Introduction

Water scarcity is one of the biggest challenges facing not only third-world countries but also the whole world. One of the most important aspects related to the water quality issue is the dispersion of pollutants as a result of wastewater discharged into natural water bodies, in addition to mismanagement and water distribution (Adeniran et al. 2015). Turbidity is one of the very effective indicators for water quality, especially drinking water, and overall public health concern for wastewater discharge to bodies of water (Huey and Meyer 2010). Oily wastewaters result from various industries, petrochemicals, pharmaceuticals, and food sectors that produce or handle oil and pose a burden to the environment, particularly to the aquatic environment (ALFUTAISI et al. 2007; Abdelwahab et al. 2021; Abuhasel et al. 2021). The oil in the water (O/W) mixture may form a very stable emulsion, especially in the presence of water currents, which may cause vigorous stirring, making the emulsion more stable. In general the emulsions are difficult to break due to their stability and persistence, so their de-emulsification is critical to lower their risk to the environment, and thus allowing the oil to be recovered and water to be reused (Merma et al. 2020; Adetunji and Olaniran 2021). Numerous techniques were used for oily wastewater treatment to reduce its impact on human health and the environment such as, adsorption in nanoparticles, ultrafiltration techniques, chemical destabilization, biological treatment, and electrochemical processes (Mearns et al. 2020; Abdelwahab et al. 2021; Adetunji and Olaniran 2021). However, the majority of these processes are complex, energy-intensive, expensive, or time-consuming (Mearns et al. 2020).

Since it has many benefits, including simple equipment, a quicker reaction time, no chemical addition, low energy and electrode usage, electrocoagulation (EC) is one of the most effective electrochemical methods for treating oily wastewater. (AlJaberi et al. 2020a, b; Moneer et al. 2021). Although the electrodes must be cleaned and maintained regularly as part of the procedure, the stress on the electrodes may shorten their life duration, requiring them to be replaced more frequently. The material and design of the electrode, the spacing between the two electrodes, the polarity of the electrodes, the density of the current, the conductivity and pH of the wastewater, and the particle size are all factors that affect EC efficiency (Garcia-Segura et al. 2017).

When electric current passes through the electrodes, which are commonly constructed iron and/or aluminum, the EC mechanism is dependent on the deposition process and redox processes that occur in the reactor. When the anode dissolves, metal cations such as Fe2+ or Al3+ are liberated, while hydrogen gas and hydroxyl ions (OH) are produced at the cathode. Chemical interactions between various ions produce electrocoagulants such as Al(OH)3, (Eqs. (1)–(5)) (AlJaberi et al. 2020a, b)

$$ {\text{Al}}_{\left( s \right)} \Rightarrow {\text{Al}}^{3 + }_{{\left( {{\text{aq}}} \right)}} + 3{\text{e}}^{ - } $$
(1)
$$ 2{\text{H}}_{2} {\text{O}} \Rightarrow {\text{O}}_{2} + 4{\text{H}}^{ + } + 4{\text{e}}^{ - } $$
(2)
$$ 2{\text{H}}_{2} {\text{O}} + 2{\text{e}} ^{ - } \Rightarrow {\text{H}}_{2\left( g \right)} + 2{\text{OH}}^{ - }_{{\left( {{\text{aq}}} \right)}} $$
(3)
$$ {\text{Al}}^{3 + } + 3{\text{OH}}^{ - } \Leftrightarrow {\text{Al}}\left( {{\text{OH}}} \right)_{3} $$
(4)
$$ n {\text{Al}}\left( {{\text{OH}}} \right)_{3} \Rightarrow {\text{Al}}_{n} \left( {{\text{OH}}} \right)_{3n} $$
(5)

Electrocoagulants that are formed during the EC, i.e., Al(OH)3, have a greater ability than chemical coagulants to remove contaminants from various types of wastewater, and they are greater than the chemical coagulants that have a minimum solubility of the products in a certain pH range, which leads to an easy separation (AlJaberi et al. 2020a, b).

The response surface methodology (RSM) is a useful valuable statistical tool for building mathematical models for complex systems, analyzing the simultaneous impacts of independent factors and their interactions on case responses, and subsequently supplying the conditions necessary for the optimal responses. (Mehdipoor and Moosavirad 2020; Ramya Sankar and Sivasubramanian 2021). The Box–Behnken design (BBD) is a powerful design method used to optimize the experiment in RSM, and the produced model by BBD can be applied to predict the system’s response to new conditions.

