Evaluation and optimization of electrocoagulation process parameters for the treatment of oil industry drilling site wastewater

doi:10.21203/rs.3.rs-4157348/v1

The effluent from the oil drilling site is a complex mixture of hazardous chemicals that causes environmental impacts on its disposal. The treatment of oil drill-site wastewater has not been explored much and requires understanding its characteristics and optimizing the treatment process. In the present study, we have optimized the electrocoagulation process with aluminum electrodes for drill-site wastewater treatment. A multi-level factorial center composite design using response surface methodology (RSM) is applied to optimize the effect of current density, pH, and inter-electrode distance (IED) on COD removal. The increasing current density shows a significant increase in COD removal, and a similar trend was observed with a decrease in pH. It was found that with current density and inter-electrode distance, the maximum COD removal achieved was 70% at the CD of 19.04 mA cm^-2and IED 2.6 cm. By varying pH and current density, the COD removal reached up to 90% at pH 6 and CD 19.04 mA cm^-2. The study shows that the current density is the dominant factor for the process's energy consumption and operating cost, followed by pH. This study's findings could be effectively used for developing large-scale treatment processes through the electrocoagulation process.

Drill site wastewater

electrocoagulation

response surface methodology

current density.

Recently, freshwater availability for consumption has been severely affected due to population growth, unsustainable industrialization, and climate change (Yavuz and Ogütveren, 2018; Jing et al., 2021). Wastewater streams generated from various industries cause a two-fold impact on this issue – first, they pollute the available water resources, and second, the 'contaminants' load in these streams limits their reusability. (Yavuz and Ogütveren, 2018; Babu et al., 2020; Shahedi, Darban and Taghipour, 2020). Furthermore, the contaminants in different industrial wastewater pose a severe threat to living organisms as many of them are carcinogenic in nature (Ighilahriz et al., 2014; Pichtel, 2016; Elnenay et al., 2017). To combat these issues, strict regulations, on-site treatment, and enhancing reusability of the wastewater should be the prime focus (Fang et al., 2013; Yavuz and Ogütveren, 2018; Babu et al., 2020). One of the industries on the list is the oil and gas industry, which generates wastewater at different extraction stages with high contamination levels due to the use of different chemicals (Kerri L Hickenbottom et al., 2013; Bryan D. Coday et al., 2014; Ighilahriz et al., 2014). The wastewater generated during the construction of a well to extract oil is termed "drilling wastewater," and the wastewater generated after the well construction for oil and natural gas recovery is termed produced water (Bryan D. Coday et al., 2014; Ighilahriz et al., 2014; Zhao et al., 2014; Sardari et al., 2018). The drilling process consumes up to 3800 m³ of water that generates wastewater streams comprising drilling additives and contains high concentrations of chemical oxygen demand (COD) and total suspended and dissolved solids (Kerri L Hickenbottom et al., 2013; Bryan D Coday et al., 2014; Ighilahriz et al., 2014; Elnenay et al., 2017). The typical range of the mentioned parameters was found as COD 600–5600 mg l^− 1, total suspended solids, 0.5–2.5 g l^− 1 mg, and total dissolved solids 1.0-8.2 g l^− 1 (Ighilahriz et al., 2014; Ma et al., 2016; Mohamed et al., 2017; Mu et al., 2021; Zhang et al., 2021). Generally, the wastewater is collected in the pit near the drilling site, and in most operations, it is discharged off-site into deep wells after the settlement of solids (Kerri L Hickenbottom et al., 2013; Bryan D Coday et al., 2014). This kind of practice degrades the groundwater quality and could cause health issues upon further use.

The drilling mud is used to drill the well and generates significant waste (Bryan D Coday et al., 2014; M. Changmai, Pasawan and Purkait, 2019). The drilling mud consists of chemicals like emulsifiers, viscosifier, lubricants, proppants, gallants, crosslinkers, foamers, and stabilizers (Kerri L. Hickenbottom et al., 2013; Bryan D Coday et al., 2014; Ighilahriz et al., 2014; Pichtel, 2016). These chemicals in drill mud threaten the ecosystem and public health, and many are carcinogenic in nature or endocrine disruptors (Ighilahriz et al., 2014; Pichtel, 2016; Liu et al., 2020). The toxic and complex nature of the wastewater thus creates the need for appropriate treatment due to its high volume generation and disposal scenario (Hickenbottom et al., 2013). (Kerri L. Hickenbottom et al., 2013; Ighilahriz et al., 2014; Elnenay et al., 2017; M Changmai, Pasawan and Purkait, 2019; Liu et al., 2020). Among the various treatment processes available, electrocoagulation (EC) is one of the technologies that could be applied to treat the drill site wastewater effectively.

The EC process typically uses electricity and iron or aluminum metal as a sacrificial anode for the coagulation. It consists of an electrode arrangement in contact with the target wastewater. The electric current passes through electrodes and causes the dissolution of the metal ions from the anode and hydroxyl ions from the cathode (Mollah et al., 2004; Hakizimana et al., 2017; Mousazadeh et al., 2021; Sivaranjani et al., 2021). These metal ions generate in-situ flocs by combining with hydroxyl ions within the wastewater (Mollah et al., 2004; Ghernaoute et al., 2012; Kabdaşlı et al., 2012; An et al., 2017; Garcia-Segura et al., 2017; Hakizimana et al., 2017). The complete process of treatment consists of three major steps, i.e., the generation of ions at the anode and cathode due to electrolytic reaction as shown in equations 1 to 3, formation of coagulants in the wastewater, and lastly, the removal of the pollutant by adsorption that gets settles down or floats to form the scum layer (Mollah et al., 2004; Sahu, Mazumdar and Chaudhari, 2014; Garcia-Segura et al., 2017; Hakizimana et al., 2017; Islam, 2019).

