Electrooxidation and subcritical water oxidation hybrid process for pistachio wastewater treatment

Electrooxidation and subcritical water oxidation hybrid process for pistachio wastewater treatment | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Electrooxidation and subcritical water oxidation hybrid process for pistachio wastewater treatment Zelal Isik, Zhalaladdin Jabbiyev, Raouf Bouchareb, Yasin Ozay, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1479310/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract In this study, a hybrid system was used for pistachio wastewater treatment based on the electrooxidation treatment process and subcritical water oxidation. The effects of current density (50–150 A/m 2 ), experiments time (0–180 min), and wastewater initial pH (4-8) were optimized for maximum total phenols and chemical oxygen demand (COD) removal. The experimental study proved that the best conditions of current density and solution pH for COD and total phenol removal efficiencies were 150 A/m 2 and original pH of 5 giving removal efficiencies of 50% and 19.02%, respectively. The electrochemically pretreated wastewater was subjected to subcritical water oxidation (SWO). The effects of temperature (376–410 K), treatment time (26.4-93.65 min), and concentration of H 2 O 2 (0.08-0.92 M) were examined for COD and total phenol removal. The experimental and predicted COD and total phenol removal of the central composite design (CCD) models demonstrated that temperature, treatment time and concentration of H 2 O 2 have highly affected each of COD and total phenol removals. The highest removal efficiencies were as much as 54.3% and 76.87% for COD and total phenol at the following optimum conditions: temperature of 403 K, 40 min of treatment time and 0.75 M of H 2 O 2 concentration. The SWO results showed that, although treatment time affects the removal yields, it is not as effective as hydrogen peroxide concentration. In addition, it was found that COD and total phenol removal yields at the lowest values of all variables remained at 18.36% and 44.58%, respectively. Besides, they increased to 42.17% and 53.17%, respectively, by increasing only treatment time to its highest level. However, further increasing the treatment time after a specific level, may not increase the removal yields. Nevertheless, higher removal rates in both COD and total phenol were achieved even in the lowest level of the treatment time, but the highest level of the other two variables. Pistachio wastewater electrooxidation subcritical water oxidation RSM Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Pistachio nuts (Pistacia vera, Anacardiaceae family) are valuable food and are largely consumed worldwide as a result of their dietary quality and health-related benefits (Gür and Demirer 2019). According to the latest statistics of 2020 reported by the International Nut and Dried Food Council (INC), over a million metric tons of pistachio was produced worldwide (Fil et al. 2014, Ozay et al. 2018, Isik et al. 2020). Iran and the USA are the leading producers, followed by Turkey as the third biggest pistachio producer in the world with a yearly production of 240,000 tons. For processing one ton of pistachio, approximately 06 m 3 of water is necessary (01 m 3 for spalling, 04 m 3 for paring, and 10 m 3 for washing), which is directly discharged to the ecosystem (Bayar et al. 2014, Gür and Demirer 2019, Isik et al. 2020). Pistachio processing wastewater contains a high level of undesirable toxic contaminants causing high values of total phenols, chemical oxygen demand (COD), and total organic carbon (TOC) (Bayar et al. 2018, Ozay et al. 2018, Gür and Demirer 2019, Isik et al. 2020). Until now, different physicochemical and biological technologies have been reported for industrial wastewater treatment for instance flotation (Elazzouzi et al. 2019), coagulation/flocculation (Bouchareb et al. 2020), electrocoagulation (Isik et al. 2021), precipitation (Xiao et al. 2018), oxidation (Shan et al. 2019), evaporation (Toth et al. 2018), solvent extraction (Al-Doury 2019), membrane filtration (Bouchareb et al. 2020), adsorption (de Caprariis et al. 2017), ion exchange (Wang et al. 2018), and biodegradation (Bouchareb et al. 2021). However, it is always a challenge for the treatment method selection since there is no best absolute and/or specific technique of water treatment. Each treatment method has got its particular advantages and disadvantages not only concerning cost, however mostly for the chosen method efficiency, practicability and environmental impact (Crini and Lichtfouse 2019). Due to the complex nature of industrial discharges, a single treatment method is incapable of adequate treatment and meeting the desired water quality standards. In addition, more biological treatment approaches remain ineffective to overwhelmed the mineralization of difficult-to-degrade pollutants and physicochemical methods cause the creation of the hazardous intermediary product (Yabalak 2018a). Therefore, highly effective hybrid or combined techniques are necessary to degrade the pollutants in the water and achieve recommended water quality in the most efficient and economical approach (Crini and Lichtfouse 2019, Bouchareb et al. 2020, Isik et al. 2021). Lately, there has been pronounced interest in the improvement of useful electrochemical techniques for the destruction of pollutant contents present in manufacturing wastewater. Electrooxidation (EO) of these contaminants is achieved through diverse methods (Shan et al. 2019). For instance, the indirect electrooxidation process uses hypochlorite and chlorine generated anodically to destruct organic contaminants (Rahmani et al. 2015a). Pollutants are also degraded by electrochemically produced hydrogen peroxide (Frangos et al. 2016). Contaminants direct anodic oxidation can also take place straight on anodes by producing physically adsorbed “active oxygen” (oxygen in the oxide lattice, MOx+1) over a process named direct or anodic oxidation (Frangos et al. 2016). Direct oxidation does not require further chemicals or oxygen and does not generate secondary contaminants or involve complicated accessories (Martínez-Huitle and Ferro 2006). The most significant constituent in the anodic oxidation progression is the anode itself. The most common anode materials for EO are carbon fiber, glassy carbon (Ti/RuO 2 , Ti/Pt–Ir), and stainless steel. On the other hand, not any of these materials has the combination of both stability and activity necessary for efficiency and durability to be applied. In recent years, anodes made of boron-doped conductive diamond have been used in electrooxidation and seem to be one of the most capable technologies in industrial wastewater treatment (Chen 2004). Compared to other electrode substances, conductive diamond has shown higher efficiency, stability and over potential for both hydrogen and oxygen development. These properties have led to the utilization of diamond electrodes in decolorization of toxic dyes solutions, electrosynthesis, oxidation of benzoic and carboxylic acids, oxidation of organic contaminants, degradation of surfactant, breakdown of phenolic aqueous wastes, and degradation of triazines. It is important to draw attention that this system reaches the total mineralization of contained organics in wastewater (Linares-Hernández et al. 2010). The environmentally eco-friendly subcritical water oxidation (SWO) method, which is a thermochemical technique and delivers resistant degradation and complex organic pollutants, is one of the most efficient methods in demand (Yabalak 2018a). Exposed elements are degraded to insignificant and non-harmful constituents, in the end to water and carbon dioxide deprived of any residue by the SWO method (Yabalak et al. 2018). In this method, the production of hydroxyl and other radical species occurs at high temperatures and pressures (Yabalak 2018b). Subcritical water is the a hot (373 K–647 K) and pressurized water to a degree to preserve it in the liquid phase at a working temperature. H 2 O 2 is acting as a green oxidizing agent, however, H 2 O 2 oxidation effect alone in the mineralization of target contaminants stays relatively low. On the other hand, H 2 O 2 synergistically contributes to the free radicals’ formation in the subcritical water environment. Numerous studies such as oxidation, extraction, solubility and organic synthesis have been conducted in a subcritical water environment due to its impressive advantages for instance availability and cheapness, having adaptable polarity as well as being an eco-friendly solvent (Yabalak 2018a). This research study engrossed on investigating the feasibility and performance of pistachio wastewater treatment by electrooxidation and the subcritical water oxidation hybrid process. The effects of current density (from 50 to 150 A/m 2 ), operating time (from 0 to 180 min), and initial wastewater pH (from 4 to 8) were examined removal of COD and total phenols, as well as recording the changes in conductivity for the electrooxidation treatment process. Moreover, electrochemically pre-treated wastewater was subjected to subcritical water oxidation. The effects of temperature (110–130 °C), treatment time (40–80 min), and concentration of H 2 O 2 (0.25-0.75 M) were examined removal of COD and total phenol for SWO treatment process. 2. Material And Methods 2.1. Pistachio wastewater characterization Pistachio wastewater characterization was carried out by using samples from a full-scale pistachio production industry located in Gaziantep, Turkey. The generated pistachio wastewater amount is about 10 m 3 /d. Samples were collected in June 2020 and kept in the refrigerator at +/- 4 °C. Wastewater was not diluted and was used as received in the further experiments. The characteristics of the studied pistachio wastewater showed that water quality parameters were higher than the acceptable range restricted by the standard pollution regulations as summarized in Table 1. Table 1. Pistachio wastewater characteristics Sample date pH Conductivity (mS/cm) COD (mg/L) Total Phenol (mg/L) 20.06.2020 5.15±0.15 8.32±0.22 20700±220 1546±45 2.2. Experimental setup Electrooxidation (EO) experiments of pistachio wastewater were accomplished in a 500 mL borosilicate glass reactor with a working volume of 250 mL. A magnetic stirrer (Wisd -Wisestir msh-20A) and a Teflon-covered magnetic stirring bar were used to mix the wastewater at 300 rpm. The reactor was engaged in the temperature-controlled water bath to fund a constant reaction temperature (298 ± 1K). The experimental arrangement is shown in Fig. 1. Activated carbon cloth (ACC) was utilized as anode/cathode electrode pairs. The electrodes were supplied by Norm Technologies, Turkey. Anode/cathode electrode pairs dimensions were arranged as 05 cm width × 08 cm high × 01 mm thickness with 40 cm 2 of total effective area and 02 cm of distance between the electrodes. In order to set anode and cathode connected to the positive and negative outlets, DC power source (AATech ADC-3303D, maximum current of 30 A) was used. SWO experiments were performed using an experimental setup which is previously given in detail (Yabalak 2018b). Briefly, a homemade stainless steel cylindrical container was used as a reactor, the reactor was heated and stirred using a heater with a integrated magnetic stirrer (Heidolph-MR.3001) and the temperature was controlled by a digital thermometer (Elimko, E-2000). 