In the present study, BBD for RSM was utilized to optimize and examine the effects of key parameters such as oil volume, temperature, initial pH, and treatment time (the independent variables) on the efficiency of the EC technique in oil removal from oil/water emulsion. This technique has a high impact on turbidity (Dawood and Li 2013; Yuksel et al. 2013; Núñez et al. 2019), and conductivity (Mahmood and Al-Musawi 2020; Pandey, and Thakur 2020). This paper describes using the EC approach for sustainable, cost-effective, efficient, and eco-friendly oily wastewater treatment using RSM. To authors’ knowledge, there are scarce number of studies concerning the removal of oily wastewater using this technique specifically, which is the main aim of this study. The experiments were carried out at the National Institute of Oceanography and Fisheries, Alexandria, Egypt (NIOF) in January 2022. In the present work, it was decided to investigate all the aspects of the EC technique as a practical and promising technique for wastewater treatment, specifically oily wastewater, by investigating the optimization of the operating conditions followed by using RSM to ensure the results and to obtain the equations that can conclude that the application of this treatment process under optimal operating conditions allows for achieving promised removal efficiencies, Moreover, a calculation of the total operating cost of the process was performed to evaluate the feasibility of this technique for oily wastewater treatment, and finally a comparison between the obtained results from the present work and that obtained in the literature about using the EC technique.

Materials and methods

Oily wastewater

Sunflower oil used for preparing synthetic oily wastewater was purchased from (ARMA, Egypt) (Fig. 1). The main physical characteristics of the used oil are presented in Table 1. The wastewater collected from (El-Umom drain, Alexandria, Egypt), Table 2 shows the major characteristics of the wastewater. Span 80 (Sorbitan Mono Oleate (C24H44O6)) was provided from Oxford Lab Chem, India. Sulfuric acid (H2SO4), and sodium hydroxide (NaOH) were obtained from Merck.

Fig. 1
figure 1

Sunflower Oil chemical structure

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Table 1 Physical properties of used oil
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Table 2 Main characteristics of wastewater
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Instrumentation

A pH meter (model AD 1080) was used to measure the pH, while Conductivity was measured using a LOVIBOND (Senso Direct 150) probe and turbidity was measured using LOVIBOND (TB 300 IR), a portable turbid meter.

Experimental set-up

The EC reactor characteristics are given as described in Table 3. The emulsion (oil, wastewater, and Span 80) was transferred to the chamber by mixing the desired concentration (ml L−1) of the oil with the wastewater sample. Then 0.5 ml of Span 80 dissolved in wastewater was added slowly while the mixture was being stirred to finally obtain the oily wastewater emulsion. To chemically stabilize the emulsion, an initial sample was taken for pH, conductivity, and turbidity measurements as initial values, and then the electrical current through the anode and cathode was connected to a DC power source (1.13 Am−2). To control the process current and potential, an ammeter and a voltmeter (Sunway, China) were connected to the chamber. At 750 rpm, a magnetic stirrer was used to create a homogeneous solution. Aluminum (Al) plates (97% purity, manufactured by the Egyptian Copper Company, Alexandria, Egypt) served as sacrificial electrodes in the EC reactor.

Table 3 Characteristics of EC Reactor
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The treatment performance was evaluated using conductivity changes and turbidity removal efficiencies that are calculated using Eq. (6)

$$\frac{{N}_{\mathrm{i}}-{N}_{\mathrm{f}}}{{N}_{\mathrm{i}}}\times 100$$
(6)

where Ni and Nf are the initial and final values of turbidity (mg L−1), and conductivity, respectively.

Experimental design

Minitab statistical software (Minitab, Version 17.0) was used to perform the experimental design, analysis of variance (ANOVA) mathematical modeling, and contour plots of the response surface. Four important functioning parameters; oil volume, temperature, initial pH, and treatment time were studied and optimized using the Box–Behnken model (BBM). The experimental design (based on BBM), fits a quadratic function to the studied experimental variables given by Eq. (7):

$$Y \left(\%\right)= \sum_{i-1}^{n}{B}_{i}{x}_{i}+\sum_{i-1}^{n}{B}_{{1}^{i}}{x}_{i}^{2}{i}_{1}^{-{r}^{1}\Sigma }$$
(7)

where β0 is an offset term, βi is the first-order or linear effect, βii is the second-order or quadratic effect, βij is the interaction effect between the coded variables, Xi is the experimental variables, n is the number of factors, and Y (%) the response given by the model. Three values for each operational variable were chosen. The inputs to obtain the experimental matrix are explained in Table 4.

