Electrocoagulation has several disadvantages, such as electrode passivation, generation of heat due to energy consumption, and significant sludge formation. The constraints restrict its application in the treatment of tannery effluent. Therefore, the introduction of alternating pulse current electrocoagulation (APC-EC) aimed to resolve these concerns. We conducted an empirical examination of the research, specifically focusing on the frequency and stirring speed. This study investigated the impact of different parameters on the effectiveness of removing chemical oxygen demand (COD), the turbidity of trivalent chromium, and the energy consumption of perforated aluminium (Al) electrodes. The implementation of the central composite design (CCD) within the surface response design technique has enhanced multiple operational parameters in the APC-EC process for the treatment of tannery effluent. By employing our advanced mathematical and statistical techniques, we successfully eliminated the highest levels of COD, chromium (III) ion, and turbidity, all while significantly reducing energy usage. As a result, we achieved the optimal conditions for our process. The components that were eliminated most rapidly at 11000 Hz, 576 rpm, and 30 minutes were COD (70.3%), turbidity (96%), and 89.56%. The surface response results provide a description of the frequency dynamics of APC-EC.

  • Innovative alternating pulse current (APC-EC) solution surpasses direct current EC limits in tannery wastewater treatment.

  • Optimization via central composite design enhances frequency, stirring speed, and reaction time.

  • Notable chemical oxygen demand and Cr+3 removal lowered energy use with inventive electrode approach.

  • Research showcases APC-EC's superior efficiency and sustainability.

  • APC-EC introduces a breakthrough method for efficient tannery effluent treatment.

The leather sector has a prominent place in the world economy. Within the domain of the leather industry, the raw hides obtained from the animal sector undergo a sequence of mechanical and chemical alterations (Costa et al. 2008; Benhadji et al. 2011; Elabbas et al. 2016). In this context, raw hides, which are a perishable byproduct of the cattle sector, undergo a process of conversion into goods that exhibit both durability and economic value. However, within the context of leather manufacture, a wide range of waste is produced as a result of the transformative process, appearing as solid, liquid, and gaseous byproducts throughout the manufacturing process. The resulting byproducts display variation depending on the characteristics of the products, the methods used, and the materials used as inputs. Undoubtedly, the wastewater generated because of numerous manufacturing operations is a significant challenge within the leather sector. The correlation between the amount of wastewater produced and the volume of water used inside a given facility is commonly recognized. The use of different chemical compounds during all stages of processing, including cleaning and preserving untreated animal skins, results in the generation of wastewater with varying compositions in terms of quality and quantity (Feng et al. 2007; Isarain-Chávez et al. 2014; Deghles & Kurt 2016). The characteristics of wastewater generated by the leather industry vary depending on the specific technology applied. The watery effluents that arise from the process of immersion and conditioning consist of many substances, including sodium chloride, hemoglobin, feces, earth matter, epidermal remnants, colloidal particles, and calcium chloride (Cassano et al. 2001; Kumar & Mani 2007; Sekar et al. 2009). The effluents produced as a consequence of the fleshing process include significant amounts of oil-grease, suspended particles, and chemical oxygen demand (COD). The effluent that is produced as a consequence of the unhairing and liming process shows increased concentrations of lime, sulfur, organic compounds, and both dissolved and suspended particulate matter (Bajza & Vrcek 2001; Abreu & Toffoli 2009; Hammami et al. 2009). The lime wash and bating wastewaters demonstrate a substantial concentration of ammonia. The effluents generated during the bating process also include the remaining enzymes used. In the absence of recycling, the degreasing process generates wastewaters that demonstrate significant levels of oil content, alkaline potassium permanganate demand, and COD. The use of sodium sulfide in degreasing processes is widespread, and it is important to highlight that the effluents produced during these processes might include different levels of sulfur (Font et al. 1996). The solution that is formed after the pickling procedure shows a notable level of solutes, which is evident from the existence of dissolved solids (Kanth et al. 2009). The chemical composition of wastewater generated by tanneries varies depending on the exact kind of substrate used in the tanning process. Currently, chromium (Shaalan et al. 2001; Fahim et al. 2006) is the primary agent used in tanneries. The complex nature of this specific wastewater composition presents a potential risk to both human well-being and environmental integrity upon its introduction into ecosystems. The primary cause of this phenomenon may be linked to the potential for chromium (III) to undergo oxidation to chromium (VI) or the presence of azo dyes (Dixit et al. 2015). However, it has been demonstrated in mammalian in vitro investigations that trivalent chromium demonstrates potential toxicity as a result of its function as a competitive inhibitor in many cellular processes (Walsh et al. 1994). In this specific context, the conventional biological treatment method often fails to achieve a satisfactory level of effectiveness due to the harmful characteristics of tannery effluent, which negatively affects the growth and reproduction of bacteria. Furthermore, traditional physicochemical techniques are characterized by a comparatively elevated cost and possess the capacity to produce secondary contaminants. The inclusion of additional chemical constituents is necessitated by the stipulated criteria (Pandey & Thakur 2020).

The restrictions noted above have motivated several businesses to investigate effective alternative treatment methods for the removal of contaminants, with a preference for electrochemical approaches. In contemporary times, electrochemical treatment approaches have reached a state of progress whereby they demonstrate not only cost comparability with other technologies but also higher efficiency and effectiveness (Chen 2004). In the present day, there has been much research conducted on electrochemical technologies in the field of environmental applications, specifically on the treatment of water and wastewater in industrialized countries. One of the procedures used for wastewater treatment is electrocoagulation (EC) due to its inherent benefits, such as its simplicity, dependability, and cost-effectiveness in operation. EC has garnered significant attention owing to its potential applications in the leather industry (Deghles & Kurt 2016; Garcia-Segura et al. 2017). The EC process (Chen 2004; Jiménez et al. 2012) involves several important phenomena. At the interfaces of the electrodes, electrochemical reactions occur. Within the aqueous medium, coagulants are produced. These coagulants then adsorb soluble or colloidal pollutants. Finally, the pollutants are removed through sedimentation and flotation mechanisms. Equations (1) and (2) illustrate the production of ions and oxygen by anodic processes.

At the anode:
formula
(1)
formula
(2)
At the cathode in a basic medium, the reaction that takes place is the formation of hydrogen bubbles according to Equation (3):
formula
(3)

Due to the pH value being approximately 4, aluminum (Al) does not remain in cationic form. The cations formed react with water to form complexes of the type , , or the poorly soluble hydroxide .

These species play the role of coagulant by neutralizing the negative charges on the surface of the colloids.
formula
(4)
formula
(5)

The metal cations, specifically aluminum ions , undergo sacrificial oxidation in the aqueous medium. This process facilitates the removal of undesired impurities, such as pollutants, through either chemical reactions leading to precipitation or by inducing the fusion of colloidal materials (flocs). Subsequently, these fused materials can be eliminated via electrolytic flotation with the assistance of hydrogen gas generated during the cathodic reaction.

Nevertheless, in the process of EC utilizing direct current (DC), it is observed that following a specific duration, a non-permeable oxide layer is generated on the anodic surface. This hinders the efficient propagation of electrical currents within the system, resulting in elevated power expenditures and diminished efficacy. This issue is exacerbated when utilizing an aluminum electrode. In a broader context, the augmented utilization of the electrode and electrical energy, along with the escalated generation of sludge, are prominent drawbacks associated with this approach. In the realm of EC, it is customary for power consumption expenditures to surpass 50% when considering the overall expenses of the unit (Mollah et al. 2001). Hence, the enhancement of the electrode processes employed in EC is of utmost importance. While the antecedent electrochemical-based treatability investigations have made significant contributions to this domain, the majority of them solely focus on elucidating the impact of prevailing process conditions on the elimination of a particular contaminant from diverse wastewater sources. To the utmost extent of the authors' understanding, there exists a conspicuous void in the pertinent domain pertaining to the exploration of groundbreaking and economically viable EC setups aimed at mitigating the drawbacks linked to energy usage and waste management of sludge. Based on the available literature, it appears that there is a scarcity of comprehensive publications focused on the investigation of a comparative optimization of tanning wastewater treatment using response surface methodology (RSM). In this context, the current investigation was used to address the gap mentioned previously by focusing on a detailed quantitative analysis of the alternating pulse current EC (APC-EC) process within the experimental domain of the pivotal variables impacting this process, such as frequency, stirring speed, and treatment time, with the aim of achieving minimal energy utilization and maximal removal. RSM was employed for the optimization and analysis of tannery wastewater treatment performance.