50 mL of EO-pre-treated pistachio wastewater was placed in the reactor and a certain amount of H 2 O 2 , which was determined according to pre-treatments of SWO, was added. The reactor was closed and its initial inner pressure was adjusted to 30 bar using nitrogen gas in each case. The reactor was heated to a specific degree and held constant during the experiment time. The reactor was cooled to room temperature, depressurised and opened at the end of the treatment time. In each experiment, an amount of 20 mL of SWO-treated sample was collected and stored at 04 °C for total phenol and COD analyses. The levels of each experimental variable above-mentioned were constructed by the central composite design (CCD) model given in Table 2. Table 2. The CCD of the independent variables of the SWO method Factors Independent variables Coded levels -1.682 -1 0 1 +1.682 x 1 Temperature (K) 376 383 393 403 410 x 2 Treatment time (min) 26.4 40 60 80 93.6 x 3 Concentration of H 2 O 2 (M) 0.08 0.25 0.5 0.75 0.92 2.3. Analysis Three studied current densities (50, 100, and 150 A/m 2 ), four altered electrooxidation times (30, 60, 120, and 180 min), and three different wastewater pH (5, 6, and 8) were investigated for pistachio wastewater EO treatment. For suitable time intervals, taken samples were centrifuged at 6000 rpm for 5 min and analysed by measuring COD, total phenols, pH, and conductivity. All electrooxidation experiments were accomplished in duplicates. Removal or elimination efficiencies were calculated using Eq (1): C i : The initial concentration and C f is the concentration after definite reaction time t (min). 3. Results And Discussion In this study, EO and SWO hybrid process was used for pistachio wastewater treatment. ACC electrode pairs were used as the anode/cathode electrodes. Current density effects, solution pH, and electrolysis time were investigated on both COD and total phenols elimination efficiencies. Moreover, electrochemically pre-treated wastewater was subjected to subcritical water oxidation. The effects of temperature (376–410 K), treatment time (26.4-93.65 min), and concentration of H 2 O 2 (0.08-0.92 M) were examined removal of COD and total phenols for the SWO process as explained in the following subsections. 3.1. Current density effect on removal efficiencies in the EO method Current density has an important impact on the success of the EO process and hence on COD and total phenol removals since it is the driving force in the migration of charge. As illustrated in Fig. 2A, COD removal efficiency increases with the increase of current density from 50 to 150 A/m 2 . Current density effect was better when increased up to 150 A/m 2 , where maximum COD elimination performance was nearly 50 % after 03 h electrolysis. COD removal efficiency versus time was improving as experiment time progresses. Previous studies by K. V. Radha ( 2009) and Toth et al. (2018), as well revealed that increases in current density directed to the increase in COD elimination. The potentials required for the oxidation of organic material are usually high. This implies that water could be oxidized and the production of oxygen is the main side reaction, which is a non-desired reaction and it may influence dramatically on the COD removal efficiency in particular for low current densities. The application of enough high current density, promotes the formation of stable oxidants, at the same time with the oxidation of other species contained in the treated pistachio wastewater. This had an additional benefit as these oxidants helped pollutants in wastewater oxidization. Similar results were known for total phenol removal with lower efficiency as shown in Fig. 2B. The total phenol concentration was reduced more with the increase of current density. Within the first 1h of reaction, there was a minor difference in total phenol removal efficiency when applying 100 and 150 A/m 2 of current density. The maximum recorded phenol removal efficiency as much as 19.02 % was obtained for 150 A/m 2 of current density after the total investigated reaction time of 180 min. In fact, the mechanism of phenol reaction is complicated and not yet entirely understood. It is agreed that dual oxidation pathways are elaborated in EO process: direct pathway degradation and indirect pathway (Cañizares et al. 1999). In the two paths, oxidation is achieved via hydroxyl radicals (OH·), which are formed on the electrode surface by release of water and/or by hydroxyl ions direct oxidation. As soon as hydroxyl radicals are generated, two restrictive behaviours, subject to the electrode nature, can be notable: chemisorption of hydroxyl radicals, which is established in active electrodes, and physisorption of hydroxyl radicals, that is established in nonactive electrodes as activated carbon cloth that was used in this study. Therefore, phenol is changed directly into water and carbon dioxide by physisorbed OH· radicals. Then, phenoxy radicals are produced, by the electrophilic attack of adsorbed OH· radicals over phenol. The formed radicals can be coupled to produce diverse molecular weights polymers. As EO is processing, solution pH has known a minor increase simultaneously with current density increase (Fig. 2C) up to maximum pH 6 for 150 A/m 2 after 180 min of reaction. On the contrary, conductivity has known a tinny decrease as a function of current density or as experience processing time as seen in Fig. 2D. 3.2. pH effect on removal efficiencies in the EO method pH is a significant operating parameter affecting the efficiency of the EO process. In the EO process, several free radicals are formed of different ratios dependent on the pH. These products show an important role in the removal of COD and total phenol in the EO process (Can 2014). COD and total phenols elimination performances as a function of electrooxidation experiment treatment time at various pH values while holding constant current density at 150 A/m 2 is shown in Fig. 3A and Fig. 3B subsequently. The best mineralization rate by electrooxidation happened at the original pH 5 then decreased at a higher solution pH of 6 and pH 8. Since the anode is perfectly stable in the acidic solution, no attempt has been performed to explore COD removal and phenol degradation in a higher alkaline environment. Similar results were obtained in previous studies, where the EO process well performed for COD and phenol removals in an acidic medium (Periyasamy and Muthuchamy 2018, Rahmani et al. 2018). The radicals formation from the supporting electrolyte in the acidic medium (pH 5) has possibly enhanced the elimination rate of organic substances (Periyasamy and Muthuchamy 2018, Rahmani et al. 2018). The acidic pH 5 perhaps inhibits the reaction of oxygen progress, resulting to the improvement of COD and total phenol degradation efficiency (Babuponnusami and Muthukumar 2012). This can be explained as at high pH, hydroxyl radical is transformed to O· - , which its oxidation characteristic declines. On the other hand, at low pH, hydroxyl radicals do react with hydrogen ions performing as a scavenger of hydroxyl radicals (Godini et al. 2013, Rahmani et al. 2015b). Fig.3B exhibits that the high phenol removal efficiency is obtained at pH=8 rather than pH=5. It can be explained as the removal of phenolic compounds on the anode surface based on electropolymerization in an alkaline solution (Belhadj Tahar and Savall 2009). During electrochemical oxidation of phenol in alkaline aqueous solution, polymer films form on ACC anodes. Hydroxyl radicals oxidize the polymer film formed on ACC electrodes. As seen in Fig. 3C, the electrooxidation reaction did not affect the final solution pH much during the whole reaction duration of 180 min. However, conductivity decreased at acidic pH (Fig. 3D). 3.3. Evaluation of SWO method The predicted and experimental COD and total phenol removal of 20 runs of the CCD models were given in Table 3. x 1 , x 2 and x 3 , demonstrates temperature (K), treatment time (min) and concentration of H 2 O 2 (M), respectively. The highest experimental and predicted COD removal yields were obtained in the run 11 and 20, respectively, as 54.5% and 54.3%. The highest experimental and predicted total phenol removal yields were obtained in the run 7 and 20, respectively, as 76.5% and 76.87%. Although run 7 provided significant total phenol removal, COD removal remained at 33.1%. Considering the experimental conditions of run 7 and run 11, it can be inferred that the temperature is a more efficacious parameter in the removal of total phenol than the removal of COD. Furthermore, considerable removal rates of total phenol compared to COD removal rates were due to the mechanism required for each response. Namely, to obtain high COD removal, the target pollutant must be degraded to the final oxidation product, H 2 O and CO 2 , where a minor change in the structure of the target molecule provides higher total phenol removal rates (Yabalak 2021). Therefore, more challenging experimental conditions are required to obtain higher COD removal rates compared to total phenol removal. In this case, the experimental variables can be adjusted according to the desired/regulation removal values of each response. Residual (Res.) values indicating a precision of a model and agreement between actual and predicted values were obtained to be between -2.1– 1.93 in the COD removal model and -5.76 – 3.34 in the total phenol removal model (Yabalak 2021). Also, leverages (L.) value close to 1 is not desired as L. values demonstrates the potential of the design points affecting the fit of the model coefficients, based on its location in the design space (Yabalak 2021). Herein, although quite reasonable and compatible experimental and predicted values were obtained in both models, the COD removal model showed better performance in the derivation of predicted results. Table 3. Experimental variables, COD and total phenol removal values, residuals and leverages values of experimental and predicted results. Run Experimental variables COD removal (%) Total phenolic removal (%) x 1 (K) x 2 (min) x 3 (M) Exp. Pre. Res. L. Exp. Pre. Res. L. 1 403 40 0.25 18.70 18.06 0.64 0.670 26.60 23.74 2.86 0.670 2 393 60 0.5 29.90 29.92 -0.024 0.166 52.10 57.86 -5.76 0.166 3 376 60 0.5 28.60 29.60 -1.00 0.607 49.40 50.34 -0.94 0.607 4 393 60 0.5 29.10 29.92 -0.82 0.166 59.60 57.86 1.74 0.166 5 383 80 0.75 33.50 34.31 -0.81 0.670 37.50 40.10 -2.60 0.670 6 393 60 0.5 29.50 29.92 -0.42 0.166 55.50 57.86 -2.36 0.166 7 403 80 0.75 33.10 33.51 -0.41 0.670 76.50 75.07 1.43 0.670 8 383 40 0.75 38.10 37.91 0.19 0.670 42.20 38.96 3.24 0.670 9 393 60 0.5 31.60 29.92 1.68 0.166 58.30 57.86 0.44 0.166 10 393 60 0.08 27.80 29.34 -1.54 0.607 34.80 35.29 -0.49 0.607 11 393 60 0.92 54.50 52.72 1.78 0.607 69.10 68.98 0.12 0.607 12 383 80 0.25 44.70 42.77 1.93 0.670 55.50 53.17 2.33 0.670 13 393 26.4 0.5 27.50 26.97 0.53 0.607 32.10 33.67 -1.57 0.607 14 393 60 0.5 30.50 29.92 0.58 0.166 61.20 57.86 3.34 0.166 15 410 60 0.5 29.90 28.66 1.24 0.607 62.80 62.22 0.58 0.607 16 383 40 0.25 18.60 18.36 0.24 0.670 43.40 44.58 -1.18 0.670 17 393 60 0.5 28.90 29.92 -1.02 0.166 60.50 57.86 2.64 0.166 18 403 80 0.25 24.90 25.26 -0.36 0.670 26.40 29.39 -2.99 0.670 19 393 93.6 0.5 29.70 29.99 -0.29 0.607 40.60 39.39 1.21 0.607 20 403 40 0.75 52.20 54.30 -2.10 0.670 74.80 76.87 -2.07 0.670 The theoretical regression equations in terms of coded factors of COD and total phenol removal models were given in Eq. 1 and 2, respectively. Eq. 2 and 3 are practical equations to predict the removal rates in the given experimental conditions. Also, the coefficient of each term in the equations demonstrates its influence portion on the response. Y 1 and Y 2 indicate the COD and total phenol removal percent in Eq. 2 and 3, respectively. The independent variables were depicted by x 1 , x 2 , and x 3 , while square effects were symbolized by and x 1 2 , x 2 2 , and x 3 2 , interaction effects were symbolized by x 1 x 2 , x 1 x 3 , and x 2 x 3 in both equations. Therefore, the most influential term of COD removal was x 2 x 3 (-7.0) followed by x 3 (6.95), where the most influential term of total phenol removal was x 2 x 3 (-7.0) x 1 x 3 (14.69) followed by x 3 (10.02). Table 4. ANOVA of the CCD models of COD and total phenol removals df: Degree of freedom The accuracy and suitability of the applied CCD models as well as the interactions between the experimental variables were assessed using