Table 4 The experimental matrix for the four operational variables for RSM based on the Box–Behnken model
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However, according to these inputs, the software Minitab 17 gave as an output a matrix of 27 experiments shown in supplementary table, the authors preferred to follow a matrix of 63 experiments including the 27-proposed experiments. Finally, the software returned response surfaces for the turbidity removal (response 1) and conductivity changes (response 2) efficiencies within the area of interest.

The ANOVA was made for the analysis of data obtained from the operational runs. The significant level was established by values of the p test (p ≤ 0.05), and the fitting quality of the model was determined by the coefficients of determination (R2 and Adj R2). The contour plots were further used to represent the functional relationship between each response (turbidity and conductivity) and the four experimental factors (amount of oil, pH, temperature, and treatment time) and how the response reacts to changes in those experimental variables.

Results and discussion

Optimization conditions and verifications

Effect of reaction time

The formation of a sufficient amount of different ions from the electrodes, which are required to generate adsorbents, such as Al (OH)3 in aluminum electrodes, as well as the discharge gas bubbles from both electrodes, which essentially assist in bringing destabilized impurities to the surface of the solution through flotation and helping to remove them, are dependent on the electrolysis time (AlJaberi et al. 2020a, b). As for turbidity removal, it increases as the reaction time increases.

As the reaction time grows from 0 to 90 min, the effectiveness of oil removal improves. The removal efficiency increased from 94% at 30 min to about 97.30% at 90 min, Therefore, the higher the reaction time the higher the removal efficiencies of these pollutants from the contaminated solution due to the significant effects of the adsorption and desorption processes occurring throughout the EC cell as the electrolysis time extends (Nasrullah et al. 2020), the removal efficiencies of these impurities from the contaminated solution increased. However, this situation continued to a limited time at which the adsorption sites became saturated with the pollutants. At this point the removal efficiency stopped and started to decline. In the present work, after 90 min, the flocs reached a certain size, the large floc became unstable and brittle, and then began to break and the removal decreased. The obtained data matched those reported by (Ji et al. 2015).

Effect of initial pH

One of the most crucial factors impacting EC efficacy is the initial pH (Moneer et al. 2021). According to thermodynamic diagrams of aluminum species, polymeric cations along with a precipitate of aluminum hydroxide, are the dominant species in the pH range of 5 to 9.5, causing emulsion destabilization. Monomeric hydroxo- aluminum species are projected to be stable at lower pH levels. Cations such as Al3+, Al(OH)2+, and Al(OH)2+, for example, would accelerate emulsion instability. If the pH is more than 9.5, the main species are Al(OH)−4, but amorphous aluminum hydroxide is still less present, therefore raising the pH will reduce the precipitate's presence and, as a result, the oil removal effectiveness (Merma et al. 2020).

The starting pH was altered from 4 to 11 to investigate the effect of pH on the removal of greasy effluent (4, 7, and 11). The maximum turbidity removal and conductivity value were observed at pH 4, as shown in Figs. 2 and 3, which match the results obtained by (Tir and Moulai-Mostefa 2008). The destabilization begins as soon as the reactive phase is reached, and is aided by the precipitation of amorphous aluminum hydroxide and polymeric cations. It is thought that, the primary destabilization mechanism is through passing flocculation and sweep coagulation (Tezcan Un et al. 2009). The ultimate pH rises to around 7 when the original pH is acidic or near neutral. The final pH fell when the initial pH was alkaline due to the ongoing creation of hydroxyl anions at the cathode.

Fig. 2
figure 2

Effects of pH on turbidity at various pH levels; (oil volume = 10 ml L.−1, temp. = 24.5 °C, and coagulation time = 90 min)

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Fig. 3
figure 3

Effects of pH on conductivity at various pH levels; (oil volume = 10 ml L.−1, temp. = 24.5 °C, and coagulation time = 90 min)

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Effect of oil volume

The oil volume varied from 10 to 20 ml L−1. Turbidity was increased with the increase in oil volume, from 45.5 to 264 NTU. The higher the oil concentration, the lower the removal efficiency is. For a given dose of aluminum, there was a limit on oil concentration in the emulsion for treatment. The amount of aluminum to dissolve the emulsion was proportional to the concentration of the emulsified oil. According to Faraday's law, a constant amount of Al3+ was released into the mixture with an increasing initial amount of oily wastewater concentration at continuous electricity density and reaction time (Shokri and Fard 2022). As a result, a similar quantity of aluminum hydroxyl ions could be formed in the mixture, which was insufficient for pollutant adsorption. Concerning turbidity removal, it rise as the volume of oil drops.

