# Electrochemical Recovery to Overcome Direct Osmosis Concentrate-Bearing Lead: Optimization of Treatment Process via RSM-CCD

^{1}

^{2}

^{3}

^{4}

^{5}

^{*}

## Abstract

**:**

^{3}, 0.217 kg Al/m

^{3}, and 0.423 USD/m

^{3}, respectively. In addition, it was found that DO concentrate obtained from metallurgical wastewater can be recovered through PA-ECF (almost 94% Pb removal). This work demonstrated that the PA-ECF technique could became a viable process applicable in the treatment of DO concentrate containing Pb-rich for reuse.

## 1. Introduction

^{−1}according to the European Council Directive 98/83/EC

^{1}, [7]. Lead can affect the reproductive system and many organs of the human body, such as the liver and kidneys, and alter brain functions. On the other hand, prolonged exposure to Pb can cause induce sterility, abortion, and neonatal deaths. To address this issue, finding a suitable and cost-effective way to remove Pb (or reduce it to a legally acceptable level) from aquatic environments has become a global challenge [3,8,9].

## 2. Materials and Methods

#### 2.1. Direct Osmosis Concentrate Characteristics

#### 2.2. Perforated Aluminium–Electrocoagulation–Flotation (PA-ECF) Set Up

#### 2.3. Analytical Methods

_{0}− C

_{t})/C

_{0}) × 100

_{0}and C

_{t}are the initial and final Pb concentration, respectively (mg/L).

#### 2.4. Experimental Statistical Design

_{0}the linear constant, the second-order, and the constant coefficient, respectively. The regression constant, quadratic coefficient, and the interaction coefficient are β

_{i}, β

_{ii}, and β

_{ij}, respectively. x

_{i}and x

_{j}are the coded independent variables.

## 3. Results and Discussion

#### 3.1. Modeling and Statistical Analysis via Central Composite Design (CCD)

_{1}is Pb removal (%), and A, B are the electrolysis time and current intensity values, respectively. The negative sign reveals the antagonistic effects, while the positive sign reveals the synergistic effects. As shown in Table 4, analysis of variance (ANOVA) was used to attest to the adequacy of the model. The data illustrate a good fit between the quadratic model and the experimental data, with a relatively high R

^{2}of 0.96. The significant factors were ranked according to the F-value or p-value with 95% confidence level. The larger F-value 34.48 and the smaller ‘p’ value (<0.0001) indicate that the model is significant. The lack of fit F-test describes the deviation of experimental data around the model. The lack of fit would not be significant as long as the model fits in well with the data. Table 5 shows that the lack of fit for the obtained model for Pb removal efficiency was not statistically significant, indicating weak model noise over their signal. Moreover, the model terms are significant only when the values of “Prob. > F” are less than 0.05. In this respect, electrolysis time (A), the quadratic terms of electrolysis time (A

^{2}), and the quadratic terms of current intensity (B

^{2}) have significant effects (p < 0.05), and current intensity (B) has a very significant effect (p < 0.0001) on Pb removal efficiency.

#### 3.2. Importance of Influencing Parameters via Pareto Diagram Analysis

_{A}) of each factor divided by the total sum of squares (SS

_{T}) as stated in Equation (8). The significance of the applied current and treatment time is confirmed by the Pareto diagram illustrated in Figure 3 [46].

#### 3.3. Interaction of Time and Current on Pb Removal

^{3+}), which strongly depends on the current value [47]. As shown, higher current intensity and electrolysis time achieved higher Pb removal. In addition, in agreement with Table 3 and Table 4, the effect of current intensity on Pb removal is higher than electrolysis time. At the beginning of the PA-ECF process, the Pb removal diminished (blue region) due to a low pH of 1.5. However, the current study exhibited that the percentage of Pb removal is desirable (90%) even at low pH if the electrolysis time is long enough (30 min), strictly in line with the literature [3,4]. It could be remarked that the greater the value of current and time of treatment, the greater the rate of the Pb abatement. In the red region on the plots, it is considered that the Pb removal reached a peak (entirely 100%) when the current increased to 2 A at 85 min of electrolysis time and pH reached approximately 5.5, then remained virtually constant at current intensity higher than 2 A. It is apparent that Al (III) in the form of Al (OH)

_{3}(s) is the predominant species within pH range of 5.5–8.5 [48]. According to Faraday’s law (Equation (11)), the mass of the electrode dissolved in the PA-ECF cell is proportional to the applied current. As is evident, at a more elevated current, the amplified generation of gas bubbles and coagulants increased the removal of contaminants through sedimentation or flotation [42,49]. However, since the consumption of both energy and electrodes increases with increasing applied current, very high current intensities are not desirable. Niazmand et al. [44] demonstrated that the highest removal of total phenolic compounds was observed at the highest current and time. Ait Ouaissa et al. [50] investigated the efficiency of an electrocoagulation unit with Al alloy in the removal of Cr (VI). According to their results, almost 97% of Cr (VI) was removed at a current density of 40 A/m

^{2}and an initial pH of 3 to 6.