the ANOVA test, the results of which were given in Table 4. Several statistical terms and tests such as F test and lack-of-fit tests and coefficients of determination (R 2 , R 2 adj ) were evaluated to determine the adequacy of both models. The higher the F value, the more noteworthy influence the relevant term or model has on the response [35]. On the contrary, the p -value is desired to be as low as possible (<0.05). Although p -values of both models were <0.0001, F values were 76.33 and 41.1 in COD and total phenol removal model, respectively. Therefore, the COD removal model is more compatible to designate the experimental variables compared to the total phenol removal model. Besides, in the COD removal model, x 3 , x 1 x 2 , x 1 x 3 , x 2 x 3 and x 3 2 , and in the total phenol removal model, x 1 , x 3 , x 1 x 3 , x 2 x 3 , x 2 2 and x 3 2 were the significant model terms. Also, an essential term that measures how well the model is lack of fit value. Lack of fit value is desired to be >0.05 (Yabalak et al. 2021). In this case, both performed model fit the data well as p values of COD and total phenol removal model were obtained as 0.0937 and 0.5535, respectively. Table 5. Regression and correlation coefficients of the CCD models of COD and total phenol removals Regression and correlation coefficients COD removal Total phenol removal Standard deviation 1.52 3.36 Mean 32.06 50.95 C.V. % 4.74 6.60 PRESS 150.47 557.52 R 2 0.9857 0.9737 Adjusted R 2 0.9727 0.9500 Predicted R 2 0.9067 0.8702 Adequate precision 33.695 22.348 The regression and correlation coefficients of the applied CCD method were demonstrated in Table 5. The fit of a model to each point in the design can be evaluated by the predicted residual error sum of squares (PRESS) value(Yabalak 2021). Herein, a smaller PRESS value observed for the COD removal model (150.12) than the total phenol removal model (557.52) indicates a better fit of the COD removal model. Also, R 2 values were 0.9857 and 0.9737 for COD and total phenol removal models, respectively, where predicted R 2 values were 0.9067 and 0.8702 for COD and total phenol removal models, respectively. A higher predicted R 2 value observed for the COD removal model proved its better efficiency in the derivation of predicted results. Also, the closeness of the adjusted and predicted R 2 values (the maximum acceptable difference is 0.2) indicate a better agreement between predicted and actual values. Therefore, an adequate correlation between adjusted and predicted R 2 values was observed in both models. Figure 4 depicted the agreement between the actual and predicted results of both models. Also, any point that cannot be predicted well by the applied model can be easily determined using this graph. In the desired model with a fit between actual and predicted results, data points split by 45-degree line(Yabalak et al. 2021). The well-aligned points observed along the above-mentioned line in Figure 4 indicate the accordance between actual and predicted results in both models. However, the more closeness of the points in the COD removal model proved a better fit of the model. It is supported by the COD and total phenol removal standard deviation values model obtained as 1.52 and 3.36, respectively. Residuals of actual results versus experimental run order in both CCD model were demonstrated in Figure 5. This plot is practical in the detection of the hidden variables that have the potential to affect the response. Random distribution was observed and the majority of the points remains in the upper and lower control limits (-4 – 4) in both methods. However, run 12, and 20 in the COD elimination model and run 2 in the total phenol removal model were pretty near to the above-mentioned limits. Experimental variables binary effects on the COD removal yields were demonstrated in Figure 6 (a), (c) and (e), where Figure 6 (b), (d) and (f) displays the binary effects of the experimental variables on the total phenol removal yields. According to Figure 6 (a), high COD removal yields can be attained at high temperature and low treatment time using 0.75 M of H 2 O 2 . However, the red area indicating the high-yielded area is much wider even at 0.65 M of H 2 O 2 in Figure 6 (b). The difference between the red area in Figure 6 (a) and (b) proved that even high removal rates of total phenol removal can be obtained under mild conditions, more challenging conditions are required to obtained high COD removal rates. For instance, at constant 0.75 M of H 2 O 2 , 373 K and in 60 min of treatment time, 40.80% and 65.85% of COD and total phenol removal, respectively, can be increased to 44.42% and 83.51%, respectively, by increasing the only temperature to 403 K. Although the increase in temperature provides 3.62% increase in COD removal rates, it provides 17.66% increase in the total phenol removal rates. Therefore, it can be said that complete mineralisation (COD removal) is less affected by temperature increase compared to phenol removal. Because, although the temperature increase easily causes deterioration in the structure of phenolic compounds, the oxidized species in the smaller chain structure still cause the COD value to be high. Figure 6 (c) and (d) demonstrates the combined effects of temperature and H 2 O 2 concentration on the COD and total phenol removal yields, respectively, at a fixed treatment time of 45 minutes. Figure 6 (c) demonstrates that the red area demonstrating high COD removal yields is much less than the red area shown in Figure 6 (d), which demonstrates high total phenol removal yields. In constant treatment time of 45 min, the obtained COD removal (20.69%) and total phenol removal (38.90%) rates at 393 K and using 0.25 M of H 2 O 2 can be altered to 28.96% of COD removal and 52.34% of total phenol removal by doubling the H 2 O 2 concentration. Further, tripling the H 2 O 2 concentration provides 45.09% of COD removal and 61.73% of total phenol removal. These finding proved that H 2 O 2 concentration has a significant effect on the responses and it must be at a sufficient level for efficient hydroxyl and other radicals’ formation based on the below-given reactions (Eq. 4-6) (Yabalak 2018a). H 2 O 2 2 • OH (4) H 2 O 2 + • OH → HO 2 • + H 2 O (5) H 2 O 2 + • OH → O 2 • - + H + + H 2 O (6) However, hydroxyl and other radical species are very unstable and can be easily affected by the experimental conditions, especially the hydrogen peroxide concentration, and can quench the radicals formed by reacting as seen in Eq. 7-8, thus causing a decrease in the removal yields (Yabalak 2018a). Therefore, it is of great importance to determine the concentration of hydrogen peroxide precisely. • OH + • OH → H 2 O 2 (7) • OH + HO 2 • → H 2 O + O 2 (8) The treatment time and H 2 O 2 concentration combined effects on the COD and total phenol removal yields at a fixed temperature of 400 K were shown in Figure 6 (e) and (f), correspondingly. At a fixed temperature of 400 K and 0.25 M of H 2 O 2 concentration, COD and total phenol removal yields can be altered from 18.25% and 27.15%, respectively, to 23.65%, and 37.73%, respectively, by increasing the treatment time from 40 min to 60 min. Further increasing the treatment time to 80 min, provides 28.03% and 33.23% of COD and total phenol removal yields, respectively. Therefore, although treatment time affects the removal yields, it is not as effective as hydrogen peroxide concentration. Also, further increasing the treatment time after a specific level, may not increase the removal yields. In Figure 7, the cube graph designed based on the predicted responses obtained from CCD models is given. By setting the experimental variable between the lowest (-1) and the highest (+1) levels, the optimum conditions for each variable for a given response level can be easily determined (Yabalak 2021). COD and total phenol removal efficiencies at the lowest values of all variables remain as 18.36% and 44.58%, respectively. However, they can be increased to 42.17% and 53.17%, respectively, by increasing only treatment time to its highest limit. Also, COD and total phenol removal performances at the highest values of all variables can be obtained as 33.51% and 75.07%, respectively. Nevertheless, higher removal rates in both COD and total phenol can be obtained even in the lowermost level of the treatment time, but the highest level of the other two variables. 4. Conclusion This study has demonstrated the importance of treating pistachio wastewater using a hybrid system between electrocoagulation as pre-treatment stage followed by subcritical water oxidation for both COD and total phenol removal. Current density and initial solution pH were optimised in the first stage of treatment. In the next stage, temperature, treatment time and concentration of H 2 O 2 were the main parameters to study the efficiency of the selected treatment process. The interactions between the experimental variables, the accuracy and suitability of the applied CCD models were evaluated using the ANOVA test, the results of the F test and lack-of-fit tests, coefficients of determination (R 2 , R 2 adj ) and supplementary statistical terms were evaluated to determine the adequacy of both models. It was found that the COD removal model is more compatible to designate the experimental variables compared to the total phenol removal model. Although p -values of both models were <0.0001, F values were 76.33 and 41.1 in COD and total phenol removal model, respectively. The regression and correlation coefficients of the applied CCD method were investigated. The fit of a model to each point in the design was evaluated by the predicted residual error sum of squares (PRESS) value. Herein, a smaller PRESS value observed for the COD removal model (150.12) than the total phenol removal model (557.52) indicated a better fit of the COD removal model. Also, R 2 values were 0.9857 and 0.9737 for COD and total phenol removal models, respectively, where predicted R 2 values were 0.9067 and 0.8702 for COD and total phenol removal models, consecutively. A higher predicted R 2 value observed for the COD removal model proved its better efficiency in the derivation of predicted results. The CCD was used to study the impact of the different studied parameters and predicted results were compared to the experimental ones. It was found that the model fits more with COD removal compared to total phenol removal. Declarations Ethical Approval: This article does not contain any studies with human participants or animals performed by any of the authors. Consent to Participate: All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript. Consent to Publish: All authors agreed with the content and that all gave explicit consent to submit and that they obtained consent from the responsible authorities at the institute/organization where the work has been carried out, before the work is submitted. Authors Contributions: All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Zelal Isik, Zhalaladdin Jabbiyev and Yasin Ozay. The first draft of the manuscript was written by Raouf Bouchareb, Erdal Yabalak, and Nadir Dizge and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Funding: No funding was received to assist with the preparation of this manuscript. Competing Interests: The authors have no competing interests to declare that are relevant to the content of this article. Availability of data and materials: All authors are requested to make sure that all data and materials as well as software application or custom code support their published claims and comply with field standards References Al-Doury MMI (2019) Treatment of oily sludge using solvent extraction. Pet Sci Technol 37:190–196. https://doi.org/10.1080/10916466.2018.1533859 Babuponnusami A, Muthukumar K (2012) Advanced oxidation of phenol: A comparison between Fenton, electro-Fenton, sono-electro-Fenton and photo-electro-Fenton processes. Chem Eng J 183:1–9. https://doi.org/10.1016/j.cej.2011.12.010 