Effect of temperature

The effect of temperature can be explained by considering the conductivity. At high temperatures, water conductivity improved, resulting in higher removal efficiency up to 28 °C and consequently lower energy consumption. Further increases in temperature above 28 °C, destabilized oil adsorption on hydroxide flocs, lowering removal effectiveness. The effect of reaction temperature on oil removal efficiency was studied by Kilany et al. 2020. They stated that the effectiveness of oil removal diminishes as temperature rises. The temperature has two opposing effects on oil removal efficiency. Among the advantages of rising temperature are: (i) decreasing solution viscosity and increasing the solubility of Al3+ as a result of which the concentration polarization and passivation tendency of the Al anode is reduced; (ii) increases collision frequency of oil and their Brownian movement. However, it enhances the solubility of Al(OH)3 in solution, besides morphing into a denser (less porous) structure with a lower adsorption capability. The disadvantages of increasing solution temperature exceed the benefits.

Analysis of variance (ANOVA)

The results of the performed experiments relating to the RSM design for turbidity removal efficiency and conductivity changes are presented in Table 4. The full quadratic model for the two responses is given by Eqs. (8), and (9), respectively:

For turbidity removal:

$$ \begin{aligned} = & \, - {122} - \, {\mathbf{0}}.{\mathbf{12}}({\mathbf{Oil}} \, {\mathbf{volume}}) + { 21}.{1}0 \, \left( {{\text{Temperature}}} \right) - { 18}.{71 }\left( {{\text{pH}}} \right) + \, {\mathbf{0}}.{\mathbf{854}} \, ({\mathbf{Time}}) \\ & + \, 0.00{3 }\left( {\text{Oil volume}} \right)^{{2}} - \, {\mathbf{0}}.{\mathbf{324}} \, \left( {{\mathbf{Temperature}}} \right)^{{\mathbf{2}}} + \, 0.{591 }\left( {{\text{pH}}} \right)^{{2}} - \, 0.00{219 }\left( {{\text{Time}}} \right)^{{2}} \\ & - \, {\mathbf{0}}.{\mathbf{323}} \, \left( {{\mathbf{Oil}} \, {\mathbf{volume}}*{\mathbf{Temperature}}} \right) \, + \, {\mathbf{0}}.{\mathbf{641}} \, ({\mathbf{Oil}} \, {\mathbf{volume}}*{\mathbf{pH}}) - \, 0.00{58 }\left( {{\text{Oil volume}}*{\text{Time}}} \right) \, \\ & + \, 0.0{3}0 \, \left( {{\text{Temperature}}*{\text{pH}}} \right) - 0.0{154 }\left( {{\text{Temperature}}*{\text{Time}}} \right) \, + \, 0.00{52 }\left( {{\text{pH}}*{\text{Time}}} \right) \\ \end{aligned} $$
(8)

For conductivity change:

$$ \begin{aligned} = & \, - {59}.{1 } + {2}.{93} \left( {{\text{Oil}} {\text{volume}}} \right) \, + {3}.{37} \left( {{\text{Temperature}}} \right) - {\mathbf{0}}.{\mathbf{50}} ({\mathbf{pH}}) + {\mathbf{0}}.{\mathbf{429}} ({\mathbf{Time}}) \\ & + 0.0{97} \left( {{\text{Oil}} {\text{volume}}} \right)^{{2}} - \, 0.0{579} \left( {{\text{Temperature}}} \right)^{{2}} + 0.{385} \left( {{\text{pH}}} \right)^{{2}} - 0.00{238} \left( {{\text{Time}}} \right)^{{2}} \\ & - 0.0{878} \left( {{\text{Oil}} {\text{volume}}*{\text{Temperature}}} \right) - {\mathbf{0}}.{\mathbf{499}} ({\mathbf{Oil}} {\mathbf{volume}}*{\mathbf{pH}}) + 0.00{45} \left( {{\text{Oil}} {\text{volume}}*{\text{Time}}} \right) \, \\ & + 0.0{41}\left( {{\text{Temperature}}*{\text{pH}}} \right) \, + 0.00{74} \left( {{\text{Temperature}}*{\text{Time}}} \right) \, - 0.0{216} \left( {{\text{pH}}*{\text{Time}}} \right) \\ \end{aligned} $$
(9)