Considering the aforementioned evidence, the current investigation introduces a pioneering technique known as APC-EC as a means to address the drawbacks associated with DC-EC. The application of an alternating pulse circuit at the output of the DC source enables the conversion of the DC into a pulsating, alternating current. Utilizing the prevailing supply model at specific temporal intervals, the direction of electric current is altered, thereby inducing a corresponding modification in the functional role of the electrodes. The pulsating current and alternating electrode role in this process serve the dual purpose of preventing anode electrode passivation and allowing ample time for coagulants to fully engage in the coagulation reaction during the off-time intervals between each pulse. Consequently, this results in a decrease in energy consumption (Koparal et al. 2008). The utilization of electrical currents is observed in conjunction with square, sinusoidal, and triangular waveforms during the process of pulsed electrolysis. Nevertheless, it is worth noting that pulsed currents featuring square wave forms, both symmetrically and asymmetrically, are more extensively employed in the realm of electrolysis (Sahay & Kushwaha 2017).

The study aimed to improve tannery wastewater treatment by introducing APC-EC to overcome issues seen in conventional DC-EC. These issues include electrode passivation, high energy consumption, heat generation, and excessive sludge. Experimental analysis focused on factors like frequency, stirring speed, and reaction time. The study aimed to optimize the APC-EC process through statistical methods, achieving maximum removal of COD, and turbidity, while minimizing energy use.

Characterization of tannery wastewater

The wastewater produced by a tannery facility located in Damascus, Syria, was collected and afterward subjected to several analyses to determine its properties. These analyses included measuring the pH level, chemical COD, concentration of Cr+3, total dissolved solids (TDS), and turbidity. The specific findings obtained from these analyses are shown in Table 1.

Table 1

Characteristics of wastewater produced by tannery plant

NoParametersValueUnit
pH 3.85 – 
COD 8,970 mg/L 
 190 mg/L 
TDS 18,500 mg/L 
Turbidity 142 NTU 
NoParametersValueUnit
pH 3.85 – 
COD 8,970 mg/L 
 190 mg/L 
TDS 18,500 mg/L 
Turbidity 142 NTU 

Reagents

This research used a range of reagents to determine the COD. Potassium dichromate (K2Cr2O₇) was used as the oxidizing agent because of its strong oxidizing characteristics, which are necessary for the decomposition of the organic components in the sample. Sulfuric acid (H2SO4) and hydrochloric acid (HCl) were used to modify the pH levels and improve the reaction conditions for the oxidation process. Acetone was essential in purifying the electrodes, guaranteeing precise readings by eliminating any possible impurities. The conversion of organic molecules into carbon dioxide (CO2) and water (H2O), which is a crucial step in properly measuring COD levels, was facilitated by the use of silver sulfate (AgSO4) and mercury(II) sulfate (HgSO4) as catalysts. The back-titration procedure required the use of ferrous ammonium sulfate as the titrant to accurately measure the quantity of the oxidizing agent that has undergone a reaction. The selection of ferroin as the indicator is based on its distinct color transition at the titration endpoint, which greatly aids in determining the endpoint of the titration. Pure distilled water was used in all studies to guarantee the integrity of the solutions and the dependability of the outcomes.

Equipment used in the experimental tests

The research included the use of an electrical generator, namely, the Model LRS-150-12 manufactured by MEAN WELL in China. The COD samples were dissolved in a thermo reactor Model 149 H1839800, manufactured by HANNA Instruments in the United States. The concentration of COD was determined using a COD meter and multiparameter photometer Model 150 HI83099, both manufactured by HANNA Instruments. The concentration of Cr (III) in the supernatant liquid was determined by using a spectrophotometer Model U-2900 (HITACHI, Japan). An essential part of the rig setup to calculate the turbidity removal percentage is the turbidity meter. Efficiency (Φ) is a key metric used to assess the effectiveness of a treatment or process, expressed as a percentage. It is determined by comparing the actual, experimentally measured change in mass or concentration of a substance after treatment (Δmexp) with the expected change under ideal conditions, as predicted by theory or models (Δmtheo). The current efficiency (Φ) is a quantification of the leading indicators of an electrolysis process, which is determined by the use of Equation (6).
formula
(6)

In the above equation, Φ represents the current efficiency expressed as a percentage, whereas and denote the actual and theoretical dissolving mass of the electrode in grams, respectively.

The comparison facilitates the measurement of the effectiveness of operations such as the elimination of pollutants or the decrease in COD in the field of environmental management. The equipment used includes the HACH Model 2100P pH meter from the United States, the Jenway Model 3,540 conductivity meter from England, the INGCO Model DM7502 digital multimeter from China, and a manually made pulse supply.

Experimental procedures

The EC reactor used in this research was constructed using acrylic material and has the following dimensions: 10 cm length, 15 cm width, and 20 cm height. Two planar sheets with rectangular shapes were used as the cathode and anode electrodes. The dimensions of each sheet were 9.5 cm in width, 10 cm in length, and 0.2 cm in thickness. These sheets were purchased from China and had a purity level of 99.5%. The sheets were perforated with circular holes, each measuring 0.2 cm in diameter, and were made of aluminum. The electrodes were arranged in a vertical configuration, with a gap of 1 centimeter between them. The objects were situated at a vertical distance of 0.5 centimeters from the lowermost part of the cell. The electrodes were connected to the pulser, which was in turn connected to a DC electric generator. The DC was injected into the pulser, where it underwent a conversion process to create an output current. This output current was then delivered to the electrodes. The selection of a duty cycle of 50% in this experimental approach was informed by previous scientific studies (Martiningsih et al. 2017; Zhang et al. 2021). The slurry for the coagulation procedure was created using a magnetic stirrer. During the experimental protocol, the electrodes were exposed to a potential difference, while the ensuing currents were measured using a voltmeter and an ammeter. The aforementioned measurements played a pivotal role in validating the attainment of the intended electrical current and guaranteeing the effective implementation of the procedure. During the first stage, the electrodes underwent abrasion using sandpaper to eliminate any adsorbed contaminants. Subsequently, the electrodes were washed by immersing them in a hydrochloric acid solution with a concentration of 10%. This was followed by treatment with acetone and distilled water, and eventually, the electrodes were dried by desiccation at a temperature of 100 °C. The weights of the plates made of aluminum were measured both before and after each electrochemical experiment.

Costs and efficiencies estimation

An electrolytic cell is linked to a pulse power supply to create an electrolysis system, where the process of electrolysis occurs in pulses. The duration of the current from the on mode to the off mode is referred to as , which represents the pulse duration or pulse width. This is the period of time during which electrolysis is actively taking place. The duration ‘t’ between the transition from the off mode to the on mode is referred to as the intermittent period of electrolysis or pulse interval. The frequency pulse EC technique utilizes a specific power source that generates three distinct parameters: pulse power or current amplitude, pulse width , and pulse interval . By adjusting these parameters, one can achieve enhanced decontamination and energy-saving effects.

The energy usage of DC-EC and APC-EC was determined using Equations (7) and (8), respectively.
formula
(7)
where is the energy consumption , U is the cell voltage (V), I is the current (A), is the EC time (h), v is the volume of the treated wastewater , and is the duty cycle (%).
formula
(8)

The variable denotes the theoretical electrode consumption in . The variable t represents the duration of the treatment in seconds.

The determination of the consumed mass of electrodes may be achieved by experimental means by quantifying the disparity in mass between the electrodes before and after each run. The theoretical electrode consumption was calculated by Equation (9).
formula
(9)
where corresponds to the molecular weight in units of , with a specific value of 26.98 g/mol for Al. The variable z signifies the number of electrons involved, which is 3 for aluminum. Finally, F represents Faraday's constant with a value of 96,485 C/mol.
The period is the ratio of the (pulse width) to the pulse period as seen in Equation (10):
formula
(10)
The pulse frequency was determined by the pulse on–off period Equation (11).
formula
(11)
The initial on time is defined as the time in which the electrochemical reaction starts. The off time is the time in which the electrochemical reaction stops. The pulse cycle (T) is equal to the sum of and , and f (Hz) represents the frequency of the pulses. The duty ratio γ (%) is the ratio of the working time to the pulse cycle. The current waveform is shown in Figure 1(a).
Figure 1

Experimental setup for APC-EC: (a) schematic; (b) actual: (1) DC power supply with pulse supply; (2) duty cycle control; (3) frequency control; (4) wire; (5) magnetic stirrer; (6) heat control; (7) speed control; (8) perforated aluminum electrodes; (9) EC cell; (10) tap; (11) tannery wastewater; (12) digital multimeter.