Bayar S, Boncukcuoǧlu R, Yilmaz AE, Fil BA (2014) Pre-Treatment of Pistachio Processing Industry Wastewaters (PPIW) by Electrocoagulation using Al Plate Electrode. Sep Sci Technol 49:1008–1018. https://doi.org/10.1080/01496395.2013.878847 Bayar S, Massara TM, Boncukcuoglu R, et al (2018) Advanced treatment of industrial wastewater from pistachio processing by Fenton process. Desalin Water Treat 112:106–111. https://doi.org/10.5004/dwt.2018.22197 Belhadj Tahar N, Savall A (2009) Electrochemical removal of phenol in alkaline solution. Contribution of the anodic polymerization on different electrode materials. Electrochim Acta 54:4809–4816. https://doi.org/10.1016/j.electacta.2009.03.086 Bouchareb EM, Kerroum D, Bezirhan Arikan E, et al (2021) Production of bio-hydrogen from bulgur processing industry wastewater. Energy Sources, Part A Recover Util Environ Eff 1–14. https://doi.org/10.1080/15567036.2021.1877853 Bouchareb R, Derbal K, Özay Y, et al (2020) Combined natural/chemical coagulation and membrane filtration for wood processing wastewater treatment. J Water Process Eng 37:101521. https://doi.org/10.1016/j.jwpe.2020.101521 Can OT (2014) COD removal from fruit-juice production wastewater by electrooxidation electrocoagulation and electro-Fenton processes. Desalin Water Treat 52:65–73. https://doi.org/10.1080/19443994.2013.781545 Cañizares P, Domínguez JA, Rodrigo MA, et al (1999) Effect of the current intensity in the electrochemical oxidation of aqueous phenol wastes at an activated carbon and steel anode. Ind Eng Chem Res 38:3779–3785. https://doi.org/10.1021/ie9901574 Chen G (2004) Electrochemical technologies in wastewater treatment. Sep Purif Technol 38:11–41. https://doi.org/10.1016/j.seppur.2003.10.006 Crini G, Lichtfouse E (2019) Advantages and disadvantages of techniques used for wastewater treatment. Environ Chem Lett 17:145–155. https://doi.org/10.1007/s10311-018-0785-9 de Caprariis B, De Filippis P, Hernandez AD, et al (2017) Pyrolysis wastewater treatment by adsorption on biochars produced by poplar biomass. J Environ Manage 197:231–238. https://doi.org/10.1016/j.jenvman.2017.04.007 Elazzouzi M, Haboubi K, Elyoubi MS (2019) Enhancement of electrocoagulation-flotation process for urban wastewater treatment using Al and Fe electrodes: Techno-economic study. Mater Today Proc 13:549–555. https://doi.org/10.1016/j.matpr.2019.04.012 Fil BA, Boncukcuoglu R, Yilmaz AE, Bayar S (2014) Electro-oxidation of pistachio processing industry wastewater using graphite anode. Clean - Soil, Air, Water 42:1232–1238. https://doi.org/10.1002/clen.201300560 Frangos P, Shen W, Wang H, et al (2016) Improvement of the degradation of pesticide deethylatrazine by combining UV photolysis with electrochemical generation of hydrogen peroxide. Chem Eng J 291:215–224. https://doi.org/10.1016/j.cej.2016.01.089 Godini K, Azarian G, Rahmani AR, Zolghadrnasab H (2013) Treatment of waste sludge: A comparison between anodic oxidation and electro-fenton processes. J Res Health Sci 13:188–193. https://doi.org/10.34172/jrhs13876 Gür E, Demirer GN (2019) Anaerobic Digestability and Biogas Production Capacity of Pistachio Processing Wastewater in UASB Reactors. J Environ Eng 145:04019042. https://doi.org/10.1061/(asce)ee.1943-7870.0001559 Isik Z, Arikan EB, Ozay Y, et al (2020) Electrocoagulation and electrooxidation pre-treatment effect on fungal treatment of pistachio processing wastewater. Chemosphere 244:125383. https://doi.org/10.1016/j.chemosphere.2019.125383 Isik Z, Bouchareb R, Saleh M, Dizge N (2021) Investigation of sesame processing wastewater treatment with combined electrochemical and membrane processes. Water Sci Technol 84:2652–2660. https://doi.org/10.2166/wst.2021.152 Linares-Hernández I, Barrera-Díaz C, Bilyeu B, et al (2010) A combined electrocoagulation-electrooxidation treatment for industrial wastewater. J Hazard Mater 175:688–694. https://doi.org/10.1016/j.jhazmat.2009.10.064 Martínez-Huitle CA, Ferro S (2006) Electrochemical oxidation of organic pollutants for the wastewater treatment: Direct and indirect processes. Chem Soc Rev 35:1324–1340. https://doi.org/10.1039/b517632h Ozay Y, Ünşar EK, Işık Z, et al (2018) Optimization of electrocoagulation process and combination of anaerobic digestion for the treatment of pistachio processing wastewater. J Clean Prod 196:42–50. https://doi.org/10.1016/j.jclepro.2018.05.242 Periyasamy S, Muthuchamy M (2018) Electrochemical oxidation of paracetamol in water by graphite anode: Effect of pH, electrolyte concentration and current density. J Environ Chem Eng 6:7358–7367. https://doi.org/10.1016/j.jece.2018.08.036 Radha K V., Sridevi V, Kalaivani K (2009) Electrochemical oxidation for the treatment of textile industry wastewater. Bioresour Technol 100:987–990. https://doi.org/10.1016/j.biortech.2008.06.048 Rahmani AR, Godini K, Nematollahi D, Azarian G (2015a) Electrochemical oxidation of activated sludge by using direct and indirect anodic oxidation. Desalin Water Treat 56:2234–2245. https://doi.org/10.1080/19443994.2014.958761 Rahmani AR, Nematollahi D, Azarian G, et al (2015b) Activated sludge treatment by electro-Fenton process: Parameter optimization and degradation mechanism. Korean J Chem Eng 32:1570–1577. https://doi.org/10.1007/s11814-014-0362-2 Rahmani AR, Nematollahi D, Samarghandi MR, et al (2018) A combined advanced oxidation process: Electrooxidation-ozonation for antibiotic ciprofloxacin removal from aqueous solution. J Electroanal Chem 808:82–89. https://doi.org/10.1016/j.jelechem.2017.11.067 Shan J, Zheng Y, Shi B, et al (2019) Regulating electrocatalysts via surface and interface engineering for acidic water electrooxidation. ACS Energy Lett 4:2719–2730. https://doi.org/10.1021/acsenergylett.9b01758 Toth AJ, Haaz E, Szilagyi B, et al (2018) COD reduction of process wastewater with vacuum evaporation. Waste Treat Recover 3:1–7. https://doi.org/10.1515/wtr-2018-0001 Wang M, Payne KA, Tong S, Ergas SJ (2018) Hybrid algal photosynthesis and ion exchange (HAPIX) process for high ammonium strength wastewater treatment. Water Res 142:65–74. https://doi.org/10.1016/j.watres.2018.05.043 Xiao W, Ke S, Quan N, et al (2018) The Role of Nanobubbles in the Precipitation and Recovery of Organic-Phosphine-Containing Beneficiation Wastewater. Langmuir 34:6217–6224. https://doi.org/10.1021/acs.langmuir.8b01123 Yabalak E (2018a) An approach to apply eco-friendly subcritical water oxidation method in the mineralization of the antibiotic ampicillin. J Environ Chem Eng 6:7132–7137. https://doi.org/10.1016/j.jece.2018.10.010 Yabalak E (2018b) Degradation of ticarcillin by subcritical water oxidation method: Application of response surface methodology and artificial neural network modeling. J Environ Sci Heal - Part A Toxic/Hazardous Subst Environ Eng 53:975–985. https://doi.org/10.1080/10934529.2018.1471023 Yabalak E (2021) Treatment of agrochemical wastewater by thermally activated persulfate oxidation method: Evaluation of energy and reagent consumption. J Environ Chem Eng 9:105201. https://doi.org/10.1016/j.jece.2021.105201 Yabalak E, Görmez Ö, Gizir AM (2018) Subcritical water oxidation of propham by H2O2 using response surface methodology (RSM). J Environ Sci Heal - Part B Pestic Food Contam Agric Wastes 53:334–339. https://doi.org/10.1080/03601234.2018.1431468 Yabalak E, Ozay Y, Vatanpour V, Dizge N (2021) Membrane concentrate management for textile wastewater with thermally activated persulfate oxidation method. Water Environ J 35:1281–1292. https://doi.org/10.1111/wej.12718 Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-1479310","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":103537025,"identity":"e9bd37ce-4de4-46d2-b247-15132055931b","order_by":0,"name":"Zelal Isik","email":"","orcid":"","institution":"Mersin University: Mersin Universitesi","correspondingAuthor":false,"prefix":"","firstName":"Zelal","middleName":"","lastName":"Isik","suffix":""},{"id":103537026,"identity":"7c9a4a4d-310d-4df7-8c5a-eb1759858e2f","order_by":1,"name":"Zhalaladdin Jabbiyev","email":"","orcid":"","institution":"Mersin University: Mersin Universitesi","correspondingAuthor":false,"prefix":"","firstName":"Zhalaladdin","middleName":"","lastName":"Jabbiyev","suffix":""},{"id":103537027,"identity":"831a3fa6-c67f-488a-b04b-962e900a8f3f","order_by":2,"name":"Raouf Bouchareb","email":"","orcid":"","institution":"Mersin University: Mersin Universitesi","correspondingAuthor":false,"prefix":"","firstName":"Raouf","middleName":"","lastName":"Bouchareb","suffix":""},{"id":103537028,"identity":"124de6ed-7229-4fcb-8fc5-4c2849def39d","order_by":3,"name":"Yasin Ozay","email":"","orcid":"","institution":"Tarsus Üniversitesi: Tarsus Universitesi","correspondingAuthor":false,"prefix":"","firstName":"Yasin","middleName":"","lastName":"Ozay","suffix":""},{"id":103537029,"identity":"23a64693-0cf0-4c48-ab37-6272d0903cb4","order_by":4,"name":"Erdal Yabalak","email":"","orcid":"","institution":"Mersin University: Mersin Universitesi","correspondingAuthor":false,"prefix":"","firstName":"Erdal","middleName":"","lastName":"Yabalak","suffix":""},{"id":103537030,"identity":"6439ca40-e6b0-464a-9740-44930b5dc98b","order_by":5,"name":"Nadir Dizge","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA20lEQVRIiWNgGAWjYHACgwMMbBJAmvkAkJCQIUULWwJICw9RWoCqQTSPAZgkqN6c/fDGwxVlFvby7j2fX92oseBhYD98dAM+LZY9aQUHz5yTYDY8c3abdc4xoMN40tJu4PdIjsHBxjYJNsMZuduMc9iAWiR4zPBrOf8GrIXHcP6bZ8Y5/4jRcgNii4S8BA/z49w2orQ8KzjYcE7CwIAnzYw5t0+Ch42gX84nb/7YUFZnL99++PHnnG91cvzsh4/h1YLQC41QSBwRBeQbGJg/EK16FIyCUTAKRhQAAEEsR+lc+a4BAAAAAElFTkSuQmCC","orcid":"","institution":"Mersin University: Mersin Universitesi","correspondingAuthor":true,"prefix":"","firstName":"Nadir","middleName":"","lastName":"Dizge","suffix":""}],"badges":[],"createdAt":"2022-03-22 21:18:51","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1479310/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1479310/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":21971227,"identity":"89ae93a8-e79e-4e46-830f-e4c323a39fb0","added_by":"auto","created_at":"2022-05-27 16:36:11","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":126295,"visible":true,"origin":"","legend":"\u003cp\u003eElectrooxidation experimental set-up (1. Magnetic stirrer, 2. Magnetic bar, 3. Storage tank for wastewater, 4. Electrodes, 5. Water Bath, 6. DC power source).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-1479310/v1/3cddd6fcd4682b8a2ac45507.png"},{"id":21971222,"identity":"29c8861a-6113-4daf-a955-5d07b22f1894","added_by":"auto","created_at":"2022-05-27 16:36:11","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":74085,"visible":true,"origin":"","legend":"\u003cp\u003eCurrent density effect on \u003cstrong\u003e(A)\u003c/strong\u003e COD removal performance, \u003cstrong\u003e(B)\u003c/strong\u003e total phenol removal performance, \u003cstrong\u003e(C)\u003c/strong\u003e change of solution pH, \u003cstrong\u003e(D)\u003c/strong\u003e change of conductivity versus time during the reaction (Experimental conditions: Solution pH=5.15, volume=250 mL, stirrer speed=500 rpm).\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-1479310/v1/a29ac3bb667028b3fb86d0e5.png"},{"id":21971224,"identity":"9c5e3e8a-86f8-4381-9cb4-6a4f7c589f3f","added_by":"auto","created_at":"2022-05-27 16:36:11","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":71461,"visible":true,"origin":"","legend":"\u003cp\u003eSolution pH effect on \u003cstrong\u003e(A)\u003c/strong\u003e COD removal efficiency, \u003cstrong\u003e(B)\u003c/strong\u003e total phenol removal efficiency, \u003cstrong\u003e(C)\u003c/strong\u003e change of solution pH, \u003cstrong\u003e(D)\u003c/strong\u003e change of conductivity versus time during the reaction (Experimental conditions: Current density=150 A/m\u003csup\u003e2\u003c/sup\u003e, volume=250 mL, stirrer speed=500 rpm).