At the anode	M(s)→ M³⁺(aq) + 3e⁻	(1)
At the cathode	3H₂O(l) + 3e− → $\frac{3}{2}$H₂ + 3OH⁻	(2)
In the solution	M³⁺(aq) + 3H2O → M(OH₃)(s) + 3H⁺(aq)	(3)

In EC, multiple factors affect the process, such as current density (CD), pH, inter-electrode distance (IED), electrode arrangement, conductivity, reactor design and concentration of the ionic species. The researchers have considered multiple combinations to get the maximum possible removal. Furthermore, the removal also increases with an increase in electrolysis time and CD (Elnenay et al., 2017; Khorram and Fallah, 2018; Wagle et al., 2020). The maximum removal of 99% was also achieved by combining CD and salt composition to treat the produced water (Manilal, Soloman and Basha, 2020). The studies were also conducted using the combination of CD, pH, reaction time for COD, hardness, and turbidity removal from produced water(Zhao et al., 2014). Some studies have also used a combination of EC, forward osmosis, and microfiltration for drilling wastewater and produced water treatment (Sardari et al., 2018; Changmai et al., 2019). With these many factors, optimization of the process and mathematical modeling become quite difficult (Amani-Ghadim et al., 2013). The mentioned limitations can be overcome by applying experimental optimization strategies such as response surface methodology (RSM) (Ponselvan et al., 2009; Khorram and Fallah, 2018). This technique uses statistical and mathematical equations/methods for developing a model and evaluating the individual and combined effect of the number of parameters on the designed response, i.e., %COD removal. The technique also provides the results in graphical forms that make it easier to visualize and comprehend the results (Amani-Ghadim et al., 2013; Pi et al., 2014; Khorram and Fallah, 2018; Wagle et al., 2020). In addition to these advantages, RSM dramatically decreases the total number of experiments required for optimization, saving time and experimental costs.

The study focuses on drill-site wastewater treatment to optimize the EC process using the response surface methodology. The EC was performed first by varying the CD and IED values, followed by changing its initial pH value, further optimizing the process, and selecting the optimum parameters and conditions for efficiently removing pollutants. The optimization process is applied with two factors and three levels of factorial design using a center composite design by using Design Expert software (version 13; 2022) each time. The two factors used for the optimization process are different for each wastewater and were selected based on the results of the first optimization study where the CD and IED were varied in the EC treatment process. The objective is to quantify the individual and interactive effects of the process parameters and their impact on COD removal and energy consumption. The cost estimation is also calculated to understand the economic aspects of the process parameters.

Wastewater characteristics

The wastewater was collected from the drill-site location at two depths of the wells owned by Oil and Natural Gas Corporation (ONGC), Mehsana, Gujarat, India. The samples were received from the drilling site at Mahesana from two depths, 1311 and 2300 m, from a single drilling well. Each characterization was done in triplicates to ensure the accuracy of the characteristics of the samples. The initial wastewater characteristics were carried out and are given in Table 1. It depicts the presence of high TDS and conductivity in both samples and a moderate level of suspended solids.

Table 1

Characteristics of the wastewater studied drill site wastewater
S. No	Parameter	Unit	Concentration (µ ± σ) n = 3
S. No	Parameter		DSW-1	DSW-2
1	Total solids	g l^− 1	9.26 ± 0.33	11.61 ± 0.32
2	Total dissolved solids	g l^− 1	6.99 ± 0.27	10.53 ± 0.22
3	Total suspended solids	g l^− 1	2.27 ± 0.23	1.08 ± 0.18
4	pH	-	7.61 ± 0.02	8.44 ± 0.02
5	Conductivity	mS cm^− 1	13.99 ± 0.30	21.04 ± 0.30
6	COD	mg l^− 1	560 ± 3.2	1488 ± 4.8

Experimental setup

The EC reactor with a capacity of 400 ml, is fabricated with plexiglass. The aluminum electrodes of dimensions 70×40×4 mm were taken for the experiments (Fig. 1). All the experiments were conducted in galvanostatic mode. The electrodes were connected to the external DC power supply (make: HTC, 2021). The electrodes were cleaned with carbide paper (C-220), washed with deionized water, and put in 0.1 M hydrochloric acid and 0.1 M acetone solution for 10 minutes. The EC process is carried out in batch mode and is continuously stirred to maintain the uniformity of the sample during the process. The sample was taken and kept for 2 hours for the suspended solids settlement, and then the supernatant was used for analysis. Preliminary runs were carried out to observe the behavior of the sample on COD removal by applying variables like CD, reaction time, and pH. The runs were also carried out to set up the variables for optimization study using the Design Expert software. The preliminary study's objective was to observe the variation in treatment efficiency by applying a different combination of input variables and also to develop a pair of variables for the optimization study. Based on the preliminary study results, optimization was done to get the optimized parameters and their optimum values.

Analytical methods

The sample measured the chemical oxygen demand (COD) values before and after the run. For each run, the change in cell voltage (CCV) was recorded; CCV is the difference in voltage value before and after the experiment by applying a constant current value to the electrocoagulation experiment. The COD of the sample was measured by the APHA standard potassium dichromate titrimetric method. The COD removal efficiency was calculated using Eq. 4 below.

