Google Scholar]
  15. , . Polyaniline-TiO2 composite photocatalysts for light-driven hexavalent chromium ions reduction. Science Bulletin 2019
    [CrossRef] [Google Scholar]
  16. , , , , . Boron removal from aqueous solutions by electrocoagulation at low concentrations. J. Environ. Chem. Eng.. 2017;5(5):5150-5156.
    [CrossRef] [Google Scholar]
  17. , , , , . Removal of Cr(VI) ions from waste water by electrocoagulation using iron electrode. Egypt. J. Pet.. 2015;24(2):183-192.
    [CrossRef] [Google Scholar]
  18. , , , . Optimization of the electrocoagulation process for the removal of copper, lead and cadmium in natural waters and simulated wastewater. J. Environ. Manage.. 2006;81(4):384-391.
    [Google Scholar]
  19. , , , , . Separate and simultaneous removal of phenol, chromium, and cyanide from aqueous solution by coagulation/precipitation: mechanisms and theory. Chem. Eng. J.. 2014;253:251-257.
    [CrossRef] [Google Scholar]
  20. , . Optimization of electrocoagulation of pistachio processing wastewaters using the response surface methodology. Desalin. Water Treat.. 2015;54(12):3338-3347.
    [Google Scholar]
  21. , , . Characterization of the removal of chromium(VI) from groundwater by electrocoagulation. J. Ind. Eng. Chem.. 2014;20(5):2775-2781.
    [CrossRef] [Google Scholar]
  22. , . Water hyacinth biochar for trivalent chromium adsorption from tannery wastewater. Environ. Sustain. Indicat. 2020:100022.
    [CrossRef] [Google Scholar]
  23. , . Iron removal, energy consumption and operating cost of electrocoagulation of drinking water using a new flow column reactor. J. Environ. Manage.. 2017;189:98-108.
    [Google Scholar]
  24. , . Hexavalent chromium removal in an electrocoagulation column reactor: process optimization using CCD, adsorption kinetics and PH modulated sludge formation. Process Saf. Environ. Prot. 2019:118-130.
    [CrossRef] [Google Scholar]
  25. , , , . Removal mechanism of heavy metal (Cu, Ni, Zn, and Cr) in the presence of cyanide during electrocoagulation using Fe and Al electrodes. J. Water Process Eng.. 2020;33(July 2019):101109.
    [CrossRef] [Google Scholar]
  26. , . Removal of arsenic from drinking water by the electrocoagulation using Fe and Al electrodes. Electrochim. Acta. 2011;56(14):5060-5070.
    [CrossRef] [Google Scholar]
  27. , . Optimization of arsenic removal from drinking water by electrocoagulation batch process using response surface methodology. Desalin. Water Treat.. 2013;51(34–36):6676-6687.
    [Google Scholar]
  28. , , , . Regeneration of pilot-scale ion exchange columns for hexavalent chromium removal. Water Res. 2017
    [CrossRef] [Google Scholar]
  29. , , , . Defluoridation of synthetic and industrial wastewater by using acidic activated alumina adsorbent: characterization and optimization by response surface methodology. J. Environ. Sci. Health – Part A Toxic/Hazard. Substan. Environ. Eng.. 2019;54(1):79-88.
    [CrossRef] [Google Scholar]
  30. , , , , , . Electrocoagulation process by using aluminium and stainless steel electrodes to treat total chromium, colour and turbidity. Procedia Chem.. 2016;19:681-686.
    [Google Scholar]
  31. , , , . Removal of lead and zinc from battery industry wastewater using electrocoagulation process: influence of direct and alternating current by using iron and stainless steel rod electrodes. Sep. Purif. Technol.. 2014;135:165-175.
    [CrossRef] [Google Scholar]
  32. , , . Effects of operational parameters on the removal of brilliant green dye from aqueous solutions by electrocoagulation. Arab. J. Chem.. 2017;10(7):S2961-S2968.
    [CrossRef] [Google Scholar]
  33. , , . Removal of Pararosaniline Hydrochloride Dye (Basic red 9) from aqueous solution by Electrocoagulation: Experimental, Kinetic, and modelling. J. Dispers. Sci. Technol. 2013:37-41.