\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-1479310/v1/5b5cf4a29b6ee919b4f5898e.png"},{"id":21971223,"identity":"3e09dce7-b575-41eb-a069-0e5f8ef5deaf","added_by":"auto","created_at":"2022-05-27 16:36:11","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":58251,"visible":true,"origin":"","legend":"\u003cp\u003eThe actual and predicted values of COD and total phenol removal models correlation.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-1479310/v1/ea689ee1e23200c109edbf71.png"},{"id":21971413,"identity":"b3b9826e-70db-480f-b09c-d337fb1c38fa","added_by":"auto","created_at":"2022-05-27 16:41:11","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":60480,"visible":true,"origin":"","legend":"\u003cp\u003eResiduals versus the experimental run in COD and total phenol removal models.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-1479310/v1/ec37e5286c55a847a312f21b.png"},{"id":21971225,"identity":"c46b60eb-2f47-41be-87a6-0152e47d464e","added_by":"auto","created_at":"2022-05-27 16:36:11","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":876080,"visible":true,"origin":"","legend":"\u003cp\u003eBinary effects of experimental variables on the COD removal (a, c and e) and total phenol removal (b, d and f) yields.\u003cstrong\u003e \u003c/strong\u003e\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-1479310/v1/1fa644eda7e2acef2fca634d.png"},{"id":21971228,"identity":"ccaafef3-4083-4695-8540-c00e2a2ca560","added_by":"auto","created_at":"2022-05-27 16:36:11","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":69799,"visible":true,"origin":"","legend":"\u003cp\u003eCube plot of COD removal and total phenol removal models\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-1479310/v1/37459f704844d0a51f6bf8c5.png"},{"id":23155650,"identity":"9ce44370-1b02-42a1-b567-25d4c4d94b62","added_by":"auto","created_at":"2022-06-28 04:11:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1710771,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1479310/v1/0f482123-24e3-4389-87e1-af545e94c047.pdf"}],"financialInterests":"","formattedTitle":"Electrooxidation and subcritical water oxidation hybrid process for pistachio wastewater treatment","fulltext":[{"header":"1.\tIntroduction","content":"\u003cp\u003ePistachio nuts (Pistacia vera, Anacardiaceae family) are valuable food and are largely consumed worldwide as a result of their dietary quality and health-related benefits\u0026nbsp;(G\u0026uuml;r and Demirer 2019). According to the latest statistics of 2020 reported by the International Nut and Dried Food Council (INC), over a million metric tons of pistachio was produced worldwide\u0026nbsp;(Fil et al. 2014, Ozay et al. 2018, Isik et al. 2020). Iran and the USA are the leading producers, followed by Turkey as the third biggest pistachio producer in the world with a yearly production of 240,000 tons. For processing one ton of pistachio, approximately 06 m\u003csup\u003e3\u003c/sup\u003e of water is necessary (01 m\u003csup\u003e3\u003c/sup\u003e for spalling, 04 m\u003csup\u003e3\u003c/sup\u003e for paring, and 10 m\u003csup\u003e3\u003c/sup\u003e for washing), which is directly discharged to the ecosystem\u0026nbsp;(Bayar et al. 2014, G\u0026uuml;r and Demirer 2019, Isik et al. 2020). Pistachio processing wastewater contains a high level of undesirable toxic contaminants causing high values of total phenols, chemical oxygen demand (COD), and total organic carbon (TOC)\u0026nbsp;(Bayar et al. 2018, Ozay et al. 2018, G\u0026uuml;r and Demirer 2019, Isik et al. 2020).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eUntil now, different physicochemical and biological technologies have been reported for industrial wastewater treatment for instance flotation\u0026nbsp;(Elazzouzi et al. 2019), coagulation/flocculation\u0026nbsp;(Bouchareb et al. 2020), electrocoagulation\u0026nbsp;(Isik et al. 2021), precipitation\u0026nbsp;(Xiao et al. 2018), oxidation\u0026nbsp;(Shan et al. 2019), evaporation\u0026nbsp;(Toth et al. 2018), solvent extraction\u0026nbsp;(Al-Doury 2019), membrane filtration\u0026nbsp;(Bouchareb et al. 2020), adsorption\u0026nbsp;(de Caprariis et al. 2017), ion exchange\u0026nbsp;(Wang et al. 2018), and biodegradation\u0026nbsp;(Bouchareb et al. 2021). However, it is always a challenge for the treatment method selection since there is no best absolute and/or specific technique of water treatment. Each treatment method has got its particular advantages and disadvantages not only concerning cost, however mostly for the chosen method efficiency, practicability and environmental impact\u0026nbsp;(Crini and Lichtfouse 2019). Due to the complex nature of industrial discharges, a single treatment method is incapable of adequate treatment and meeting the desired water quality standards. In addition, more biological treatment approaches remain ineffective to overwhelmed the mineralization of difficult-to-degrade pollutants and physicochemical methods cause the creation of the hazardous intermediary product\u0026nbsp;(Yabalak 2018a). Therefore, highly effective hybrid or combined techniques are necessary to degrade the pollutants in the water and achieve recommended water quality in the most efficient and economical approach\u0026nbsp;(Crini and Lichtfouse 2019, Bouchareb et al. 2020, Isik et al. 2021).\u003c/p\u003e\n\u003cp\u003eLately, there has been pronounced interest in the improvement of useful electrochemical techniques for the destruction of pollutant contents present in manufacturing wastewater. Electrooxidation (EO) of these contaminants is achieved through diverse methods\u0026nbsp;(Shan et al. 2019). For instance, the indirect electrooxidation process uses hypochlorite and chlorine generated anodically to destruct organic contaminants\u0026nbsp;(Rahmani et al. 2015a). Pollutants are also degraded by electrochemically produced hydrogen peroxide\u0026nbsp;(Frangos et al. 2016). Contaminants direct anodic oxidation can also take place straight on anodes by producing physically adsorbed \u0026ldquo;active oxygen\u0026rdquo; (oxygen in the oxide lattice, MOx+1) over a process named direct or anodic oxidation\u0026nbsp;(Frangos et al. 2016). Direct oxidation does not require further chemicals or oxygen and does not generate secondary contaminants or involve complicated accessories\u0026nbsp;(Mart\u0026iacute;nez-Huitle and Ferro 2006). The most significant constituent in the anodic oxidation progression is the anode itself. The most common anode materials for EO are carbon fiber, glassy carbon (Ti/RuO\u003csub\u003e2\u003c/sub\u003e, Ti/Pt\u0026ndash;Ir), and stainless steel. On the other hand, not any of these materials has the combination of both stability and activity necessary for efficiency and durability to be applied. In recent years, anodes made of boron-doped conductive diamond have been used in electrooxidation and seem to be one of the most capable technologies in industrial wastewater treatment\u0026nbsp;(Chen 2004). Compared to other electrode substances, conductive diamond has shown higher efficiency, stability and over potential for both hydrogen and oxygen development. These properties have led to the utilization of diamond electrodes in decolorization of toxic dyes solutions, electrosynthesis, oxidation of benzoic and carboxylic acids, oxidation of organic contaminants, degradation of surfactant, breakdown of phenolic aqueous wastes, and degradation of triazines. It is important to draw attention that this system reaches the total mineralization of contained organics in wastewater\u0026nbsp;(Linares-Hern\u0026aacute;ndez et al. 2010).\u003c/p\u003e\n\u003cp\u003eThe environmentally eco-friendly subcritical water oxidation (SWO) method, which is a thermochemical technique and delivers resistant degradation and complex organic pollutants, is one of the most efficient methods in demand\u0026nbsp;(Yabalak 2018a). Exposed elements are degraded to insignificant and non-harmful constituents, in the end to water and carbon dioxide deprived of any residue by the SWO method\u0026nbsp;(Yabalak et al. 2018). In this method, the production of hydroxyl and other radical species occurs at high temperatures and pressures\u0026nbsp;(Yabalak 2018b). Subcritical water is the a hot (373 K\u0026ndash;647 K) and pressurized water to a degree to preserve it in the liquid phase at a working temperature. H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e is acting as a green oxidizing agent, however, H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e oxidation effect alone in the mineralization of target contaminants stays relatively low. On the other hand, H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e synergistically contributes to the free radicals\u0026rsquo; formation in the subcritical water environment. Numerous studies such as oxidation, extraction, solubility and organic synthesis have been conducted in a subcritical water environment due to its impressive advantages for instance availability and cheapness, having adaptable polarity as well as being an eco-friendly solvent\u0026nbsp;(Yabalak 2018a).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis research study engrossed on investigating the feasibility and performance of pistachio wastewater treatment by electrooxidation and the subcritical water oxidation hybrid process. The effects of current density (from 50 to 150 A/m\u003csup\u003e2\u003c/sup\u003e), operating time (from 0 to 180 min), and initial wastewater pH (from 4 to 8) were examined removal of COD and total phenols, as well as recording the changes in conductivity for the electrooxidation treatment process. Moreover, electrochemically pre-treated wastewater was subjected to subcritical water oxidation. The effects of temperature (110\u0026ndash;130 \u0026deg;C), treatment time (40\u0026ndash;80 min), and concentration of H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e (0.25-0.75 M) were examined removal of COD and total phenol for SWO treatment process.\u003c/p\u003e"},{"header":"2. Material And Methods","content":"\u003cp\u003e\u003cem\u003e2.1. Pistachio wastewater characterization \u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003ePistachio wastewater characterization was carried out by using samples from a full-scale pistachio production industry located in Gaziantep, Turkey. The generated pistachio wastewater amount is about 10 m\u003csup\u003e3\u003c/sup\u003e/d. Samples were collected in June 2020 and kept in the refrigerator at +/- 4 \u0026deg;C. Wastewater was not diluted and was used as received in the further experiments. The characteristics of the studied pistachio wastewater showed that water quality parameters were higher than the acceptable range restricted by the standard pollution regulations as summarized in Table 1.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e Pistachio wastewater characteristics\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"25.77319587628866%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSample date\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.402061855670103%\"\u003e\n \u003cp\u003e\u003cstrong\u003epH\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25.77319587628866%\"\u003e\n \u003cp\u003e\u003cstrong\u003eConductivity\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(mS/cm)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.52577319587629%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCOD\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(mg/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.52577319587629%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal Phenol (mg/L)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.77319587628866%\"\u003e\n \u003cp\u003e20.06.2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.402061855670103%\"\u003e\n \u003cp\u003e5.15\u0026plusmn;0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"25.77319587628866%\"\u003e\n \u003cp\u003e8.32\u0026plusmn;0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.52577319587629%\"\u003e\n \u003cp\u003e20700\u0026plusmn;220\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.52577319587629%\"\u003e\n \u003cp\u003e1546\u0026plusmn;45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.2. Experimental setup\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eElectrooxidation (EO) experiments of pistachio wastewater were accomplished in a 500 mL borosilicate glass reactor with a working volume of 250 mL. A\u0026nbsp;magnetic stirrer\u0026nbsp;(Wisd -Wisestir msh-20A)\u0026nbsp;and a Teflon-covered magnetic stirring bar were used to mix the wastewater\u0026nbsp;at 300 rpm.