The ANOVA results for turbidity removal and conductivity changes are summarized in Tables 5 and 6, respectively. As indicated in Table 5, the statistical analysis showed the significance of the linear terms (oil volume and treatment time), the square term (temperature)2, and cross-term interactions (oil volume × temperature, and oil volume × pH) in the model with Pr (probability) < 0.05 (Table 5). However, the coefficients corresponding to the rest of the terms were not significant (Pr > 0.05). Focusing on Table 6, the significance of the linear terms (of pH and treatment time), and cross-terms interactions (of oil volume × pH) in the model of conductivity changes, were observed. On other hand, the positive model terms indicate that the variable creates a positive effect on the process responses, while the negative sign suggests a reverse influence on the process, as shown in Figs. 4a, and 5a.

Table 5 Analysis of variance table (ANOVA) for response surface quadratic model of turbidity removal
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Table 6 Analysis of variance table (ANOVA) for response surface quadratic model of conductivity change
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Fig. 4
figure 4

a Main effects of operating variables (oil volume, temperature, pH, and treatment time) on turbidity removal efficiency, b the interaction plots of operation variables on turbidity removal efficiency

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Fig. 5
figure 5

a Main effects of operating variables (oil volume, temperature, pH, and treatment time) on turbidity removal efficiency, b the interaction plots of operation variables on conductivity changes efficiency

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For the maximum turbidity removal and conductivity changes, the optimization of the parameters was as follows: volume of the oil is 10.9 ml L−1, the temperature of 28 °C, pH should be 4, and the time of removal is 90 min. Consequently, the contour plots of the two responses were presented in Figs. 6a, and b, respectively.

Fig. 6
figure 6

The Response surfaces for the a turbidity removal and b conductivity change at optimization conditions

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The contour plot of the turbidity removal indicated that the highest removal was obtained when pH levels and volume of oil were low with high temperature and long treatment time (Fig. 6a). Alternatively, the high changes in conductivity were accompanied by increasing the oil volume (Fig. 6b).

Removal of pollution load groups

Oily wastewater results from anthropogenic activities especially in the industrial sector (Sanghamitra et al. 2021), which represents one of the most hazardous wastewaters to the environment, and its treatment has become a serious issue for the environment (Rahi et al. 2021). Adetunji and Olaniran 2021 mentioned that, the pollution load from oily wastewater treatment comes from Oil and Grease, COD, BOD, DO, and nutrient load. In the present study these main groups are analyzed for the samples before and after EC as mentioned in Table 7, which illustrates the percentage removal of oil and grease, COD, NO2, PO4, DO & BOD as 74, 76, 88, 98, 29, 49%, respectively, which indicates that, EC technique is a very promising, efficient, eco-friendly method for oily wastewater treatment.

Table 7 Percent removal of pollution load groups from samples before and after treatment
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Comparison with different electrodes in the percent of the turbidity removal

The oily wastewater treatment was compared with various types of wastewater treated by the EC process, and reported in the literature and is shown in Table 8. It also shows other wastewater treatment using EC in terms of electrode types, configurations, conditions, operating cost, types of wastewater treated and removal percent of turbidity.

Table 8 Comparison of using the EC technique for the removal of turbidity from wastewater treatment
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The electrode arrangement is an important factor for treatment efficiency. According to the EC configuration system, most of the researches used Mono-polar (MP) system. In EC monopolar mode all electrodes are connected to the power supply which makes reaction times and current densities higher than in bipolar mode, leading to higher operating costs also when applied at industrial scale. On the contrary, in bipolar mode only two electrodes are connected to the power supply, this kind of arrangement has the advantage of increasing the surface area for the reaction without any additional operating costs of electricity, lower reaction times, higher efficiency, and simplification of operation. For all these reasons, the present study has used a bipolar system for EC wastewater treatment.

In addition, Electrode material is one of the main factors affecting the efficiency of the EC. Various materials have been used depending on cost-effectiveness, availability and efficiency. The most widely used electrodes are iron and aluminum because they have proven effective, cheap, available and very good coagulants (Kobya et al. 2006). Several studies stated that, the removal efficiencies of Al are higher than that of Fe (Nawarkar and Salkar 2019). From the literature, Liu et al. 2019 investigated EC effectiveness of wastewater treatment using several types of electrodes and found that turbidity removal rates of electrodes Al–Al > Al–Fe > Fe–Al > Fe–Fe electrodes, 62.5%, 60.4%, 52.4%, and 49.8%, for these material configurations, respectively. Also, this study compared the two electrodes Al and Fe and discovered the floc generated from Fe electrodes are smaller and consequently less likely to coagulate and settle quickly, resulting in Fe electrodes that are less suitable for this wastewater treatment. For all these reasons Al-Al electrodes were used in the present study.