Where C_i and C_f are the sample's initial and final COD in mg L^− 1.

The EC process is optimized using RSM using Design-Expert software version 13. The two factors of three-point design by face-centered central composite design (CCD) were applied to evaluate the effects of CD, inter-electrode distance (IED), and pH individually and in combination on COD removal performance of the EC process. The set of experiments obtained from the software is then carried out, and the data obtained is then used to optimize the process and predict the optimum conditions for the process. Based on the preliminary results, the levels of variables were entered into the software for each sample, and the software generated the EC runs. A total of 13 experiments for each sample were performed; 9 were based on a 3-level factorial design, and 4 additional experiments replicated central points to predict the curvature and region of the optimum condition. The experimental data obtained was utilized to fit a full quadratic model as follows:

$\varvec{Y}={\varvec{\beta }}_{\varvec{o}}+{\varvec{\beta }}_{1}{\varvec{X}}_{1}+ {\varvec{\beta }}_{2}{\varvec{X}}_{2}+{\varvec{\beta }}_{11}{\varvec{X}}_{11}^{2}+ {\varvec{\beta }}_{22}{\varvec{X}}_{22}^{2}+ {\varvec{\beta }}_{12}{{\varvec{X}}_{1}\varvec{X}}_{2}+ \varvec{\epsilon }$

(5)

where Y is the response (COD removal); β₀ is the coefficient of model intercept, (β₁, β₂) are the linear coefficients of the independent variables, (β₁₁, β₂₂) are the quadratic coefficients of the independent variables, and β₁₂ is the interaction coefficient of the independent variables; X₁ and X₂ are the independent variables, and ε is error term in the evaluation. All of the coefficients of the input variables impact the dependent variable, and their values suggest the magnitude of the impact they put on the dependent variable.

The data from a series of EC runs were fitted to the model (Eq. 5), and coefficients were obtained for the model. The model's quality and prediction power were analyzed by the value of the coefficient of determination, R². Fisher F-test was used to check the significance of the parameters. The model coefficients obtained were selected based on their calculated probability (p) value at a 95% confidence interval. The contour plots based on the experiment runs show the effect of independent variables on the response variable. The predicted versus observed plots also depict the performance of the model. The variables used in the optimization process for each type of wastewater are shown in Table S1 (SI). The parameters for the DSW-1 are CD and pH, and DSW-2 are CD and IED. The runs were designed based on three levels of the independent variables. The experiments were designed to observe the individual and combined effect of the independent variable on the response variable. The complete methodology for energy and operating cost calculation is given in the supplementary material.

Preliminary results of the study

The sample was preliminarily treated at different CD, pH, and reaction time values to observe the wastewater treatment performance on applying the EC process. In the preliminary studies, the IED was kept constant at 2 cm in all the runs, and the mixing speed was maintained at 300 rpm. The result obtained from the preliminary studies is shown in Fig. 1.

The results show that the COD removal increases in both samples with increased reaction time and CD. There is a significant increase in COD removal on increasing the CD, as shown in Fig. 1b. In the case of DSW-1 at 10.94 mA cm^− 2 CD, the removal efficiency increases from 13.11–41.11% percent by increasing the run-time from 15 to 45 minutes. The effect of pH was observed by treating the sample at its original pH, as given in Table 1, and an acidic pH of 4(Fig. 1c). On varying pH to 4, in the case of DSW-2, the effect of change in pH was observed to be more dominating than in DSW-1At pH 4, the increase in COD removal was seen from 53–67%, in the case of DSW-1, while for DSW-2, the change in COD removal was double than it was at sample pH, i.e., from 42 to 88%.

A similar trend was observed in the case of DSW-2; on increasing the reaction time from 15 to 45 minutes, the removal increases from 19.23 to 57.69%, and on increasing CD from 4.76 to 19.04 mA cm^− 2, the removal increases from 42.31 to 65.21%. Similar results were observed in earlier studies; researchers depict that increasing the CD causes an increase in bubble density and decreases bubble size, and also increases the generation of Al³⁺ ions and hence more coagulant formation and higher COD removal, thus enhancing the treatment process (Khosla and Venkatachalam, 1991). Researchers also reported increased COD removal with reaction time due to increased floc formation and settlement (Huda et al., 2017). Therefore, increasing the COD removal by increasing the reaction time is a predicted point followed in studies.

Based on the results observed from preliminary studies, the reaction time was kept constant at 45 minutes as the maximum COD removal was observed between 30 and 45 minutes in both samples. The optimization process was carried out in two stages; the first set of variables was optimized using the DSW-1 wastewater sample, and based on the optimization results, the second set of optimization was carried out in the DSW-2 wastewater sample. In the first step of optimization, internal factors CD and IED were used. The IED affects the drop in the ohmic resistance of the cell; it increases by increasing the IED. The IED also affects the electric energy; for a low conductivity solution, the energy consumption increases with the IED and vice versa (Mollah et al., 2004; Yoosefian et al., 2017). The decrease in IED value also causes more bubble generation and turbulence and thus lead to high mass transfer and reaction rates between the pollutant and coagulant (Mollah et al., 2001; Sahu, Mazumdar and Chaudhari, 2014; Hakizimana et al., 2017). In the second step, CD, which altered the system pH, was used for the optimization process. The effect of initial pH plays a crucial role in determining the performance of the EC process. Various studies have used a range of pH values to maximize COD removal. Researchers varied the initial pH from 5 to 11 to analyze its effect on COD removal (Tir and Moulai-Mostefa, 2008). It was observed that dominant aluminum species play a critical role in pollutant removal at a pH range of 5–6 (Ponselvan et al., 2009). A similar trend was observed on varying pH from 4–10 for COD removal (Faheem et al., 2021).