\u0026nbsp;The reactor was engaged in the temperature-controlled water bath\u0026nbsp;to fund a constant reaction temperature (298 \u0026plusmn; 1K). The experimental arrangement is shown in Fig. 1.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eActivated carbon cloth (ACC) was utilized as anode/cathode electrode pairs. The electrodes were supplied by Norm Technologies, Turkey. Anode/cathode electrode pairs dimensions were arranged as 05 cm width\u0026nbsp;\u0026times; 08\u0026nbsp;cm high \u0026times; 01 mm thickness with 40 cm\u003csup\u003e2\u003c/sup\u003e of total effective area\u003csup\u003e\u0026nbsp;\u003c/sup\u003eand 02 cm of distance between the electrodes. In order to set anode and cathode connected to the positive and negative outlets, DC power source (AATech ADC-3303D, maximum current of 30 A) was used.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSWO experiments were performed\u0026nbsp;using an experimental setup which is previously given in detail\u0026nbsp;(Yabalak 2018b). Briefly, a\u0026nbsp;homemade stainless steel\u0026nbsp;cylindrical container was used as a reactor, the reactor was heated and stirred using a heater with a integrated magnetic stirrer (Heidolph-MR.3001) and the temperature was controlled by a digital thermometer (Elimko, E-2000). 50 mL of EO-pre-treated pistachio wastewater was placed in the reactor and a certain amount of H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e, which was determined according to pre-treatments of SWO, was added. The reactor was closed and its initial inner pressure was adjusted to 30 bar using nitrogen gas in each case. The reactor was heated to a specific degree and held constant during the experiment time. The reactor was cooled to room temperature, depressurised and opened at the end of the treatment time. In each experiment, an amount of 20 mL of SWO-treated sample was collected and stored at 04 \u0026deg;C for total phenol and COD analyses. The levels of each experimental variable above-mentioned were constructed by the central composite design (CCD) model given in Table 2. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u0026nbsp;\u003c/strong\u003eThe CCD of the independent variables of the SWO method\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" width=\"12.595419847328245%\"\u003e\n \u003cp\u003e\u003cstrong\u003eFactors\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" width=\"40.458015267175576%\"\u003e\n \u003cp\u003e\u003cstrong\u003eIndependent variables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" width=\"46.94656488549618%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCoded levels\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.714285714285715%\"\u003e\n \u003cp\u003e-1.682\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.142857142857142%\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.918367346938776%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.142857142857142%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.081632653061224%\"\u003e\n \u003cp\u003e+1.682\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.619502868068833%\"\u003e\n \u003cp\u003e\u003cem\u003ex\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.53537284894838%\"\u003e\n \u003cp\u003eTemperature (K)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.045889101338432%\"\u003e\n \u003cp\u003e376\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.030592734225621%\"\u003e\n \u003cp\u003e383\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.45697896749522%\"\u003e\n \u003cp\u003e393\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.030592734225621%\"\u003e\n \u003cp\u003e403\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.281070745697896%\"\u003e\n \u003cp\u003e410\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.619502868068833%\"\u003e\n \u003cp\u003e\u003cem\u003ex\u003c/em\u003e\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.53537284894838%\"\u003e\n \u003cp\u003eTreatment time (min)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.045889101338432%\"\u003e\n \u003cp\u003e26.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.030592734225621%\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.45697896749522%\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.030592734225621%\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.281070745697896%\"\u003e\n \u003cp\u003e93.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.619502868068833%\"\u003e\n \u003cp\u003e\u003cem\u003ex\u003c/em\u003e\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.53537284894838%\"\u003e\n \u003cp\u003eConcentration of H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e (M)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.045889101338432%\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.030592734225621%\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.45697896749522%\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.030592734225621%\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.281070745697896%\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.3. Analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThree studied current densities (50, 100, and 150 A/m\u003csup\u003e2\u003c/sup\u003e), four altered electrooxidation times (30, 60, 120, and 180 min), and three different wastewater pH (5, 6, and 8) were investigated for pistachio wastewater EO treatment. For suitable time intervals, taken samples were centrifuged at 6000 rpm for 5 min and analysed by measuring COD, total phenols, pH, and conductivity. All electrooxidation experiments were accomplished in duplicates.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRemoval or elimination efficiencies were calculated using Eq (1):\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/p\u003e\n\u003cp\u003eC\u003csub\u003ei\u003c/sub\u003e : The initial concentration and\u0026nbsp; C\u003csub\u003ef\u003c/sub\u003e is the concentration after definite reaction time t (min).\u003c/p\u003e"},{"header":"3. Results And Discussion","content":"\u003cp\u003eIn this study, EO and SWO hybrid process was used for pistachio wastewater treatment. ACC electrode pairs were used as the anode/cathode electrodes. Current density effects, solution pH, and electrolysis time were investigated on both COD and total phenols elimination efficiencies.\u0026nbsp;Moreover, electrochemically pre-treated wastewater was subjected to subcritical water oxidation. The effects of temperature (376\u0026ndash;410 K), treatment time (26.4-93.65 min), and concentration of H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e (0.08-0.92 M) were examined removal of COD and total phenols for the SWO process as explained in the following subsections.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e3.1. Current density effect on removal efficiencies in the EO method\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eCurrent density has an important impact on the success of the EO process and hence on COD and total phenol removals since it is the driving force in the migration of charge. As illustrated in Fig. 2A, COD removal efficiency increases with the increase of current density from 50 to 150 A/m\u003csup\u003e2\u003c/sup\u003e. Current density effect was better when increased up to 150 A/m\u003csup\u003e2\u003c/sup\u003e, where maximum COD elimination performance was nearly 50 % after 03 h electrolysis. COD removal efficiency versus time was improving as experiment time progresses. Previous studies by K. V. Radha\u0026nbsp;( 2009)\u0026nbsp;and Toth et al.\u0026nbsp;(2018), as well revealed that increases in current density directed to the increase in COD elimination. The potentials required for the oxidation of organic material are usually high. This implies that water could be oxidized and the production of oxygen is the main side reaction, which is a non-desired reaction and it may influence dramatically on the COD removal efficiency in particular for low current densities. The application of enough high current density, promotes the formation of stable oxidants, at the same time with the oxidation of other species contained in the treated pistachio wastewater. This had an additional benefit as these oxidants helped pollutants in wastewater oxidization. Similar results were known for total phenol removal with lower efficiency as shown in Fig. 2B. The total phenol concentration was reduced more with the increase of current density. Within the first 1h of reaction, there was a minor difference in total phenol removal efficiency when applying 100 and 150 A/m\u003csup\u003e2\u003c/sup\u003e of current density. The maximum recorded phenol removal efficiency as much as 19.02 % was obtained for 150 A/m\u003csup\u003e2\u0026nbsp;\u003c/sup\u003eof current density after the total investigated reaction time of 180 min. In fact, the mechanism of phenol reaction is complicated and not yet entirely understood. It is agreed that dual oxidation pathways are elaborated in EO process: direct pathway degradation and indirect pathway\u0026nbsp;(Ca\u0026ntilde;izares et al. 1999). In the two paths, oxidation is achieved via hydroxyl radicals (OH\u0026middot;), which are formed on the electrode surface by release of water and/or by hydroxyl ions direct oxidation.\u003c/p\u003e\n\u003cp\u003eAs soon as hydroxyl radicals are generated, two restrictive behaviours, subject to the electrode nature, can be notable: chemisorption of hydroxyl radicals, which is established in active electrodes, and physisorption of hydroxyl radicals, that is established in nonactive electrodes as activated carbon cloth that was used in this study. Therefore, phenol is changed directly into water and carbon dioxide by physisorbed OH\u0026middot; radicals. Then, phenoxy radicals are produced, by the electrophilic attack of adsorbed OH\u0026middot; radicals over phenol. The formed radicals can be coupled to produce diverse molecular weights polymers.\u003c/p\u003e\n\u003cp\u003eAs EO is processing, solution pH has known a minor increase simultaneously with current density increase (Fig. 2C) up to maximum pH 6 for 150 A/m\u003csup\u003e2\u0026nbsp;\u003c/sup\u003eafter 180 min of reaction. On the contrary, conductivity has known a tinny decrease as a function of current density or as experience processing time as seen in Fig. 2D.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e3.2. pH effect on removal efficiencies in the EO method\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003epH is a significant operating parameter affecting the efficiency of the EO process. In the EO process, several free radicals are formed of different ratios dependent on the pH. These products show an important role in the removal of COD and total phenol in the EO process\u0026nbsp;(Can 2014).