Figure 1

Experimental setup for APC-EC: (a) schematic; (b) actual: (1) DC power supply with pulse supply; (2) duty cycle control; (3) frequency control; (4) wire; (5) magnetic stirrer; (6) heat control; (7) speed control; (8) perforated aluminum electrodes; (9) EC cell; (10) tap; (11) tannery wastewater; (12) digital multimeter.

Close modal

Response surface methodology

The field of design of experiment (DoE) comprises a wide range of mathematical approaches that are very beneficial for statistical modeling and systematic analysis of a given subject matter. The optimization of intended responses or measures of output is accomplished by using variables or factors (Asaithambi et al. 2016; Ghelich et al. 2019). RSM is a frequently used approach in the field of DoE for the construction of models. It serves as a sequential approach that effectively enhances the design and formulation of innovative items, ultimately increasing their overall performance (Asaithambi et al. 2012; Ghelich et al. 2019). Furthermore, the RSM is widely used as an empirical statistical technique for the purpose of constructing mathematical models, optimizing trials involving several factors, and examining the relationships between the response variable and the explanatory variables (Pavlović et al. 2014). The RSM offers a significant benefit compared to the usual strategy of analyzing one variable at a time due to its capacity to reduce the required number of experimental operations, thereby saving time. The achievement of RSM is facilitated via the simultaneous interaction of factors and the modeling of specific response parameters. As a result, the use of RSM facilitates a more expeditious and methodical investigation of the many factors implicated (Ghelich et al. 2019). The present study used the RSM with the aid of the Design-Expert program, which was created by Stat-Ease Inc. (Pavlović et al. 2014). This facilitated the development of a pragmatic and efficient association between the selected replies and the control variables. The experimental methodologies were devised in line with the concepts of the catalytic chemical decomposition (CCD). The use of RSM was implemented in a series of five consecutive steps described below (Bezerra et al. 2008):

  • 1. The process involves the identification of several elements that have an impact on the intended results, as well as the determination of the minimum and maximum values associated with each of these factors. The pH and conductivity reader measures the pH values that result from electrode dissociation, without the addition of any chemicals to manipulate the pH. The characteristics that were chosen to impact the effectiveness of APC-EC include frequency, stirring speed, and response time. The lower and higher limits of the variables were established based on the results of our first tests conducted on parameters such as COD, turbidity, removal effectiveness, final pH, consumption of energy, and electrode usage. The characteristics have substantial economic significance within electrochemical processes and, as a result, were included as the variables that are dependent (responses).

  • 2. The determination of the DoE and the analysis of the experimental trials are influenced by the design approach. The second step of the RSM involves the careful development of an experimental design model. To achieve this objective, it is possible to use a model that incorporates both first-order and second-order kinetics. As explicated in the scholarly literature, researchers have often shown a preference for the utilization of second-order symmetrical designs, such as the three-level factorial design (Missaoui et al. 2013), the central composite layout (Gengec et al. 2013; Tahir 2019), and the Box–Behnken design (Mehralian et al. 2021). Variability in the selection of experimental sites, the number of levels for factors, and the number of runs is seen across different design models. The selected approach for this research was the CCD, which included lower and higher levels for each variable inside Design-Expert program. The variables were analyzed over five levels, which were designated as -α, −1, 0, +1, and +α. The determination of the α level for each variable is dependent on the number of variables (k), and may be achieved by using Equation (12) (Bezerra et al. 2008).
    formula
    (12)
  • 3. The software was used to compute the α levels. Table 2 presents the predetermined values for the experimental design of the three components, namely, frequency, stirring speed, and treatment duration. The program calculated the total number of experiments using a particular formula.
    formula
    (13)
    where k is the number of variables and cp corresponds to the number of the central points (Bezerra et al. 2008; Pandey & Thakur 2020). The CCD for three experimental variables comprised 20 experiments. Each experiment was conducted in the sequence listed in Table 3 and performed only once. By repeating the experiments six times, the results can achieve better precision due to averaging the inherent variability in the measurements.
  • 4. The statistical and mathematical evaluation of experimental data involves the use of polynomial functions to accurately represent and understand the acquired results. A series of 20 tests were conducted based on the specified circumstances outlined in Tables 3 and 4. DoE software recorded the values for the proportion of COD, , turbidity, final pH, and energy and electrode usage for each experiment. The experimental data were analyzed using the Design-Expert software (commercial package) and fitted to a second-order polynomial model. Subsequently, regression coefficients were derived from the analysis. The response variable (chromium ions removal %, turbidity reduction %, COD removal %, and energy consumption), which is denoted by (y), was used to assess the second-order polynomial equation, which represents the relationship between the chosen variables (x) and their interactions (independent variables multiplication in regression equation and quadratic terms), as described by Equation (14) (Mondal et al. 2012; Assémian et al. 2018). The assessment of model adequacy was conducted using analysis of variance (ANOVA). The regression coefficients and were used to ascertain the most suitable polynomial model, while the F-test was utilized to examine the statistical significance. The statistical regression of full quadratic modes is mentioned below:
    formula
    (14)
    where β0, βi, βii, and βij are the regression coefficients for intercept, linear, quadratic, and interaction terms, respectively.
  • 5. The optimal levels of each variable are achieved to obtain the appropriate levels of replies. In the field of numerical optimization, the selection process involves strategically choosing desired objectives for each component, along with corresponding responses, from the various options available within the software interface. Following this, the computer seeks to identify the optimal configurations that maximize the desirability function (Pavlović et al. 2014). The optimal objective for each variable was selected from the range specified in Tables 3 and 4. There are several response choices including maximizing removal efficiency and minimizing energy consumption (Khataee et al. 2010).

Table 2

Experimental design levels for frequency, stirring speed, and reaction time

Predicted and actual variable
VariableUnitFactorsα-lowLowCenterHighα-high
   −1.68 −1 +1 +1.68 
Frequency Hz  272.8 3,000 7,000 11,000 13,727.2 
Stirring speed rpm  127.2 400 800 1,200 1,472.7 
Reaction time min  9.8 15 22.5 30 35.1 
Predicted and actual variable
VariableUnitFactorsα-lowLowCenterHighα-high
   −1.68 −1 +1 +1.68 
Frequency Hz  272.8 3,000 7,000 11,000 13,727.2 
Stirring speed rpm  127.2 400 800 1,200 1,472.7 
Reaction time min  9.8 15 22.5 30 35.1 
Table 3