Effect of variables on treatment performance

The effect of variables like CD, IED, and pH on the treatment performance was observed by carrying out various EC runs using various combinations of mentioned variables. The software-generated experimental runs, COD removal results and CCVs are presented in Table 2. All the parameters have significantly affected COD removal except the IED; the p-value of IED was 0.40, which is more than the significant p-value of 0.05. In the case of DSW-1, the CD is the major factor (model coefficient = 13) leading the removal process, while in the case of DSW-2, the synergistic effect of the two variables, pH (model coefficient = 12) and CD (model coefficient = 15), was observed. The CCV also reaches its maximum value of 0.4 V in the case of wastewater with lower conductivity, i.e., DSW-1. It was observed that in the case of DSW-1, CD plays a major role in COD removal compared to IED as IED has a model coefficient of 1.51 (CD = 13), whereas, with DSW-2, there is a synergistic effect of both variables, CD, and pH, the increase in CD increases the COD removal and CCV, and the decrease in pH increases the COD removal. It means that the pH and CD play a significant role in the COD removal process and contributes to the system performance; specifically, CD contributes majorly to the treatment performance for both the wastewater samples within the experimental range of this study.

The experimental data developed the quadratic model stated in Eq. 2 to replicate the effect of variables during the EC process. The model terms developed by the data were tested for their statistical significance using a t-test. The significant level of all the variables was tested at the 95% confidence limit, the significant variables were included, and nominal terms were excluded from the model. The model's significance can also be tested by calculating the coefficient of variance, which depicts the spread of the data around the mean. Its acceptable values should be less than 10% (Asfaha et al., 2022; Nasrullah et al., 2022). In the current study, the coefficient of variance was calculated; in the case of DSW-1, the value was 8.76; for DSW-2, it was 7.56. The model developed for the COD removal and the CCV is shown in equations 6 to 9. The model validation was also carried out; the results obtained are shown in Table S5 (SI).

Table 2: Experimental results obtained from the factorial runs of COD removal and the CCV between the two samples.

	DSW-1				DSW-2
Run	CD (mA cm^− 2)	IED (cm)	COD removal (% ±SD)	CCV (V)	CD (mA cm^− 2)	pH	COD removal (% ±SD)	CCV (V)
1	11.9	2.6	43.32 ± 1.07	0.13	11.90	4	81.88 ± 0.46	0.12
2	11.9	1.2	44.00 ± 0.91	0.3	19.04	6	90.5 ± 1.03	0.18
3	11.9	4	47.06 ± 0.62	0.18	19.04	4	80.86 ± 0.54	0.2
4	19.04	4	64.76 ± 0.59	0.52	4.76	8	22.64 ± 2.25	0.01
5	19.04	1.2	60.78 ± 0.33	0.18	11.90	6	70.52 ± 0.64	0.07
6	19.04	2.6	70.59 ± 1.35	0.2	4.76	4	77.03 ± 1.75	0.16
7	4.76	2.6	32.14 ± 1.15	0.1	11.90	6	71.5 ± 0.64	0.07
8	11.9	2.6	42.26 ± 1.07	0.13	11.90	6	70.52 ± 0.64	0.07
9	11.9	2.6	42.86 ± 1.07	0.13	11.90	6	71.43 ± 0.64	0.07
10	4.76	1.2	42.00 ± 8.03	0.2	11.90	8	57.14 ± 0.52	0.1
11	4.76	4	44.00 ± 6.65	0.21	19.04	8	67.46 ± 1.25	0.3
12	11.9	2.6	42.04 ± 1.07	0.13	11.90	6	70.52 ± 0.64	0.07
13	11.9	2.6	41.05 ± 1.07	0.13	4.76	6	66.67 ± 0.27	0.1

The scatter plots of predicted versus actual values for COD removal and the CCV are shown in Fig. 2. The high value of the adjusted R² value also shows the significance of the model. The difference between R² and adjusted R² also advocates the high significance of the model (Acharya, Thakur and Chaudhari, 2020). Similar trends were also observed in other studies with dye, palm oil, and textile wastewaters, where observed and adjusted R² values were ~ 99% and ~ 98%, respectively (Amani-Ghadim et al., 2013; Khorram and Fallah, 2018; Nasrullah et al., 2022). The trend of an R² value of more than 95% was observed in the case of COD removal in studies done with other kinds of wastewater and is considered a reliable and effective model for optimizing the process (Tir and Moulai-Mostefa, 2008; Ponselvan et al., 2009; Manilal, Soloman and Basha, 2020; Nasrullah et al., 2022). The results from all the studies show the closeness of the R^2, and the adjusted R² value depicts that the model and the independent variable are significant statistically and can predict the response (Khorram and Fallah, 2018; Acharya, Thakur and Chaudhari, 2020; Nasrullah et al., 2022).