\u003c/p\u003e\n\u003cp\u003eCOD and total phenols elimination performances as a function of electrooxidation experiment treatment time at various pH values while holding constant current density at 150 A/m\u003csup\u003e2\u003c/sup\u003e is shown in Fig. 3A and Fig. 3B subsequently. The best mineralization rate by electrooxidation happened at the original pH 5 then decreased at a higher solution pH of 6 and pH 8. Since the anode is perfectly stable in the acidic solution, no attempt has been performed to explore COD removal and phenol degradation in a higher alkaline environment. Similar results were obtained in previous studies, where the EO process well performed for COD and phenol removals in an acidic medium (Periyasamy and Muthuchamy 2018, Rahmani et al. 2018). The radicals formation from the supporting electrolyte in the acidic medium (pH 5) has possibly enhanced the elimination rate of organic substances (Periyasamy and Muthuchamy 2018, Rahmani et al. 2018). The acidic pH 5 perhaps inhibits the reaction of oxygen progress, resulting to the improvement of COD and total phenol degradation efficiency (Babuponnusami and Muthukumar 2012). This can be explained as at high pH, hydroxyl radical is transformed to O\u0026middot;\u003csup\u003e-\u003c/sup\u003e, which its oxidation characteristic declines. On the other hand, at low pH, hydroxyl radicals do react with hydrogen ions performing as a scavenger of hydroxyl radicals (Godini et al. 2013, Rahmani et al. 2015b). Fig.3B exhibits that the high phenol removal efficiency is obtained at pH=8 rather than pH=5. It can be explained as the removal of phenolic compounds on the anode surface based on electropolymerization in an alkaline solution (Belhadj Tahar and Savall 2009). During electrochemical oxidation of phenol in alkaline aqueous solution, polymer films form on ACC anodes. Hydroxyl radicals oxidize the polymer film formed on ACC electrodes. As seen in Fig. 3C, the electrooxidation reaction did not affect the final solution pH much during the whole reaction duration of 180 min. However, conductivity decreased at acidic pH (Fig. 3D).\u003c/p\u003e\n\u003ch3\u003e\u003cem\u003e3.3. Evaluation of SWO method\u003c/em\u003e\u003c/h3\u003e\n\u003cp\u003eThe predicted and experimental COD and total phenol removal of 20 runs of the CCD models were given in Table 3. \u003cem\u003ex\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e, \u003cem\u003ex\u003c/em\u003e\u003csub\u003e2\u003c/sub\u003e and \u003cem\u003ex\u003c/em\u003e\u003csub\u003e3\u003c/sub\u003e, demonstrates temperature (K), treatment time (min) and concentration of H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e (M), respectively. The highest experimental and predicted COD removal yields were obtained in the run 11 and 20, respectively, as 54.5% and 54.3%. The highest experimental and predicted total phenol removal yields were obtained in the run 7 and 20, respectively, as 76.5% and 76.87%. Although run 7 provided significant total phenol removal, COD removal remained at 33.1%. Considering the experimental conditions of run 7 and run 11, it can be inferred that the temperature is a more efficacious parameter in the removal of total phenol than the removal of COD. Furthermore, considerable removal rates of total phenol compared to COD removal rates were due to the mechanism required for each response. Namely, to obtain high COD removal, the target pollutant must be degraded to the final oxidation product, H\u003csub\u003e2\u003c/sub\u003eO and CO\u003csub\u003e2\u003c/sub\u003e, where a minor change in the structure of the target molecule provides higher total phenol removal rates\u0026nbsp;(Yabalak 2021). Therefore, more challenging experimental conditions are required to obtain higher COD removal rates compared to total phenol removal. In this case, the experimental variables can be adjusted according to the desired/regulation removal values of each response. Residual (Res.) values indicating a precision of a model and agreement between actual and predicted values were obtained to be between -2.1\u0026ndash; 1.93 in the COD removal model and -5.76 \u0026ndash; 3.34 in the total phenol removal model\u0026nbsp;(Yabalak 2021). Also, leverages (L.) value close to 1 is not desired as L. values demonstrates the potential of the design points affecting the fit of the model coefficients, based on its location in the design space\u0026nbsp;(Yabalak 2021). Herein, although quite reasonable and compatible experimental and predicted values were obtained in both models, the COD removal model showed better performance in the derivation of predicted results.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3.\u0026nbsp;\u003c/strong\u003eExperimental variables, COD and total phenol removal values, residuals and leverages values of experimental and predicted results.\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" width=\"7.070707070707071%\"\u003e\n \u003cp\u003e\u003cstrong\u003eRun\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" width=\"23.232323232323232%\"\u003e\n \u003cp\u003e\u003cstrong\u003eExperimental variables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" width=\"32.323232323232325%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCOD\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eremoval (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"8\" width=\"37.37373737373738%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal phenolic\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eremoval (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.818181818181818%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ex\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003csub\u003e1\u003c/sub\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;(K)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ex\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003csub\u003e2\u003c/sub\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp; (min)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ex\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003csub\u003e3\u003c/sub\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp; (M)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\"\u003e\n \u003cp\u003e\u003cstrong\u003eExp.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.090909090909092%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePre.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"9.090909090909092%\"\u003e\n \u003cp\u003e\u003cstrong\u003eRes.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"7.954545454545454%\"\u003e\n \u003cp\u003e\u003cstrong\u003eL.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e\u003cstrong\u003eExp.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" width=\"6.818181818181818%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePre.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"10.227272727272727%\"\u003e\n \u003cp\u003e\u003cstrong\u003eRes.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.227272727272727%\"\u003e\n \u003cp\u003e\u003cstrong\u003eL.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\"\u003e\n \u003cp\u003e403\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e18.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e18.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"9.375%\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e0.670\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e26.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 10.2148%;\" width=\"9.375%\"\u003e\n \u003cp\u003e23.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 11.6602%;\" width=\"9.375%\"\u003e\n \u003cp\u003e2.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e0.670\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\"\u003e\n \u003cp\u003e393\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e29.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e29.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"9.375%\"\u003e\n \u003cp\u003e-0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e0.166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e52.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 10.2148%;\" width=\"9.375%\"\u003e\n \u003cp\u003e57.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 11.6602%;\" width=\"9.375%\"\u003e\n \u003cp\u003e-5.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e0.166\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\"\u003e\n \u003cp\u003e376\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e28.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e29.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"9.375%\"\u003e\n \u003cp\u003e-1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e0.607\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e49.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 10.2148%;\" width=\"9.375%\"\u003e\n \u003cp\u003e50.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 11.6602%;\" width=\"9.375%\"\u003e\n \u003cp\u003e-0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e0.607\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\"\u003e\n \u003cp\u003e393\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e29.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e29.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"9.375%\"\u003e\n \u003cp\u003e-0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e0.166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e59.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 10.2148%;\" width=\"9.375%\"\u003e\n \u003cp\u003e57.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 11.6602%;\" width=\"9.375%\"\u003e\n \u003cp\u003e1.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e0.166\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\"\u003e\n \u003cp\u003e383\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e33.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e34.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"9.375%\"\u003e\n \u003cp\u003e-0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e0.670\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e37.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 10.2148%;\" width=\"9.375%\"\u003e\n \u003cp\u003e40.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 11.6602%;\" width=\"9.375%\"\u003e\n \u003cp\u003e-2.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e0.670\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\"\u003e\n \u003cp\u003e393\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e29.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e29.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"9.375%\"\u003e\n \u003cp\u003e-0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e0.166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e55.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 10.2148%;\" width=\"9.375%\"\u003e\n \u003cp\u003e57.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 11.6602%;\" width=\"9.375%\"\u003e\n \u003cp\u003e-2.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e0.166\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\"\u003e\n \u003cp\u003e403\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e33.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e33.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"9.375%\"\u003e\n \u003cp\u003e-0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e0.670\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e76.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 10.2148%;\" width=\"9.375%\"\u003e\n \u003cp\u003e75.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 11.6602%;\" width=\"9.375%\"\u003e\n \u003cp\u003e1.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e0.670\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\"\u003e\n \u003cp\u003e383\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e38.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e37.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"9.375%\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e0.670\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e42.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 10.2148%;\" width=\"9.375%\"\u003e\n \u003cp\u003e38.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 11.6602%;\" width=\"9.375%\"\u003e\n \u003cp\u003e3.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e0.670\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\"\u003e\n \u003cp\u003e393\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e31.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e29.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"9.375%\"\u003e\n \u003cp\u003e1.