Comparison of actual and predicted removal percentages

RunFrequency (Hz)Stirring speed (rpm)Reaction time (min)COD removal (%)
Removal (%)
Turbidity removal (%)
Actual valuePredicted valueActual valuePredicted valueActual valuePredicted value
7,000 800 22.5 58.6 57.6 69.1 68.5 75.5 76.0 
3,000 400 30 63.2 64.4 87.0 87.3 85.0 82.1 
11,000 400 30 65.0 64.4 90.8 90.5 94.0 93.2 
3,000 1,200 30 65.8 64.9 95.0 92.3 93.0 92.5 
7,000 800 22.5 57.4 57.6 67.8 68.5 76.2 76.0 
13,727.2 800 22.5 50.2 50.4 71.7 72.3 80.0 80.2 
11,000 1,200 15 36.1 35.6 46.1 45.7 47.0 49.7 
7,000 1,472.7 22.5 52.8 53.4 61.0 64.6 70.0 69.7 
272.8 800 22.5 53.6 52.4 66.0 65.2 71.0 71.4 
10 3,000 1,200 15 36.7 38.0 40.0 40.4 50.0 50.5 
11 3,000 400 15 34.2 35.2 24.1 27.5 32.0 34.1 
12 7,000 800 22.5 57.9 57.4 68.0 68.7 76.7 76.5 
13 7,000 800 22.5 57.1 57.1 69.8 68.5 74.8 76.0 
14 7,000 800 22.5 57.2 57.6 65.6 68.2 75.9 76.4 
15 7,000 800 9.8 25.0 23.4 24.6 20.9 30.0 26.6 
16 7,000 800 35.1 70.0 70.7 95.0 98.6 95.0 98.8 
17 11,000 1,200 30 63.2 62.8 81.7 78.4 90.0 87.6 
18 11,000 400 15 33.5 35.0 47.0 49.8 49.0 49.3 
19 7,000 800 22.5 58.0 57.6 70.6 68.5 77.0 76.0 
20 7,000 127.2 22.5 54.0 52.4 67.5 63.9 60.0 60.7 
RunFrequency (Hz)Stirring speed (rpm)Reaction time (min)COD removal (%)
Removal (%)
Turbidity removal (%)
Actual valuePredicted valueActual valuePredicted valueActual valuePredicted value
7,000 800 22.5 58.6 57.6 69.1 68.5 75.5 76.0 
3,000 400 30 63.2 64.4 87.0 87.3 85.0 82.1 
11,000 400 30 65.0 64.4 90.8 90.5 94.0 93.2 
3,000 1,200 30 65.8 64.9 95.0 92.3 93.0 92.5 
7,000 800 22.5 57.4 57.6 67.8 68.5 76.2 76.0 
13,727.2 800 22.5 50.2 50.4 71.7 72.3 80.0 80.2 
11,000 1,200 15 36.1 35.6 46.1 45.7 47.0 49.7 
7,000 1,472.7 22.5 52.8 53.4 61.0 64.6 70.0 69.7 
272.8 800 22.5 53.6 52.4 66.0 65.2 71.0 71.4 
10 3,000 1,200 15 36.7 38.0 40.0 40.4 50.0 50.5 
11 3,000 400 15 34.2 35.2 24.1 27.5 32.0 34.1 
12 7,000 800 22.5 57.9 57.4 68.0 68.7 76.7 76.5 
13 7,000 800 22.5 57.1 57.1 69.8 68.5 74.8 76.0 
14 7,000 800 22.5 57.2 57.6 65.6 68.2 75.9 76.4 
15 7,000 800 9.8 25.0 23.4 24.6 20.9 30.0 26.6 
16 7,000 800 35.1 70.0 70.7 95.0 98.6 95.0 98.8 
17 11,000 1,200 30 63.2 62.8 81.7 78.4 90.0 87.6 
18 11,000 400 15 33.5 35.0 47.0 49.8 49.0 49.3 
19 7,000 800 22.5 58.0 57.6 70.6 68.5 77.0 76.0 
20 7,000 127.2 22.5 54.0 52.4 67.5 63.9 60.0 60.7 
Table 4

Experimental and predicted results of electrochemical treatment parameters and their effects on process efficiency

RunFrequency (Hz)Stirring speed (rpm)Reaction time (min)Final
Current density
Electrodes consumption (
Energy consumption
Actual valuePredicted valueActual valuePredicted valueActual valuePredicted valueActual valuePredicted value
7,000 800 22.5 5.4 5.3 21.0 20.4 0.3 0.3 2.9 3.0 
3,000 400 30 5.4 5.3 24.5 24.3 0.5 0.5 5.2 5.0 
11,000 400 30 5.5 5.4 14.7 16.5 0.3 0.4 3.1 3.0 
3,000 1,200 30 5.6 5.5 24.5 24.3 0.5 0.5 5.2 5.0 
7,000 800 22.5 5.3 5.3 21.0 20.4 0.3 0.3 2.9 3.0 
13,727.2 800 22.5 5.2 5.2 16.7 13.8 0.3 0.2 2.0 1.8 
11,000 1,200 15 4.9 4.9 14.7 16.5 0.2 0.1 1.5 1.6 
7,000 1,472.7 22.5 5.3 5.3 21.0 20.4 0.3 0.3 2.9 3.0 
272.8 800 22.5 5.1 5.1 25.0 26.9 0.4 0.4 4.1 4.2 
10 3,000 1,200 15 4.8 4.8 24.5 24.3 0.3 0.3 2.6 2.5 
11 3,000 400 15 4.7 4.7 24.5 24.3 0.3 0.3 2.6 2.5 
12 7,000 800 22.5 5.3 5.3 21.0 20.4 0.3 0.3 2.9 3.0 
13 7,000 800 22.5 5.3 5.3 21.0 20.4 0.3 0.3 2.9 3.0 
14 7,000 800 22.5 5.4 5.3 21.0 20.4 0.3 0.3 2.9 3.0 
15 7,000 800 9.8 4.5 4.5 21.0 20.4 0.1 0.1 1.3 1.3 
16 7,000 800 35.1 5.7 5.7 21.0 20.4 0.5 0.5 4.6 4.7 
17 11,000 1,200 30 5.7 5.6 14.7 16.5 0.3 0.4 3.1 3.0 
18 11,000 400 15 4.8 4.8 14.7 16.5 0.1 0.1 1.5 1.6 
19 7,000 800 22.5 5.3 5.3 21.0 20.4 0.3 0.3 2.9 3.0 
20 7,000 127.2 22.5 5.0 5.0 21.0 20.4 0.3 0.3 2.9 3.0 
RunFrequency (Hz)Stirring speed (rpm)Reaction time (min)Final
Current density
Electrodes consumption (
Energy consumption
Actual valuePredicted valueActual valuePredicted valueActual valuePredicted valueActual valuePredicted value
7,000 800 22.5 5.4 5.3 21.0 20.4 0.3 0.3 2.9 3.0 
3,000 400 30 5.4 5.3 24.5 24.3 0.5 0.5 5.2 5.0 
11,000 400 30 5.5 5.4 14.7 16.5 0.3 0.4 3.1 3.0 
3,000 1,200 30 5.6 5.5 24.5 24.3 0.5 0.5 5.2 5.0 
7,000 800 22.5 5.3 5.3 21.0 20.4 0.3 0.3 2.9 3.0 
13,727.2 800 22.5 5.2 5.2 16.7 13.8 0.3 0.2 2.0 1.8 
11,000 1,200 15 4.9 4.9 14.7 16.5 0.2 0.1 1.5 1.6 
7,000 1,472.7 22.5 5.3 5.3 21.0 20.4 0.3 0.3 2.9 3.0 
272.8 800 22.5 5.1 5.1 25.0 26.9 0.4 0.4 4.1 4.2 
10 3,000 1,200 15 4.8 4.8 24.5 24.3 0.3 0.3 2.6 2.5 
11 3,000 400 15 4.7 4.7 24.5 24.3 0.3 0.3 2.6 2.5 
12 7,000 800 22.5 5.3 5.3 21.0 20.4 0.3 0.3 2.9 3.0 
13 7,000 800 22.5 5.3 5.3 21.0 20.4 0.3 0.3 2.9 3.0 
14 7,000 800 22.5 5.4 5.3 21.0 20.4 0.3 0.3 2.9 3.0 
15 7,000 800 9.8 4.5 4.5 21.0 20.4 0.1 0.1 1.3 1.3 
16 7,000 800 35.1 5.7 5.7 21.0 20.4 0.5 0.5 4.6 4.7 
17 11,000 1,200 30 5.7 5.6 14.7 16.5 0.3 0.4 3.1 3.0 
18 11,000 400 15 4.8 4.8 14.7 16.5 0.1 0.1 1.5 1.6 
19 7,000 800 22.5 5.3 5.3 21.0 20.4 0.3 0.3 2.9 3.0 
20 7,000 127.2 22.5 5.0 5.0 21.0 20.4 0.3 0.3 2.9 3.0 

Removal analysis

The present study involved data collection focused on pollutant concentrations, subsequently analyzed according to the methodologies outlined by Solak et al. (2009) and Hasani et al. (2019). The analysis included calculating the percentage of COD removal, Cr+3 removal percentage, and the reduction of turbidity level. These calculations were performed using the following equations:
formula
(15)
where and are the COD at initial and end reaction time, respectively.
formula
(16)
where and are chromium concentrations (mg/L) at initial and end reaction time, respectively.
formula
(17)
where and are turbidity registered at initial and end reaction time, respectively.

Removal efficiency of COD, , turbidity, energy consumption and electrodes, and final

Tables 3 and 4 provide the removal efficiency of COD, Cr+3, turbidity reduction, and the energy consumption using Al–Al perforated electrodes. The results in these tables also include the corresponding projected values obtained by RSM, along with the final pH and current density. Adjusted coefficient of determination (R2) is a modified version of R2 that has been adjusted for the number of predictors in a regression model. It is used to determine how well a regression model fits the data while taking into account the number of predictors in the model. Unlike R2, which can only increase or stay the same as more predictors are added, can decrease if the addition of a predictor does not improve the model enough to offset the penalty for adding another variable.