#### 3.4. Optimization Process

^{2}and 0.5 g/L of electrolyte. Yoosefian et al. [52] reported optimization of iron–electrocoagulation treatment for ciprofloxacin antibiotic removal by RSM and CCD. Under optimal conditions (pH 7.5, a reaction time of 20 min, 150 A/m

^{2}, and 1.5 cm interelectrode distance, the researchers achieved 99% CP removal. In the study of Genawi et al. [53] the researchers reported almost 100% electrocoagulation efficiency for the removal of Cr (VI) from tannery wastewater using RSM using 130 A/m

^{2}and pH 7.

^{2}). Furthermore, Ano et al. [54] analyzed nitrate removal by electrocoagulation using the Box–Behnken design under RSM. They found that under optimal conditions (1.80 A, 33 min, and pH 8.73), the nitrate removal was 73.8%.

#### 3.5. Cost-Effectiveness Estimation

_{AEC}+ β × E

_{CONS}

_{AEC}(kg/m

^{3}) and E

_{CONS}(kWh/m

^{3}) are electrode mass-consumed material and energy required to remove the target contaminant, respectively; the Energy Ministry and Markets of Iran announced the prices of 1 kWh of electricity (α) and electrode material (β) as 0.04 USD/kWh and 1.95 USD/kg of Al in 2020, respectively.

_{CONS}) can be estimated by Equation (10), while the cost of electrodes is calculated by Faraday’s law, which is influenced by time and current as Equation (11) [37]:

_{L}denote the voltage applied (V), current (A), electrolysis time (h), and working volume (m

^{3}), respectively. MV is the molecular mass of Al (26.98 g/mol), Z is the number of transferred electrons number (3), and F is the Faraday’s constant (96,487 C/mol). Therefore, according to Equations (10) and (11), both energy and electrode consumptions were calculated to evaluate the operating cost of the cell (Equation (9)).

^{3}. Under the optimal operating conditions, the necessary energy, the electrode mass, and the operating cost were equal to 0.0025 kWh/m

^{3}, 0.217 kg Al/m

^{3}, and 0.423 USD/m

^{3}, respectively. Gonder et al. [56] estimated the total operating cost equal to 0.3 USD/m

^{3}using electrocoagulation-Al electrodes to remove organics, oil-grease, and chloride under optimum conditions (pH 6, 10 A/m

^{2}, and 30 min). Bakshi et al. [57] applied electrocoagulation for phosphate removal, and the calculated operating cost was 0.22 USD/m

^{3}at optimized conditions (pH 7, 11.5 V, interelectrode distance of 3 cm, 0.5 kg/m

^{3}of salts, and 14 min of treatment). While, in another study, Bian et al. [58] reported energy consumption between 0.378 and 0.977 kWh/m

^{3}to treat oily bilge using electrocoagulation, which was much higher than the value of the present study. As the results revealed, the present study confirms previous findings.

#### 3.6. Treatment of Direct Osmosis Concentrate from Metallurgical Industry

## 4. Conclusions

^{3}, 0.217 kg Al/m

^{3}, and 0.423 USD/m

^{3}. It can be concluded that modeling and optimization by CCD under RSM could lead to satisfactory results. In addition, 94.2% Pb removal was achieved from DO concentrate of real metallurgical wastewater through PA-ECF. Although the prospect of this study was to examine the process on an industrial scale and to achieve reasonable results with real operating parameters, further studies are essential to examine electrochemical degradation pathways and mechanisms for Pb removal via GC-MS analysis.

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

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**Figure 2.**The normal probability plot of residuals (

**a**), externally studentized residuals vs. predicted (

**b**), externally studentized residuals vs. the run number (

**c**), and predicted vs. actual plots (

**d**) for Pb removal.

**Figure 3.**Pareto analysis of the effects of factors on Pb removal for DO concentrate treatment by the PA-ECF technique.