    [CrossRef] [Google Scholar]
  34. , , . Effects of Operational parameters on the removal of brilliant green dye from aqueous solutions by electrocoagulation. Arab. J. Chem.. 2017;10:S2961-S2968.
    [CrossRef] [Google Scholar]
  35. , . Mercury(II) removal from water by electrocoagulation using aluminium and iron electrodes. J. Hazard. Mater.. 2009;168(2–3):1430-1436.
    [Google Scholar]
  36. , , . Optimization of microwave-assisted biodiesel production from papaya oil using response surface methodology. Renew. Energy 2019:18-28.
    [CrossRef] [Google Scholar]
  37. , . Treatment of CNC industry wastewater by electrocoagulation technology: an application through response surface methodology. Int. J. Environ. Anal. Chem.. 2020;100(1):1-19.
    [CrossRef] [Google Scholar]
  38. , , . Treatment of metal plating wastewater using iron electrode by electrocoagulation process: optimization and process performance. Process Saf. Environ. Prot.. 2018;119:207-217.
    [CrossRef] [Google Scholar]
  39. , . Electrocoagulation treatment of rice grain based distillery effluent using copper electrode. J. Water Process Eng.. 2016;11:1-7.
    [CrossRef] [Google Scholar]
  40. , , , . Reduction of Cr(VI) to Cr(III) and removal of total chromium from wastewater using scrap iron in the form of zerovalent iron(ZVI): batch and column studies. Can. J. Chem. Eng.. 2011;89(6):1575-1582.
    [Google Scholar]
  41. Quality, Drinking-Water, 1996. Chromium in Drinking-Water Background Document for Development of WHO Guidelines for Drinking-Water Quality. 2.
  42. , . Biosorption of Chromium by Using Spirulina Sp. Arab. J. Chem.. 2016;9(6):846-853.
    [CrossRef] [Google Scholar]
  43. , , , . Treatment of wastewater by electrocoagulation: a review. Environ. Sci. Pollut. Res.. 2014;21(4):2397-2413.
    [Google Scholar]
  44. , , , . Electrocoagulation – Photocatalytic process for the treatment of lithographic wastewater. Optimization using response surface methodology (RSM) and kinetic study. Catal. Today. 2016;266:120-125.
    [CrossRef] [Google Scholar]
  45. , , , . Studies on the Al-Zn-In-alloy as anode material for the removal of chromium from drinking water in electrocoagulation process. Desalination. 2011;275(1–3):260-268.
    [CrossRef] [Google Scholar]
  46. , . Removal of hexavalent chromium in dual-chamber microbial fuel cells separated by different ion exchange membranes. J. Hazard. Mater. 2019:121459.
    [Google Scholar]
  47. , . Optimizing electrocoagulation process for the treatment of biodiesel wastewater using response surface methodology. Desalin. Water Treat.. 2016;57(5):1491-1496.
    [Google Scholar]
  48. , . Simultaneous removal of cadmium, zinc and manganese using electrocoagulation: influence of operating parameters and electrolyte nature. J. Environ. Manage.. 2017;204:394-403.
    [CrossRef] [Google Scholar]
  49. , . Optimizing the removal of trivalent chromium by electrocoagulation using experimental design. Chem. Eng. J.. 2009;148(2–3):488-495.
    [Google Scholar]
  50. , , . Chromium ions (Cr6+& Cr3+) removal from synthetic wastewater by electrocoagulation using vertical expanded Fe anode. J. Electroanal. Chem.. 2014;735:123-128.
    [CrossRef] [Google Scholar]
  51. , . Using response surface methodology to evaluate electrocoagulation in the pretreatment of produced water from polymer-flooding well of Dagang oilfield with bipolar aluminum electrodes. Desalin. Water Treat.. 2016;57(33):15314-15325.
    [Google Scholar]
  52. , . Removal of hexavalent chromium from industrial wastewater by electrocoagulation: a comprehensive comparison of aluminium and iron electrodes. Sep. Purif. Technol.. 2009;66(1):159-166.
    [Google Scholar]

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