Table 3 presents a comprehensive comparison between the actual and expected removal percentages for COD, Cr3+ (chromium), and turbidity in several experimental trials. The results demonstrate a robust association between the observed and projected values, emphasizing the model's precision in predicting treatment effectiveness. Modifications in frequency, agitation velocity, and duration of the reaction had a substantial effect on the rates of removal, demonstrating patterns that align with theoretical predictions. For example, when the frequencies and stirring speeds were raised, there was generally a noticeable improvement in the removal of pollutants. This was evident from the greater percentages of COD and Cr3+ removal, as well as the decreased turbidity. The most significant improvements were reported in experiments with extended reaction durations, particularly in run 16, which achieved a maximum of 70% COD reduction and over 95% removal of chromium ions (Cr3+). Nevertheless, the results also reveal a phenomenon of diminishing returns in certain instances, when an increase in reaction speed or frequency did not lead to significantly improved outcomes. This indicates that optimizing these parameters is essential for maximizing the effectiveness of pollution removal without needless resource consumption.

Figures 35 demonstrate the impact of frequency and reaction duration on many parameters, including pollutant removal efficiency, final pH, energy consumption, and electrode usage. These measurements were taken at a stirring speed of 800 rpm. A range of frequencies from 3,000 to 11,000 Hz was tested, while reaction times varied from 15 to 30 min. An increase in frequency resulted in enhanced pollutant removal efficacy and an elevation in the final pH. Notably, the highest levels of COD and turbidity removal were attained at a frequency of 7,400 Hz. This phenomenon indicates that the utilization of high frequencies might lead to irregular electrode reactions, particularly when there is a power interruption. The current in an electrolytic cell is comprised of the movement of ions facilitated by an electric field during its operational duration. During moments of electric current deactivation, the concentration of ions undergoes a fast reduction, leading to a swift restoration and renewal of ion concentration. Hence, the current density increases in the pulsed current mode compared to a continuous current source, hence amplifying the efficacy of EC when using alternating pulsed current. Periodic pulse switching refers to the EC process where a forward pulse (cathodic pulse) is followed by a backward pulse (anodic pulse). Applying a pulsed signal with periodic switching promotes simultaneous coagulation of metal ions and colloids. Coagulation between metal ions and colloids occurs as a result of the recurrent changes in the polarity of the electrodes. Simultaneously, the recurrent changes in the polarity of the electrodes play a beneficial role in mitigating electroplating.
Figure 2

The comparison between actual and predicted values for various responses: (a) COD removal %, (b) removal %, (c) turbidity reduction %, and (d) energy consumption.

Figure 2

The comparison between actual and predicted values for various responses: (a) COD removal %, (b) removal %, (c) turbidity reduction %, and (d) energy consumption.

Close modal
Figure 3

Percentage removal: (a) COD, (b) , (c) turbidity, and (d) final pH using a combination of frequency and reaction time (stirring speed: 800 rpm).

Figure 3

Percentage removal: (a) COD, (b) , (c) turbidity, and (d) final pH using a combination of frequency and reaction time (stirring speed: 800 rpm).

Close modal
Figure 4

Percentage removal: (a) COD, (b) , (c) turbidity, and (d) final pH using a combination of frequency and stirring speed (reaction time: 30 min).

Figure 4

Percentage removal: (a) COD, (b) , (c) turbidity, and (d) final pH using a combination of frequency and stirring speed (reaction time: 30 min).

Close modal
Figure 5

Surface response plot of the effect of frequency and reaction time on the energy consumption (stirring speed: 800 rpm).

Figure 5

Surface response plot of the effect of frequency and reaction time on the energy consumption (stirring speed: 800 rpm).

Close modal

Table 4 demonstrates the impact of different electrochemical treatment parameters on the effectiveness of the pollutant removal procedure. The characteristics encompass stirring velocity, frequency, duration of reaction, ultimate pH, current density, electrode usage, and energy expenditure. The stirring speeds ranged between 127.2 and 1,472.7 revolutions per minute (rpm), while the frequency varied from 272.8 to 13,727.2 Hz. Increasing the stirring speeds often enhances the development of flocs by assuring even distribution of ions, which facilitates the aggregation of particles into larger flocs that can be easily removed. The reaction times observed in the studies varied between 9.8 and 35.1 min. Extended reaction durations often result in improved pollutant removal as they provide more chances for ion interaction and the development of flocs. The pH values at the end of the procedure varied between 4.5 and 5.7, which is the ideal range. This range affects the charge and solubility of ions, ultimately affecting the effectiveness of floc production. The variation in current density, ranging from 14.7 to 25.0 mA/cm2, has a direct impact on the production of ions and the creation of flocs. Increasing current densities can expedite the creation of ions, which in turn enhances the formation of flocs. However, this can lead to higher energy consumption without a substantial improvement in pollution removal. The electrode consumption exhibited a range of 0.1–0.5 kg/m3, but the energy consumption had a variation between 1.3 and 5.2 kWh/m3. The current density and reaction time have an impact on these parameters, where greater values are associated with increased current densities and longer reaction times. Efficient floc production, which improves the removal of pollutants while minimizing resource consumption, is achieved by optimal ion replenishment, as indicated by current density and pulse power supply mode, as well as stirring speed. The implementation of the pulse power supply mode reduces electrolysis duration while preserving high current efficiency, leading to decreased energy and electrode usage, enhanced electrolysis efficiency, and low heating of the adapter. Moreover, a higher current density and larger electroactive regions result from perforations in the electrodes, and sharp edges increase the likelihood of distortion. This phenomenon is well recognized within the field of corrosion research. The primary method by which the speciation and solubility of Al are regulated in the aluminum agglomerated coagulant-electrocoagulation (APC-EC) process involves the hydrolysis and agglomeration reactions of trivalent aluminum ions . In a theoretical context, the presence of several aluminum hydroxide species, namely, (which is preferred for the coagulation process), , (also preferred for the coagulation process), , and , may be theoretically seen within the pH range of 3–5 (Lu et al. 1999; Bingül et al. 2023). The concentration of aluminum hydroxide decreased and was transformed into the aluminum hydroxide ion when the pH decreased below 7 (Lu et al. 1999; Bingül et al. 2023). According to the study findings, the optimal pH for achieving the maximum removal of COD and was determined to be 5.6 by comparing Tables 3 and 4. Hence, it can be deduced that the observed pH level is optimal for conducting EC due to the predominant formation of aluminum complexes (coagulants) in this pH range (Lu et al. 1999; Bingül et al. 2023).

Figure 3(d) demonstrates that when the frequency rises, the pH of the solution typically increases. This is primarily caused by the heightened generation of hydroxide ions (OH⁻) at the cathode. The natural increase in pH allowed for the most effective elimination of pollutants, eliminating the need for any additional pH changes. The untreated solution maintained an appropriate pH range for efficient elimination of pollutants, indicating that the electrochemical process automatically regulated the pH to favorable levels for treatment.

Reference studies indicate that acidic environment is more favorable because it leads to the reduction of Cr (VI) to Cr (III) under acidic conditions due to the high proton supply (Atba et al. 2023).
formula
(18)
Subsequently, formation of hydroxide ions at the cathode increases the pH of the tannery wastewater, and Cr (III) precipitates in the form of a hydroxide (Verma Shiv et al. 2013).
formula
(19)
In addition, the high removal rates of COD, Cr3+, and turbidity at acidic pH levels suggest that pollutant removal is not solely due to the anodic oxidation of the aluminum electrode in the APC-EC process. It also indicates a possible role for indirect oxidation, likely facilitated by the presence of oxidants such as hypochlorite or chlorine. The chloride anions that are found in the effluent from tanneries (as shown in Table 1) have the potential to undergo oxidation, resulting in the formation of chlorine, as shown by Equation (20). The aforementioned process may produce hypochlorous acid (HClO) according to Equation (21) and hypochlorite ions according to Equation (22).
formula
(20)
formula
(21)
formula
(22)

These particular species have the ability to participate in the oxidation of soluble organic matter due to their elevated oxidative potentials (Güven et al. 2009; Verma Shiv et al. 2013).

Figure 4 demonstrates the collective influence of speed and frequency on both removal efficiencies. The most prominent influence of stirring speed is seen in mass transfer. The findings show that the removal of COD, , and turbidity increases marginally as the mixing speed increased. In the literature, similar findings have been reported (Camcioglu et al. 2017). The augmentation of stirring speed facilitates the removal of gas bubbles that accumulate on the surface of the electrode. This increase in turbulence leads to a reduction in the thickness of the diffusion layer at the electrode surface and subsequently reduces cell resistance (Sridhar et al. 2012). One additional objective of the stirring process is to ensure the even dispersion of iron-based coagulants throughout the reaction. The uniformity of temperature and pH levels inside the reactor is achieved by the stirring action of the electrolyte. Elevated stirring rates, on the other hand, possess the capacity to disintegrate the aggregates formed inside the reactor, resulting in the formation of minuscule aggregates that pose difficulties in their separation from water (Bayar et al. 2011; Parsa et al. 2011).