**Figure 4.**Contour plot (

**a**), and 3D response surface plot (

**b**) of the effect of current intensity and electrolysis time on removal efficiency of Pb.

**Figure 5.**Contour plot of electrolysis time and current intensity on Pb removal at optimum condition.

**Figure 7.**A set of experiments under optimal conditions (electrolysis time of 77.65 min and current intensity of 0.9 A) for Pb removal from DO concentrate of the actual metallurgical wastewater.

Independent | Code | Code Levels | ||||
---|---|---|---|---|---|---|

Variables | Variables | −1.41 | −1 | 0 | +1 | +1.41 |

Electrolysis time (min) | A | 5.85 | 22 | 61 | 100 | 116.15 |

Current intensity (A) | B | 0.09 | 0.5 | 1.5 | 2.5 | 2.91 |

Runs | Standard Run No. | Experimental Matrix | Removal Efficiency (%) | ||
---|---|---|---|---|---|

Pb | |||||

A | B | Actual | Predicted | ||

1 | 12 | 0 | 0 | 98.90 | 98.2 |

2 | 13 | 0 | 0 | 97.51 | 98.2 |

3 | 11 | 0 | 0 | 96.4 | 98.2 |

4 | 5 | −1.41 | 0 | 92.89 | 92.72 |

5 | 10 | 0 | 0 | 99.87 | 98.2 |

6 | 2 | +1 | −1 | 91.53 | 91.75 |

7 | 7 | 0 | −1.41 | 88.45 | 88.13 |

8 | 3 | −1 | +1 | 95.35 | 95.36 |

9 | 4 | +1 | +1 | 100.00 | 99.92 |

10 | 6 | +1.41 | 0 | 97.64 | 97.59 |

11 | 9 | 0 | 0 | 98.31 | 98.2 |

12 | 1 | −1 | −1 | 89.12 | 89.42 |

13 | 8 | 0 | +1.41 | 97.99 | 98.08 |

Parameter | Equation for Real Variables |
---|---|

Υ_{1} (Pb removal efficiency, %) | +98.20 + 1.72 × A + 3.52 × B + 0.56 × AB − 1.52 × A^{2} − 2.55 × B^{2} |

Source of Variations | Sum of Squares | Df | Mean Square | F-Value | p-Value Prob > F | Remarks |
---|---|---|---|---|---|---|

Model | 179.67 | 5 | 35.93 | 34.48 | <0.0001 | Highly significant |

A-Electrolysis time | 23.73 | 1 | 23.73 | 22.77 | 0.002 | significant |

B-Current intensity | 99.36 | 1 | 99.36 | 95.34 | <0.0001 | Highly significant |

AB | 1.25 | 1 | 1.25 | 1.20 | 0.3089 | |

A^{2} | 16.15 | 1 | 16.15 | 15.50 | 0.0056 | Significant |

B^{2} | 45.23 | 1 | 45.23 | 43.41 | 0.0003 | Significant |

Residual | 7.29 | 7 | 1.04 | |||

Lack of Fit | 0.29 | 3 | 0.096 | 0.055 | 0.9809 | Not significant |

Pure Error | 7.01 | 4 | 1.75 | |||

Cor Total | 186.97 | 12 | ||||

R^{2}/R^{2}_{adj} (%) = 0.96/0.93 |

Parameters | Electrolysis Time (min) | Current Intensity (A) | Removal (%) Predict | Removal (%) Experimental |
---|---|---|---|---|

Optimal value | 77.65 | 0.9 | 97.8 | 95.52 |

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**MDPI and ACS Style**

Moosazade, M.; Ashoori, R.; Moghimi, H.; Amani, M.A.; Frontistis, Z.; Taheri, R.A.
Electrochemical Recovery to Overcome Direct Osmosis Concentrate-Bearing Lead: Optimization of Treatment Process via RSM-CCD. *Water* **2021**, *13*, 3136.
https://doi.org/10.3390/w13213136

**AMA Style**

Moosazade M, Ashoori R, Moghimi H, Amani MA, Frontistis Z, Taheri RA.
Electrochemical Recovery to Overcome Direct Osmosis Concentrate-Bearing Lead: Optimization of Treatment Process via RSM-CCD. *Water*. 2021; 13(21):3136.
https://doi.org/10.3390/w13213136

**Chicago/Turabian Style**

Moosazade, Milaad, Razieh Ashoori, Hamid Moghimi, Mohammad Ali Amani, Zacharias Frontistis, and Ramezan Ali Taheri.
2021. "Electrochemical Recovery to Overcome Direct Osmosis Concentrate-Bearing Lead: Optimization of Treatment Process via RSM-CCD" *Water* 13, no. 21: 3136.
https://doi.org/10.3390/w13213136