Response surface Modelling (RSM)

RSM is commonly used for optimization purposes in industry and academic research when a number of variables influence the system's performance (Khorram and Fallah, 2018), and it helps to estimate the process's significant operating parameters and their synergistic and antagonistic effects (Ponselvan et al., 2009). Based on the result obtained from the factorial design, response surface modeling was performed to visualize the effect of the independent variable on the response variable. The model summary and model performance for both samples were mentioned in Tables S3 to S6. The contour plots and 3d surface plots for COD removal are shown in Fig. 3. The contour plots are the 2D representation of the response variable where each line shows the individual removal efficiency and its curvature with the independent parameter. The 3D plot shows the effect of individual parameters on the response and 3D representation and helps to understand the independent parameters' synergistic or antagonistic effect on the response variable. The contour plot depicts the specific removal efficiency and the points along the parameters or combinations of achieving it. The 3d plot expresses the interaction by the curvature change along the parameters' values. It also helps in deciding the values to use in the practical scenario while carrying out the process in the lab or the field.

Previous studies also used the above parameters to evaluate the effect on different responses like COD removal, turbidity removal, decolorization, and TOC removal (Tir and Moulai-Mostefa, 2008; Ponselvan et al., 2009; Maha Lakshmi and Sivashanmugam, 2013; Khorram and Fallah, 2018). The most common parameters used in all studies were CD and reaction time, and apart from these, pH, electrolyte, inter-electrode distance, and initial pollutant concentration were used for optimization. The individual and combined effect of parameters on the response helps to understand the EC process and manage the desired point of pollutant removal by optimizing the process parameters.

Impact of CD, pH, and IED

Among all the parameters in the EC process, CD plays a vital role in the removal process. It controls the reaction rate and determines the metal dissolution rate in the reactor (Khorram and Fallah, 2018). The CD causes the generation of metal ions that form flocs that either settle or float up to form a scum layer (Sahu, Mazumdar and Chaudhari, 2014; Hakizimana et al., 2017). In the case of both samples, an increase in CD increases the removal and reaches the maximum value at 19.04 mA cm-2. The maximum removal of 70.59% and 90.50% was achieved with DSW-1 and DSW-2, respectively, at the maximum CD of 19.04 mA cm^− 2 (Fig. 3). The contour lines and the 3D plot shows that COD removal increases with the increase in CD in both samples. A large polymeric chain flocs formation leads to contaminant removal by attachment to the chain and precipitation. The formation of Al³⁺ also causes destabilization of the colloidal particles and leads to either settling or collapsing to form flocs with aluminum flocs (Hakizimana et al., 2017; Moussa et al., 2017). The CD increases the number of metal ions, and the formation of flocs and the amount of Al ions formed during the process was explained by Faraday's law (equation. 6) (Wagle et al., 2020).

$$\varvec{W}= \frac{\varvec{i}\varvec{t}\varvec{M}}{\varvec{z}\varvec{F}}$$

10

where, W is the aluminum dissolved in grams, i is the current in Ampere, t is the run time in seconds, M is the atomic weight of aluminum, Z is the number of electrons (3 for Al), and F is the Faraday constant. The relationship shows that current is directly proportional to the amount of aluminum generated in the solution, and increasing the value leads to pollutant removal.

The effect of variation in pH was also investigated by varying the pH from 4 to 8, and the result is illustrated in Fig. 3b. The region of maximum removal was observed between the pH range of 4–6 at all CD values, and the removal was more than 70%. The maximum removal of 90.5% percent was observed at pH 6 and 81.88% at pH 4, at the maximum CD of 19.04 mA cm^− 2.

These results agree with previous studies, and it was observed that the maximum COD removal was obtained for pH < 6.0 (Ponselvan et al., 2009). More than 80% COD removal was observed in another study at pH 4 (Gengec et al., 2012). At a low pH value, metal species like Al³⁺ generated at the anode cause destabilization of the colloidal particles; this causes agglomeration of the particles that precipitate or float up by the bubbles to form a scum layer at a ~ pH 6 and more amorphous aluminum monomeric and polymeric species cause adsorption. Many such species cause sweep coagulation (Cañizares et al., 2007). The results confirm the impact of pH on the removal process and are one of the critical parameters for the EC process for drill-site wastewater treatment.

Combined effect of parameters on the treatment performance

In the case of DSW-1, the combined effect of CD and IED can be seen in Fig. 3a. The contour and 3d plot depict the COD removal effect of varying the independent variables. It can be observed from the contour plot that the effect of IED on COD removal is insignificant as compared to the CD. However, it can be observed that at 2.6 cm IED, we get a maximum removal of 70.59% for a CD of 19.04 mA cm^− 2. Also, at the CD of 11.9 mA cm^− 2, we get the removal in the 43–47% range in all the IED values. There is a subtle effect of IED observed in the case of maximum CD, at a maximum CD value of 19.04 mA cm^− 2; the COD removal first increases (64–70%) and then decreases (70–64%) by varying the IED from 1.2 to 2.6 and 4 cm. The possible reason for the IED's insignificant effect could be the wastewater's high conductivity (13.99 mS cm⁻1) that nullifies the resistance caused by varying the IED. The IED variation affects the electrode resistance; therefore, more voltage is required for a constant current value to overcome that resistance. Therefore, we require an optimum IED value for the process, which provides maximum pollutant removal (Ponselvan et al., 2009; Huda et al., 2017).

In a study of the treatment of ore production wastewater, an IED of 1 cm was found optimum for wastewater having 1 mS/cm conductivity(Das and Nandi, 2021). In another study, for swine wastewater treatment of conductivity 5 mS/cm, 2 cm IED was observed to be the optimum condition for best removal (Chen et al., 2021). In the samples DSW-1 and DSW-2, due to the high conductivity of the wastewater (13 mS cm^− 1 and 21 mS cm^− 1), the effect of IED gets diminished, and the voltage developed by varying the IED did not impact the COD removal process; hence, the effect was reflected in the results.