With the third parameter (stirring speed) set to 30 min, graphs were used to illustrate the combined effects of stirring speed (400–1,200 rpm) and frequency (3,000–11,000 Hz) on the removal efficiencies of COD, Cr+3, and turbidity. These results indicate that to effectively remove when the COD is at its peak, an average frequency of 7,000 Hz and a stirring speed exceeding 685 rpm are required. It has also been demonstrated that while the frequency helps reduce energy consumption, the stirring speed does not have a significant impact on energy usage.

In addition, a decrease in frequency leads to an increase in sludge production. The results indicate that there was an increase in the removal percentages of COD, , and turbidity from 71.6, 89.51, and 89.52% to 72.82, 89.32, and 95.58% respectively, when the stirring speed was raised from 400 to 800 rpm at a frequency of 7,000 Hz. Specifically, the energy consumption for a reaction period of 30 min and a mixing speed of 400 rpm dropped from 4.9 to and the consumption of electrodes decreased from 0.47 to as the frequency increased from 3,000 to 11,000 Hz.

Model validation

The model outcome reveals that the R2 and adjusted R2 for COD removal are 0.9951 and 0.9907, respectively. Based on the ANOVA analysis of current study, it can be observed that the interplay between frequency, stirring speed, and reaction time has a significant impact on the parameters of COD, turbidity, and energy usage.

In the present work, the degree of confidence level is 95%. As seen in Figure 2, the model's predicted values align closely with the experimental data, exhibiting a proximity to the diagonal line across all sites. The ANOVA analysis revealed that the quadratic models provide statistical significance (p-value < 0.05) and may be used to predict the percentage of COD (which is mentioned in the Supplementary material), , and turbidity removal, as well as the energy consumption (Khan et al. 2019; Ano et al. 2020).

Obtained regression correlations

Equations (23)–(26) describe the quadratic regression models used for evaluating various parameters in the study. Equation (23) details the COD removal percentage, considering factors like frequency (A), stirring speed (B), and reaction time (C). The model incorporates linear, interaction, and quadratic terms to capture their effects on COD removal. Equation (24) focuses on the removal percentage of Cr+3. Turbidity reduction is addressed in Equation (25). Finally, Equation (26) presents the energy consumption in kW h/m3, influenced by the frequency, stirring speed, and reaction time, emphasizing the significant role of these variables in the overall energy efficiency of the process. Figure 2 displays a comparison of the predicted and actual data for four distinct parameters: percentage of COD removal, Cr(III) removal, turbidity reduction, and consumption of energy. Each subplot displays a scatter plot that represents the actual values on the x-axis and the predicted values, obtained from the regression equations, on the y-axis. The presented data exhibit a close agreement with the diagonal line in each subplot, suggesting a strong relationship between the predicted and actual values.
formula
(23)
formula
(24)
formula
(25)
formula
(26)

Combination of operating parameters

The percentage removals of COD, , and turbidity are assessed in relation to energy consumption and electrode consumption. Various factors that influence these responses (frequency, reaction time, and stirring velocity) were taken into account, and the impacts of different variables are graphically represented in Figures 35 using RSM. The influence of operational settings on predicting the highest percentage removal of COD, , turbidity, as well as energy consumption and electrode consumption was also examined. The COD, , and turbidity removal efficiencies exhibited an upward trend with the progressive extension of electrolysis duration and the utilization of low frequency. Simultaneously, the energy consumption and electrode performance experienced a notable surge in response to the prolonged electrolysis period, as visually depicted in Figure 3. In Figures 4 and 5, increasing the stirring speed from 500 to 700 rpm and the frequency from 6,000 to 8,000 Hz led to improvements in pollutant removal, including a higher percentage of COD removal, increased Cr3+ concentration reduction, improved turbidity removal, and reduced energy consumption.

The surface response curve depicted in Figure 5 offers valuable insights into the impact of frequency and reaction time on energy consumption during the electrochemical treatment process, while maintaining a stirring speed of 800 rpm. The results indicate that raising the frequency from 3,000 to 11,000 Hz results in a significant decrease in energy usage, suggesting that higher frequencies improve the energy efficiency of the treatment process. However, there is a clear relationship between reaction time and energy use. Longer reaction times, which can range from 9 to 33 min, lead to higher energy usage. The relationship between frequency and reaction time indicates that higher frequencies consistently reduce energy consumption across different reaction times, highlighting the significance of optimizing frequency to enhance the overall efficiency of the operation.

Optimization with RSM

One of the primary benefits of RSM in the context of CCD is the attainment of optimal conditions for the elimination of pollutants and energy utilization. According to the CCD, the outcomes were optimized by employing the regression equations (Equations (23)–(26)). To optimize the process, the Design-Expert software explores the design space, taking into consideration various constraints and limitations. To achieve the authentic maxima or minima, multiple randomized initial points are selected. Each process variable and response variable must be assigned a predetermined target value (Khan et al. 2019).

During the optimization process, the variables of frequency, stirring speed, and reaction time were carefully chosen within their respective ranges. The objective was to maximize the responses, including the percentage of COD, concentration of , and efficiency of turbidity removal, while simultaneously minimizing energy consumption. Based on the given operating parameters, the optimal value was achieved at a frequency of 11,000 Hz, a stirring speed of 576 rpm, and a time of 30 min. The optimal values of COD removal, Cr3 removal, turbidity reduction are 70, 90, and 96% and for energy and electrode consumption are 3 kW h=m3 and 0:35 kg=m3, respectively.

This investigation focused on assessing the efficacy of the APC-EC technique employing perforated aluminum electrodes for the elimination of COD, , and turbidity in tannery wastewater. The study also examined the influence of various process parameters, including frequency, stirring speed, and reaction time, on the percentage of removal for COD, , and turbidity, as well as the energy consumption. This analysis was conducted using RSM with Central Composite Design. The analysis of the experimental results, as determined by ANOVA, revealed that RSM proved to be a practical and suitable approach for optimizing the process variables. The optimal removal efficiencies achieved for COD, Cr3+, and turbidity were 72.70, 90.09, and 95.21%, respectively. In terms of energy and electrode consumption per unit volume, the performance figures were 4.3 kWh/m3 and 0.47 kg/m3, respectively. The obtained results were acquired under these experimental conditions: a frequency of 5,900 Hz, a stirring speed of 830 rpm, and a reaction time of 30 min. To optimize the removal efficiencies of COD, it is imperative to augment the applied frequency, while acknowledging that such augmentation may lead to a concomitant reduction in energy consumption. Present research highlighted that pH level adjustments were unnecessary, as the baseline pH of the tannery effluent was carefully maintained during all experiments due to controlled electrode dissolution. The study also demonstrated that factors like reaction time, frequency, and stirring speed significantly influenced various responses, including the removal efficiencies of turbidity, COD, and Cr3+, as well as pH stability, energy consumption, and electrode material usage. Therefore, the APC-EC technique has considerable promise for practical wastewater treatment applications due to its efficacy, cost-effectiveness, and robustness of the used electrodes. The ANOVA table (Analysis of Variance) indicates a p-value less than 0.05 degree of confident level, which is presented in the Supplementary material.

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.