According to conditions, operating current density and pH are the main operational parameters controlling directly the efficiency of EC. These parameters affected the reaction times and influenced the dominant pollutant separation mode. Comparing the operating conditions of the present study with the literature (Table 8), it showed that the higher current densities increased the operating cost, and it may not be the most efficient mode of the running reactor. The optimal conditions involved an adjustment between current density, operational cost, and efficient use of pH solution (Garcia-Segura et al. 2017). From the table, it can be seen that the EC system in the present study consumes lower electricity which leads to more efficiency and lower operating cost.

Very few researchers have calculated the operating cost, which depends on electrode material price, electricity price in different countries, and type of pollutant removed (Kobya and Delipinar 2008). It was found that, the total operating cost for removing oil from oily wastewater treatment is very cheap in the local market (2.2 US$*m−3).

Operating cost

Equations (10) and (11) present the total operating cost (TOC), which includes specific energy consumption (W) and the cost of the aluminum electrode (ElC). In the present study, only W and ElC have been considered to evaluate the TOC (US$*m−3) for removing oil from oily wastewater (Ulu et al. 2015; Moneer et al. 2022)

$$\mathrm{EIC}=\frac{I\times Mw\times t}{n\times F\times V}$$
(10)
$$ {\text{TOC}} = \pounds{ W}+ {\text{EIC}} $$
(11)

where W represents the specific energy consumption (kW h kg−1), I represents the current density (A), t represents the reaction time (h), V represents the volume of solution (L), F represents the Faraday constant, n represents the number of transferred electrons (3 electrons) for Aluminum, and Mw represents the molecular mass of Aluminum. In May 2022, the prices of electrical energy for the industrial sector £ and Aluminum electrodes ß on the Egyptian market (in this study) were 0.055 US$*1 KWh−1 and 0.70 US$*1 m−3, respectively. The TOC (US$*m−3) for removing oil from oily wastewater was found to be (2.2 US$*m−3). In 2013, the TOC for the oil tanning industry in India was (6.28 US$*m−3) (Maha Lakshmi and Sivashanmugam 2013). On the other hand, for the Canadian market, in 2017 TOC for oil removal from food was (0.4 US$*m−3) (An et al. 2017).

Mechanism of oil removal

The adsorption of oil molecules on the produced flocs of aluminum hydroxide gelatinous suspension by charge neutralization between aluminum hydroxide and the functional groups of oil molecules is seen in Fig. 7; according to Eq. 12 (Shokri and Fard 2022)

Fig. 7
figure 7

Schematic diagram of oil removal mechanism by electrocoagulation technique

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$$\mathrm{Oil} -{\mathrm{H}}_{\left(\text{aq.}\right)} +\left(\mathrm{OH}\right){\mathrm{OAl}}_{\left(s\right)}\to \mathrm{Oil} - {\mathrm{OAl}}_{\left(s\right)} + {\mathrm{H}}_{2}{\mathrm{O}}_{(l)}$$
(12)

Conclusion

Oily wastewater was treated by EC with aluminum electrodes in a batch bi-polar system. It was concluded that, the process is very efficient and economical for removing oil from oily wastewater using aluminum electrodes. The economic study indicated that, the process is very cheap under the Egyptian market prices compared to other countries. TOC (US$*m−3) for removing oil from oily wastewater was (2.2 US$*m−3). The significant independent variables and their interaction were assessed using ANOVA. The efficiency of the treatment was done by using a statically model of optimal conditions, effects of treatment time, initial pH, oil volume, and Temperature. The EC process under optimal operating conditions oil volume of 10 ml L−1, temperature of 28 °C, initial pH of 4, and coagulation time interval of 90 min, lead to a maximum removal efficiency of 97% and 73.4% for turbidity and conductivity, respectively.

Finally, the treated wastewater showed remarkable changes in the efficiency of removal of the main oily wastewater pollution load for COD, NO2, PO4, DO & BOD which are 74, 76, 88, 98, 29, and 49%, respectively. That indicates that, EC is a very promising, efficient, and eco-friendly method for oily wastewater treatment.