The combined effect of pH and CD can be visualized from the 3D plot in Fig. 3b, and it was observed that with increased CD, the removal process enhanced while maintaining a constant pH. On the other hand, by increasing the CD and pH simultaneously, we can observe a reduction in COD removal from 4 to 8. In the case of pH 4, the removal of 77% was obtained at the CD of 4.76 mA cm^− 2. On increasing the pH to 8, the COD removal reduces to 22% at the CD of 4.76 mA cm^− 2, and it reaches 80% at 19.76 mA cm^− 2. As seen in Fig. 3b, in the region of 5–6 pH, there is a small increase and drop in COD removal (⁓85% to ⁓80%), signifying the optimum pH range for COD removal.

Similar trends were reported in other studies; the optimum pH condition for COD removal was in the range of 4–7 (Tir and Moulai-Mostefa, 2008; Gengec et al., 2012; Tak et al., 2015; Khorram and Fallah, 2018; Faheem et al., 2021). It was observed that the maximum COD removal at pH 5 remains constant till pH 6 and starts decreasing after that value (Tir and Moulai-Mostefa, 2008). The maximum removal on varying the pH of the wastewater was reported in the pH range of 4–6 in most of the studies (Gengec et al., 2012; Khorram and Fallah, 2018). All the studies showed that the effective pollutant removal region was observed under an acidic pH regime. This phenomenon is because, in an acidic medium, the formation of Al³⁺ ions and hydrolysis of the ions lead to the formation of monomeric Al(OH)₂⁺ and Al(OH)²⁺. These monomeric cations further react to form hydroxypolymeric chains such as Al₂(OH)₂⁴⁺ and Al₆(OH)₁₅³⁺ (Faheem et al., 2021). The polymeric chains formed have a large surface area and cause the entrapment of soluble organic and inorganic compounds and colloidal particles. The particles' adsorption and entrapment will separate from the solution by settlement or floating by bubbles.

The effect of varying the independent parameters (CD, pH, IED) on the CCV during the process was also observed and shown as contour and 3d plots in Fig. 4. The voltage applied during the process was the sum of the equilibrium overpotential anode, cathode overpotential, and ohmic resistance drop of the solution (Chen, Chen and Yue, 2002). It directly influences energy consumption and as its value increases, so does the process's energy consumption. In the case of DSW-1, the CCV change was maximum at high CD and high IED values. The minimum change was observed in the IED value of 2.6 cm. At a low IED value of 0.5 to 1.0 cm, due to less resistance, the flow of current is smooth and hence causes less CCV (Ponselvan et al., 2009). At a high IED of 4 cm, the resistance is higher than the low IED value of 2.6 cm; hence, the voltage needs to increase to maintain the flow of constant current, which adds to the CCV value (Huda et al., 2017). It was observed that a change in voltage also impacts the removal process as it directly affects the bubble generation (Khatibikamal et al., 2010). In the case of aluminum, it was reported that the CCV also causes significant COD removal up to a certain optimum value and has a low impact on COD removal after the optimum conditions. (Moosavirad, 2017). From the results obtained by treating both samples, it can be concluded that CD, pH, and IED play a role in the CCV initially, and during the process, any changes in the parameters mentioned above also reflect the impact on CCV.

With the treatment process observed in DSW-2, the maximum CCV of 0.25 V was observed at the maximum CD value of 19.04 mA cm^− 2 and pH 8. It was also observed from Fig. 4b that at a constant pH 8 value, increasing the CD from 4.76 mA cm^− 2 to 19.04 mA cm^− 2 increases the CCV from 0.05 to 0.25 V. Furthermore, it was observed that at a low CD value of 4.76 mA cm^− 2, the CCV increases from 0.01 to 0.16 V and at high CD value of 19.04 mA cm^− 2 it decreases from 0.3 to 0.2 of by varying the pH from 8 to 4. The two results signify that high CD and low pH or acidic pH cause the major CCV. Due to the high current input, more voltage must be pushed to manage the power requirements as power is directly proportional to the current and voltage; however, the high conductivity value can compensate for the power requirement. It was reported that current, voltage, and conductivity are correlated, hence the associated parameters, like CD and power requirement (Chen, Chen and Yue, 2002). Although the change in the CCV due to the change in CD is not a major value, it still signifies that the CD impacts the change in voltage and plays a dominant role in the process. Additionally, it was observed that in the case of aluminum electrodes, less CCV was observed as compared to other metals used in EC (Izquierdo et al., 2010). In the case of DSW-2, a similar trend was observed with an increase in CD.

Optimization of cost and energy consumption

The energy consumption with respect to COD removal and electrode and operating cost associated with energy is calculated and shown in Table 3. In the case of DSW-1, the energy consumption varied from 2.5 to 23.28 kWh kg^− 1 COD^− 1 with COD removal of 43–65%. The maximum removal was also observed at the energy consumption of 15.64 kWh kg^− 1COD^− 1. In both samples, the CD is the dominating factor in energy consumption; however, changes in the IED value significantly affect operating costs. While increasing the IED from 1.2 to 4 cm, the operating cost increased from 0.05 to 0.03 USD m^− 3; a similar trend is observed at 4.76 mA cm^− 2, that on increasing the CD value, the cost increased from 0.03 to 0.24 USD m^− 3; for change in CD from 4.76 to 19.04 mA cm^− 2. Increasing the IED causes an increase in resistance, leading to an increase in the CCV that requires more energy to overcome the resistance.