Abreu
M. A.
&
Toffoli
S. M.
2009
Characterization of a chromium-rich tannery waste and its potential use in ceramics
.
Ceramics International
35
(
6
),
2225
2234
.
https://doi.org/10.1016/j.ceramint.2008.12.011
.
Ano
J.
,
Henri Briton
B. G.
,
Kouassi
K. E.
&
Adouby
K.
2020
Nitrate removal by electrocoagulation process using experimental design methodology: A techno-economic optimization
.
Journal of Environmental Chemical Engineering
8
(
5
),
104292
.
https://doi.org/10.1016/j.jece.2020.104292
.
Asaithambi
P.
,
Garlanka
L.
,
Anantharaman
N.
&
Matheswaran
M.
2012
Influence of experimental parameters in the treatment of distillery effluent by electrochemical oxidation
.
Separation Science and Technology
47
(
3
),
470
481
.
https://doi.org/10.1080/01496395.2011.621160
.
Asaithambi
P.
,
Aziz
A. R. A.
&
Daud
W. M. A. B. W.
2016
Integrated ozone – Electrocoagulation process for the removal of pollutant from industrial effluent: Optimization through response surface methodology
.
Chemical Engineering and Processing: Process Intensification
105
,
92
102
.
https://doi.org/10.1016/j.cep.2016.03.013
.
Assémian
A. S.
,
Kouassi
K. E.
,
Drogui
P.
,
Adouby
K.
&
Boa
D.
2018
Removal of a persistent dye in aqueous solutions by electrocoagulation process: Modeling and optimization through response surface methodology
.
Water, Air, & Soil Pollution
229
(
6
),
184
.
https://doi.org/10.1007/s11270-018-3813-2
.
Atba
W.
,
Mouna
C.
,
Azzeddine
G.
,
Laefer
D. F.
&
Sabir
H.
2023
Effect of electrocoagulation parameters on chromium removal, sludge settling, and energy consumption
.
Analytical and Bioanalytical Electrochemistry
15
(
3
),
166
183
.
Bajza
Z.
&
Vrcek
I. V.
2001
Water quality analysis of mixtures obtained from tannery waste effluents
.
Ecotoxicology and Environmental Safety
50
(
1
),
15
18
.
https://doi.org/10.1006/eesa.2001.2085
.
Bayar
S.
,
Yıldız
Y. Ş.
,
Yılmaz
A. E.
&
İrdemez
Ş
.
2011
The effect of stirring speed and current density on removal efficiency of poultry slaughterhouse wastewater by electrocoagulation method
.
Desalination
280
(
1
),
103
107
.
https://doi.org/10.1016/j.desal.2011.06.061
.
Benhadji
A.
,
Taleb Ahmed
M.
&
Maachi
R.
2011
Electrocoagulation and effect of cathode materials on the removal of pollutants from tannery wastewater of Rouïba
.
Desalination
277
(
1
),
128
134
.
https://doi.org/10.1016/j.desal.2011.04.014
.
Bezerra
M. A.
,
Santelli
R. E.
,
Oliveira
E. P.
,
Villar
L. S.
&
Escaleira
L. A.
2008
Response surface methodology (RSM) as a tool for optimization in analytical chemistry
.
Talanta
76
(
5
),
965
977
.
https://doi.org/10.1016/j.talanta.2008.05.019
.
Bingül
Z.
,
Irdemez
Ş.
&
Demircioğlu
N.
2023
Effect of controlled and uncontrolled pH on tannery wastewater treatment by the electrocoagulation process
.
International Journal of Environmental Analytical Chemistry
103
(
16
),
4269
4284
.
https://doi.org/10.1080/03067319.2021.1925261
.
Camcioglu
S.
,
Ozyurt
B.
&
Hapoglu
H.
2017
Effect of process control on optimization of pulp and paper mill wastewater treatment by electrocoagulation
.
Process Safety and Environmental Protection
111
,
300
319
.
https://doi.org/10.1016/j.psep.2017.07.014
.
Cassano
A.
,
Molinari
R.
,
Romano
M.
&
Drioli
E.
2001
Treatment of aqueous effluents of the leather industry by membrane processes: A review
.
Journal of Membrane Science
181
(
1
),
111
126
.
https://doi.org/10.1016/S0376-7388(00)00399-9
.
Chen
G.
2004
Electrochemical technologies in wastewater treatment
.
Separation and Purification Technology
38
(
1
),
11
41
.
https://doi.org/10.1016/j.seppur.2003.10.006
.
Costa
C. R.
,
Botta
C. M. R.
,
Espindola
E. L. G.
&
Olivi
P.
2008
Electrochemical treatment of tannery wastewater using DSA® electrodes
.
Journal of Hazardous Materials
153
(
1
),
616
627
.
https://doi.org/10.1016/j.jhazmat.2007.09.005
.
Deghles
A.
&
Kurt
U.
2016
Treatment of tannery wastewater by a hybrid electrocoagulation/electrodialysis process
.
Chemical Engineering and Processing: Process Intensification
104
,
43
50
.
https://doi.org/10.1016/j.cep.2016.02.009
.
Dixit
S.
,
Yadav
A.
,
Dwivedi
P. D.
&
Das
M.
2015
Toxic hazards of leather industry and technologies to combat threat: A review
.
Journal of Cleaner Production
87
,
39
49
.
https://doi.org/10.1016/j.jclepro.2014.10.017
.
Elabbas
S.
,
Ouazzani
N.
,
Mandi
L.
,
Berrekhis
F.
,
Perdicakis
M.
,
Pontvianne
S.
,
Pons
M. N.
,
Lapicque
F.
&
Leclerc
J. P.
2016
Treatment of highly concentrated tannery wastewater using electrocoagulation: Influence of the quality of aluminium used for the electrode
.
Journal of Hazardous Materials
319
,
69
77
.
https://doi.org/10.1016/j.jhazmat.2015.12.067
.
Fahim
N. F.
,
Barsoum
B. N.
,
Eid
A. E.
&
Khalil
M. S.
2006
Removal of chromium(iii) from tannery wastewater using activated carbon from sugar industrial waste
.
Journal of Hazardous Materials
136
(
2
),
303
309
.
https://doi.org/10.1016/j.jhazmat.2005.12.014
.
Feng
J. W.
,
Sun
Y. B.
,
Zheng
Z.
,
Zhang
J. B.
,
Li
S.
&
Tian
Y. C.
2007
Treatment of tannery wastewater by electrocoagulation
.
Journal of Environmental Sciences
19
(
12
),
1409
1415
.
https://doi.org/10.1016/s1001-0742(07)60230-7
.
Font
J.
,
Gutiérrez
J.
,
Lalueza
J.
&
Pérez
X.
1996
Determination of sulfide in the leather industry by capillary electrophoresis
.
Journal of Chromatography A
740
(
1
),
125
132
.
https://doi.org/10.1016/0021-9673(96)00098-2
.
Garcia-Segura
S.
,
Eiband
M. M. S. G.
,
de Melo
J. V.
&
Martínez-Huitle
C. A.
2017
Electrocoagulation and advanced electrocoagulation processes: A general review about the fundamentals, emerging applications and its association with other technologies
.
Journal of Electroanalytical Chemistry
801
,
267
299
.
https://doi.org/10.1016/j.jelechem.2017.07.047
.
Gengec
E.
,
Ozdemir
U.
,
Ozbay
B.
,
Ozbay
I.
&
Veli
S.
2013
Optimizing dye adsorption onto a waste-derived (modified charcoal ash) adsorbent using Box–Behnken and central composite design procedures
.
Water, Air, & Soil Pollution
224
(
10
),
1751
.
https://doi.org/10.1007/s11270-013-1751-6
.
Ghelich
R.
,
Jahannama
M. R.
,
Abdizadeh
H.
,
Torknik
F. S.
&
Vaezi
M. R.
2019
Central composite design (CCD)-response surface methodology (RSM) of effective electrospinning parameters on PVP-B-Hf hybrid nanofibrous composites for synthesis of HfB2-based composite nanofibers
.
Composites Part B: Engineering
166
,
527
541
.
https://doi.org/10.1016/j.compositesb.2019.01.094
.
Güven
G.
,
Perendeci
A.
&
Tanyolaç
A.
2009
Electrochemical treatment of simulated beet sugar factory wastewater
.
Chemical Engineering Journal
151
(
1
),
149
159
.
https://doi.org/10.1016/j.cej.2009.02.008
.
Hammami
S.
,
Ouejhani
A.
,
Bellakhal
N.
&
Dachraoui
M.
2009
Application of Doehlert matrix to determine the optimal conditions of electrochemical treatment of tannery effluents
.
Journal of Hazardous Materials
163
(
1
),
251
258
.
https://doi.org/10.1016/j.jhazmat.2008.06.124
.
Hasani
G.
,
Maleki
A.
,
Daraei
H.
,
Ghanbari
R.
,
Safari
M.
,
McKay
G.
,
Yetilmezsoy
K.
,
Ilhan
F.
&
Marzban
N.
2019
A comparative optimization and performance analysis of four different electrocoagulation-flotation processes for humic acid removal from aqueous solutions
.
Process Safety and Environmental Protection