Meanwhile, in the case of DSW-2, the energy consumption value ranges from 0.81 to 10.38 kWh kg^− 1COD^− 1 with COD removal of 67–77%. The maximum removal of 90% was observed with an energy consumption of 7.22 kWh kg^− 1 COD^− 1, and minimum energy consumption was observed at acidic pH and at 4.76 mA cm^− 2. In contrast, the maximum value of CCV was at pH 8 and a CD of 19.04 mA cm^− 2. A similar trend was observed in the operating cost of the treatment, which agreed with other studies conducted on different industrial wastewaters (Kobya, Can and Bayramoglu, 2003; Tezcan, Koparal and Bakir, 2009; Elabbas et al., 2016). It was reported that the increase in CD value contributes directly to energy consumption (Faheem et al., 2021). Evidently, energy consumption is directly affected by the current supply. The energy consumption increases from 0.53 to 3 kWh m^− 3 on increasing the CD from 10 to 40 mA cm^− 2 (Maha Lakshmi and Sivashanmugam, 2013). Furthermore, it was observed that a pH change does not significantly affect operating costs. In DSW-1, the change in pH from 4 to 8 increases the operating cost from 0.041 to 0.42 USD m^− 3. A similar result was reported: changing pH from 4 to 10 does not significantly affect energy consumption and operating costs (Faheem et al., 2021). As a process operation, this provides flexibility to change the pH to desirable values without incurring additional costs. Overall, CD contributes majorly to energy consumption and operating costs, followed by IED and pH. However, the impact of CD is way more than the other two parameters.

Table 3

Energy consumption and cost analysis for the two samples.
DSW-1
CD (mA cm^− 2)	IED (cm)	EC (kWh kg^− 1 COD^− 1)	C_energy (kWh m^− 3)	C_electrode (kg of Al m^− 3)	Operational Cost (USD m^− 3)
4.76	1.2	2.50	0.42	2.80E-05	0.03
19.04	1.2	12.24	3.168	1.12E-04	0.24
4.76	4	2.86	0.594	2.80E-05	0.05
19.04	4	23.28	6.072	1.12E-04	0.47
4.76	2.6	4.08	0.525	2.80E-05	0.04
19.04	2.6	15.64	4.416	1.12E-04	0.34
11.9	1.2	7.42	1.425	6.99E-05	0.11
11.9	4	11.71	2.6475	6.99E-05	0.20
11.9	2.6	11.46	1.965	6.99E-05	0.15
DSW-2
CD (mA cm^− 2)	pH	EC (kWh kg^− 1 COD^− 1)	C_energy (kWhm^− 3)	C_electrode (kg of Al m^− 3)	Operational Cost (USD m^− 3)
4.76	4	0.81	0.53	2.80E-05	0.04
19.04	4	6.83	4.68	1.12E-04	0.36
4.76	8	2.81	0.54	2.80E-05	0.04
19.04	8	10.38	5.94	1.12E-04	0.46
4.76	6	0.93	0.53	2.80E-05	0.04
19.04	6	7.22	5.54	1.12E-04	0.43
11.9	4	3.23	2.24	6.99E-05	0.17
11.9	8	4.95	2.40	6.99E-05	0.18
11.9	6	3.70	2.24	6.99E-05	0.17
CD: Current Density; IED = Inter-Electrode Distance

The optimization of the two wastewater is shown in Table S6. The optimization was carried out with the help of the software and is based on the criteria required at the end. In each case, all other parameters were kept in range, and the parameter was either maximized or minimized. In both the samples, the minimum cost criteria have the highest desirability of 0.951 and 1 in DSW-1 and DSW-2, respectively. However, both cases give the maximum COD removal of 41.20% and 39.84% in DSW-1 and DSW-2, respectively. The maximum COD removal criteria give out 68.19% and 86.19% COD removal in both wastewaters. The minimum cost and maximum COD removal criteria give the best combination and significant COD removal of 64.19% and 78.21%. It should be noted that earlier studies show optimized conditions based on maximum pollutant removal. The reason could be to meet the desired discharge standard. However, this study demonstrated that treatment costs could be optimized simultaneously with maximum COD removal based on wastewater management objectives.

Comparison of Energy consumption on both optimization

The results obtained from the two optimization studies were compared with respect to energy consumption for COD removal and related variations in the process parameters. The results illustrating the same have been shown in Fig. 5, depicting the effect of the operating parameters on the COD removal and energy expenditure for both optimization studies. The energy consumption and its associated COD removal as a function of varying IED and CD in the case of DSW-1 is shown in Fig. 5a. It can be concluded that the COD removal in each CD value was almost the same at all IED values. Therefore, for DSW-1, the constraint on IED is insignificant for different CD values. Also, the COD removal was in the range of 40–50% in CD values of 4.76 and 11.90 mA cm^− 2 and increased to more than 60% in the 19.04 mA cm^− 2 CD value. It can also be said from Fig. 5a that the difference in energy consumption was almost the same in 4.76 mA cm^− 2 at all IED values and shows an increasing trend in 11.9 and 19.04 mA cm^− 2 at all IED values. The graph also depicts that the energy consumption trend was steeper in a higher CD value of 19.04 mA cm^− 2 than 11.90 mA cm^− 2. As with higher IED values, maintaining the constant CD value is a little difficult due to an increase in ohmic overpotential, resulting in high energy consumption and operational costs (Pi et al., 2014; Wagle et al., 2020).