121
,
103
117
.
https://doi.org/10.1016/j.psep.2018.10.025
.
Isarain-Chávez
E.
,
de la Rosa
C.
,
Godínez
L. A.
,
Brillas
E.
&
Peralta-Hernández
J. M.
2014
Comparative study of electrochemical water treatment processes for a tannery wastewater effluent
.
Journal of Electroanalytical Chemistry
713
,
62
69
.
https://doi.org/10.1016/j.jelechem.2013.11.016
.
Jiménez
C.
,
Sáez
C.
,
Martínez
F.
,
Cañizares
P.
&
Rodrigo
M. A.
2012
Electrochemical dosing of iron and aluminum in continuous processes: A key step to explain electro-coagulation processes
.
Separation and Purification Technology
98
,
102
108
.
https://doi.org/10.1016/j.seppur.2012.07.005
.
Kanth
S. V.
,
Venba
R.
,
Madhan
B.
,
Chandrababu
N. K.
&
Sadulla
S.
2009
Cleaner tanning practices for tannery pollution abatement: Role of enzymes in eco-friendly vegetable tanning
.
Journal of Cleaner Production
17
(
5
),
507
515
.
https://doi.org/10.1016/j.jclepro.2008.08.021
.
Khan
S. U.
,
Islam
D. T.
,
Farooqi
I. H.
,
Ayub
S.
&
Basheer
F.
2019
Hexavalent chromium removal in an electrocoagulation column reactor: Process optimization using CCD, adsorption kinetics and pH modulated sludge formation
.
Process Safety and Environmental Protection
122
,
118
130
.
https://doi.org/10.1016/j.psep.2018.11.024
.
Khataee
A. R.
,
Zarei
M.
&
Moradkhannejhad
L.
2010
Application of response surface methodology for optimization of azo dye removal by oxalate catalyzed photoelectro-fenton process using carbon nanotube-PTFE cathode
.
Desalination
258
(
1
),
112
119
.
https://doi.org/10.1016/j.desal.2010.03.028
.
Koparal
A. S.
,
Yildiz
Y. Ş.
,
Keskinler
B.
&
Demircioğlu
N.
2008
Effect of initial pH on the removal of humic substances from wastewater by electrocoagulation
.
Separation and Purification Technology
59
(
2
),
175
182
.
https://doi.org/10.1016/j.seppur.2007.06.004
.
Kumar
S. S.
&
Mani
A.
2007
Measurement of physical and transport properties of tannery effluent (soak liquor)
.
International Communications in Heat and Mass Transfer
34
(
3
),
339
346
.
https://doi.org/10.1016/j.icheatmasstransfer.2006.12.004
.
Lu
X.
,
Chen
Z.
&
Yang
X.
1999
Spectroscopic study of aluminium speciation in removing humic substances by al coagulation
.
Water Research
33
(
15
),
3271
3280
.
https://doi.org/10.1016/S0043-1354(99)00047-0
.
Martiningsih
W.
,
Alfanz
R.
,
Akbar
M.
,
Laksmono
J. A.
&
Meliana
Y. J. A. S. L.
2017
Effect of PWM signal on hydrogen production using Hoffman voltameter methods
.
23
(
12
),
11897
11901
.
https://doi.org/10.1166/asl.2017.10540
.
Mehralian
M.
,
Khashij
M.
&
Dalvand
A.
2021
Treatment of cardboard factory wastewater using ozone-assisted electrocoagulation process: Optimization through response surface methodology
.
Environmental Science and Pollution Research
28
(
33
),
45041
45049
.
https://doi.org/10.1007/s11356-021-13921-7
.
Missaoui
K.
,
Bouguerra
W.
,
Hannachi
C.
&
Hamrouni
B. C.
2013
Boron removal by electrocoagulation using full factorial design
.
Journal of Water Resource and Protection
5
(9).
https://doi.org/10.4236/jwarp.2013.59088
.
Mollah
M. Y. A.
,
Schennach
R.
,
Parga
J. R.
&
Cocke
D. L.
2001
Electrocoagulation (EC) – Science and applications
.
Journal of Hazardous Materials
84
(
1
),
29
41
.
https://doi.org/10.1016/S0304-3894(01)00176-5
.
Mondal
B.
,
Srivastava
V. C.
&
Mall
I. D.
2012
Electrochemical treatment of dye-bath effluent by stainless steel electrodes: Multiple response optimization and residue analysis
.
Journal of Environmental Science and Health, Part A
47
(
13
),
2040
2051
.
https://doi.org/10.1080/10934529.2012.695675
.
Pandey
N.
&
Thakur
C.
2020
Statistical comparison of response surface methodology–based central composite design and hybrid central composite design for paper mill wastewater treatment by electrocoagulation
.
Process Integration and Optimization for Sustainability
4
(
4
),
343
359
.
https://doi.org/10.1007/s41660-020-00123-w
.
Parsa
J. B.
,
Vahidian
H. R.
,
Soleymani
A. R.
&
Abbasi
M.
2011
Removal of acid brown 14 in aqueous media by electrocoagulation: Optimization parameters and minimizing of energy consumption
.
Desalination
278
(
1
),
295
302
.
https://doi.org/10.1016/j.desal.2011.05.040
.
Pavlović
M. D.
,
Buntić
A. V.
,
Mihajlovski
K. R.
,
Šiler-Marinković
S. S.
,
Antonović
D. G.
,
Radovanović
Ž
&
Dimitrijević-Branković
S. I.
2014
Rapid cationic dye adsorption on polyphenol-extracted coffee grounds – A response surface methodology approach
.
Journal of the Taiwan Institute of Chemical Engineers
45
(
4
),
1691
1699
.
https://doi.org/10.1016/j.jtice.2013.12.018
.
Sahay
P. P.
&
Kushwaha
A. K.
2017
Electrochemical supercapacitive performance of potentiostatically cathodic electrodeposited nanostructured MnO2 films
.
Journal of Solid State Electrochemistry
21
(
8
),
2393
2405
.
https://doi.org/10.1007/s10008-017-3574-7
.
Sekar
S.
,
Sivaprakasam
S.
&
Mahadevan
S.
2009
Investigations on ultraviolet light and nitrous acid induced mutations of halotolerant bacterial strains for the treatment of tannery soak liquor
.
International Biodeterioration & Biodegradation
63
(
2
),
176
181
.
https://doi.org/10.1016/j.ibiod.2008.08.005
.
Shaalan
H. F.
,
Sorour
M. H.
&
Tewfik
S. R.
2001
Simulation and optimization of a membrane system for chromium recovery from tanning wastes
.
Desalination
141
(
3
),
315
324
.
https://doi.org/10.1016/S0011-9164(01)85008-6
.
Solak
M.
,
Kılıç
M.
,
Hüseyin
Y.
&
Şencan
A.
2009
Removal of suspended solids and turbidity from marble processing wastewaters by electrocoagulation: Comparison of electrode materials and electrode connection systems
.
Journal of Hazardous Materials
172
(
1
),
345
352
.
https://doi.org/10.1016/j.jhazmat.2009.07.018
.
Sridhar
R.
,
Sivakumar
V.
,
Immanuel
V. P.
&
Maran
J. P.
2012
Development of model for treatment of pulp and paper industry bleaching effluent using response surface methodology
.
Environmental Progress & Sustainable Energy
31
(
4
),
558
565
.
https://doi.org/10.1002/ep.10581
.
Tahir
H.
2019
Modeling and optimization of electrocoagulation process for the removal of yellow145 dye based on central composite design
.
Pakistan Journal of Analytical & Environmental Chemistry
20
(
2
).
https://doi.org/10.21743/pjaec/2019.12.15
.
Verma Shiv
K.
,
Khandegar
V.
&
Saroha Anil
K.
2013
Removal of chromium from electroplating industry effluent using electrocoagulation
.
Journal of Hazardous, Toxic, and Radioactive Waste
17
(
2
),
146
152
.
https://doi.org/10.1061/(ASCE)HZ.2153-5515.0000170
.
Walsh
A. R.
,
Ohalloran
J.
&
Gower
A. M.
1994
Some effects of elevated levels of chromium (iii) in sediments to the mullet Chelon labrosus (R)
.
Ecotoxicology and Environmental Safety
27
(
2
),
168
176
.
https://doi.org/10.1006/eesa.1994.1014
.
Zhang
J.
,
Li
J.
,
Ma
C.
,
Yi
L.
,
Gu
T.
&
Wang
J.
2021
High-efficiency and energy-saving alternating pulse current electrocoagulation to remove polyvinyl alcohol in wastewater
.
RSC Advances
11
(
63
),
40085
40099
.
https://doi.org/10.1039/D1RA08093H
.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (CC BY 4.0), which permits copying, adaptation and redistribution, provided the original work is properly cited (http://creativecommons.org/licenses/by/4.0/).

Supplementary data