In the case of DSW-2, the COD removal trend with varying pH values is similar, corresponding to the CD values, except at the CD value of 19.04 mA cm^− 2, where the COD removal is marginally higher at 6 pH than the 4 pH value. Regarding energy consumption, the trends seem similar across pH variations corresponding to each CD value. The maximum COD removal and minimum energy consumption across pH variations in each CD were observed at 4 pH except at 19.04 mA cm^− 2 (6.75 to 9.99 kWh/kg). Also, energy consumption seems to increase with increased CD value as more current input leads to more energy consumption. The Figure also shows that at pH values of 4 and 6, the difference in energy consumption is negligible at all CD values. Although the case is opposite with 8 pH, the energy consumption is twice more than the pH 4 and 6 at CD value of 4.76 mA cm^− 2_, and the difference decreases at 11.90 mA cm^− 2 CD and then increases for 19.04 mA cm^− 2 CD value. The results depict that the optimum condition for energy consumption per percent COD removal is 11.90 mA cm^− 2 and 6 pH value.

The two optimization studies show that CD and pH parameters influence COD removal and energy consumption the most, whereas IED has the least impact. The maximum energy consumption value goes to 23.38 kWh kg^− 1COD^− 1 with COD removal of 65% in the case of DSW-1 while it reached 10.38 kWh kg^− 1COD^− 1 value in the case of DSW-2 having COD removal > 85%. It can be said that the critical parameters that require optimization for the electrocoagulation treatment process for drill site wastewater with high conductivity are pH and current density.

In this study, the performance of EC with aluminum electrodes was investigated with two different drill site wastewaters. Further, the treatment process was optimized with and without altering the system by changing its initial pH value, optimizing the process, and selecting the optimum parameters (CD, IED, and pH). The optimization process was carried out with the help of response surface methodology using design expert software. The study results showed that the current density significantly affects COD removal, followed by pH and inter-electrode distance, which has the least effect on COD removal. The results indicate that the inter-electrode distance minimizes the COD removal of high-conductivity wastewater. It was also demonstrated that significant COD removal was obtained at low pH and low current density of 4.76 mA cm^− 2. Furthermore, it was observed that the effective pH range of COD removal is 4–6. In the case of a CCV, high current and low pH contribute more to the CCV than the inter-electrode distance. Increasing the inter-electrode distance increases the CCV. The effect of these parameters on cost and energy consumption is also evaluated. The maximum cost was obtained at high CD and low pH in the case of DSW-1 and high CD and maximum value of inter-electrode distance in the case of DSW-2. The optimum value for maximum COD removal as determined by the response surface modeling is as follows: DSW-1, COD removal > 86% at 19.04 mA cm^− 2 and 5.4 pH, and for DSW-2, COD removal > 65% at 19.04 mA cm^− 2 and 2.6 cm inter-electrode distance. The energy consumption and cost analysis indicate that CD and pH play a significant role in optimizing the EC process and have a major impact on them. With IED and CD variation, the energy consumption reaches more than 20 kWh kg^− 1COD^− 1, while with CD and pH, it was 10.38 kWh kg^− 1COD^− 1. The optimum condition for max COD removal and min energy was observed at 4.76 mA cm^− 2 and 4 pH value with COD ⁓80%. The study first presents the optimization process for drill site wastewater treatment using RSM and further focuses on energy and cost evaluation. The study demonstrated the effectiveness of EC in treating oil drill site wastewater and depicts the significance of optimization for both process, energy, and cost optimization. This study could open the path for on-site drill site treatment and bring a holistic study on energy and cost optimization of the treatment process.

Author contribution Conceptualization: Pramod Kumar; Methodology: Pramod Kumar, Swatantra P Singh and Tabish Nawaz; Data collection, software use and formal analysis: Pramod Kumar; Writing the first draft of the manuscript: Pramod Kumar; Manuscript revision and editing: Pramod Kumar, Swatantra P Singh and Tabish Nawaz. All authors have read and agreed to the published version of the manuscript. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Acknowledgment

The work was supported financially by Center of Excellence- Oil, Gas and Energy, OIL India Limited, and ONGC India. The funders had no role in study design, data collection and analysis, decision to publish, or manuscript preparation. We would like to thank IRCC and IIT Bombay for their help in providing the platform for the research work.

Ethical approval and consent to participate: Not applicable.

Consent for publication: All authors agree to publish.

Competing interests: The authors declare no competing interests.

Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author upon request.

Funding

This work was supported by IRCC, IIT Bombay (Grant number: 0010014094). Swatantra P. Singh has received funding from Oil and Natural Gas Corporation and Oil India Limited

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Evaluation and optimization of electrocoagulation process parameters for the treatment of oil industry drilling site wastewater

Status:

Version 1

Abstract

Figures

Introduction

Material and methods

Analytical methods

Result and discussion

Impact of CD, pH, and IED

Combined effect of parameters on the treatment performance

Optimization of cost and energy consumption

Conclusion

Declarations

References

Supplementary Files

Status:

Version 1

At the anode	M(s)→ M³⁺(aq) + 3e⁻	(1)
At the cathode	3H₂O(l) + 3e− → \(\frac{3}{2}\)H₂ + 3OH⁻	(2)
In the solution	M³⁺(aq) + 3H2O → M(OH₃)(s) + 3H⁺(aq)	(3)