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e0.166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e58.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 10.2148%;\" width=\"9.375%\"\u003e\n \u003cp\u003e57.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 11.6602%;\" width=\"9.375%\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e0.166\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\"\u003e\n \u003cp\u003e393\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e27.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e29.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"9.375%\"\u003e\n \u003cp\u003e-1.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e0.607\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e34.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 10.2148%;\" width=\"9.375%\"\u003e\n \u003cp\u003e35.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 11.6602%;\" width=\"9.375%\"\u003e\n \u003cp\u003e-0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e0.607\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\"\u003e\n \u003cp\u003e393\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e54.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e52.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"9.375%\"\u003e\n \u003cp\u003e1.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e0.607\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e69.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 10.2148%;\" width=\"9.375%\"\u003e\n \u003cp\u003e68.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 11.6602%;\" width=\"9.375%\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e0.607\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\"\u003e\n \u003cp\u003e383\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e44.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e42.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"9.375%\"\u003e\n \u003cp\u003e1.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e0.670\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e55.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 10.2148%;\" width=\"9.375%\"\u003e\n \u003cp\u003e53.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 11.6602%;\" width=\"9.375%\"\u003e\n \u003cp\u003e2.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e0.670\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\"\u003e\n \u003cp\u003e393\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e26.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e27.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e26.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"9.375%\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e0.607\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e32.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 10.2148%;\" width=\"9.375%\"\u003e\n \u003cp\u003e33.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 11.6602%;\" width=\"9.375%\"\u003e\n \u003cp\u003e-1.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e0.607\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\"\u003e\n \u003cp\u003e393\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e30.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e29.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"9.375%\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e0.166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e61.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 10.2148%;\" width=\"9.375%\"\u003e\n \u003cp\u003e57.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 11.6602%;\" width=\"9.375%\"\u003e\n \u003cp\u003e3.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e0.166\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\"\u003e\n \u003cp\u003e410\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e29.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e28.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"9.375%\"\u003e\n \u003cp\u003e1.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e0.607\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e62.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 10.2148%;\" width=\"9.375%\"\u003e\n \u003cp\u003e62.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 11.6602%;\" width=\"9.375%\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e0.607\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\"\u003e\n \u003cp\u003e383\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e18.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e18.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"9.375%\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e0.670\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e43.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 10.2148%;\" width=\"9.375%\"\u003e\n \u003cp\u003e44.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 11.6602%;\" width=\"9.375%\"\u003e\n \u003cp\u003e-1.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e0.670\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\"\u003e\n \u003cp\u003e393\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e28.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e29.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"9.375%\"\u003e\n \u003cp\u003e-1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e0.166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e60.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 10.2148%;\" width=\"9.375%\"\u003e\n \u003cp\u003e57.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 11.6602%;\" width=\"9.375%\"\u003e\n \u003cp\u003e2.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e0.166\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\"\u003e\n \u003cp\u003e403\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e24.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e25.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"9.375%\"\u003e\n \u003cp\u003e-0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e0.670\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e26.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 10.2148%;\" width=\"9.375%\"\u003e\n \u003cp\u003e29.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 11.6602%;\" width=\"9.375%\"\u003e\n \u003cp\u003e-2.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e0.670\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\"\u003e\n \u003cp\u003e393\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e93.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e29.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e29.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"9.375%\"\u003e\n \u003cp\u003e-0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e0.607\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e40.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 10.2148%;\" width=\"9.375%\"\u003e\n \u003cp\u003e39.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 11.6602%;\" width=\"9.375%\"\u003e\n \u003cp\u003e1.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e0.607\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\"\u003e\n \u003cp\u003e403\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e52.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e54.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"9.375%\"\u003e\n \u003cp\u003e-2.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e0.670\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e74.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 10.2148%;\" width=\"9.375%\"\u003e\n \u003cp\u003e76.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 11.6602%;\" width=\"9.375%\"\u003e\n \u003cp\u003e-2.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e0.670\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe theoretical regression equations in terms of coded factors of COD and total phenol removal models were given in Eq. 1 and 2, respectively.\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/p\u003e\n\u003cp\u003eEq. 2 and 3 are practical equations to predict the removal rates in the given experimental conditions. Also, the coefficient of each term in the equations demonstrates its influence portion on the response.\u003cem\u003e\u0026nbsp;Y\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e and \u003cem\u003eY\u003c/em\u003e\u003csub\u003e2\u003c/sub\u003e indicate the COD and total phenol removal percent in Eq. 2 and 3, respectively. The independent variables were depicted by \u003cem\u003ex\u003csub\u003e1\u003c/sub\u003e\u003c/em\u003e, \u003cem\u003ex\u003c/em\u003e\u003csub\u003e2\u003c/sub\u003e, and \u003cem\u003ex\u003c/em\u003e\u003csub\u003e3\u003c/sub\u003e, while square effects were symbolized by and \u003cem\u003ex\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e, \u003cem\u003ex\u003c/em\u003e\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e, and \u003cem\u003ex\u003c/em\u003e\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e, interaction effects were symbolized by \u003cem\u003ex\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e\u003cem\u003ex\u003c/em\u003e\u003csub\u003e2\u003c/sub\u003e,\u003csub\u003e\u0026nbsp;\u003c/sub\u003e\u003cem\u003ex\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e\u003cem\u003ex\u003c/em\u003e\u003csub\u003e3\u003c/sub\u003e, and \u003cem\u003ex\u003c/em\u003e\u003csub\u003e2\u003c/sub\u003e\u003cem\u003ex\u003c/em\u003e\u003csub\u003e3\u003c/sub\u003e in both equations. Therefore, the most influential term of COD removal was x\u003csub\u003e2\u003c/sub\u003e\u003cem\u003ex\u003c/em\u003e\u003csub\u003e3\u003c/sub\u003e (-7.0)\u003csup\u003e\u0026nbsp;\u003c/sup\u003efollowed by \u003cem\u003ex\u003c/em\u003e\u003csub\u003e3\u003c/sub\u003e (6.95), where the most influential term of total phenol removal was x\u003csub\u003e2\u003c/sub\u003e\u003cem\u003ex\u003c/em\u003e\u003csub\u003e3\u003c/sub\u003e (-7.0)\u003csup\u003e\u0026nbsp;\u003c/sup\u003ex\u003csub\u003e1\u003c/sub\u003e\u003cem\u003ex\u003c/em\u003e\u003csub\u003e3\u003c/sub\u003e (14.69)\u003csup\u003e\u0026nbsp;\u003c/sup\u003efollowed by \u003cem\u003ex\u003c/em\u003e\u003csub\u003e3\u003c/sub\u003e (10.02).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4.\u0026nbsp;\u003c/strong\u003eANOVA of the CCD models of COD and total phenol removals\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003edf: Degree of freedom\u003c/em\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe accuracy and suitability of the applied CCD models as well as the interactions between the experimental variables were assessed using the ANOVA test, the results of which were given in Table 4. Several statistical terms and tests such as \u003cem\u003eF\u0026nbsp;\u003c/em\u003etest and\u0026nbsp;lack-of-fit tests\u0026nbsp;and coefficients of determination (R\u003csup\u003e2\u003c/sup\u003e, R\u003csup\u003e2\u003c/sup\u003e\u003csub\u003eadj\u003c/sub\u003e) were evaluated to determine the adequacy of both models. The higher the \u003cem\u003eF\u003c/em\u003e value, the more noteworthy influence the relevant term or model has on the response [35]. On the contrary, the \u003cem\u003ep\u003c/em\u003e-value is desired to be as low as possible (\u0026lt;0.05). Although \u003cem\u003ep\u003c/em\u003e-values of both models were \u0026lt;0.0001, \u003cem\u003eF\u003c/em\u003e values were 76.33 and 41.1 in COD and total phenol removal model, respectively. Therefore, the COD removal model is more compatible to designate the experimental variables compared to the total phenol removal model. Besides, in the COD removal model, \u003cem\u003ex\u003c/em\u003e\u003csub\u003e3\u003c/sub\u003e,\u003cem\u003e\u0026nbsp;x\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e\u003cem\u003ex\u003c/em\u003e\u003csub\u003e2\u003c/sub\u003e,\u003cem\u003e\u0026nbsp;x\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e\u003cem\u003ex\u003c/em\u003e\u003csub\u003e3\u003c/sub\u003e,\u003cem\u003e\u0026nbsp;x\u003c/em\u003e\u003csub\u003e2\u003c/sub\u003e\u003cem\u003ex\u003c/em\u003e\u003csub\u003e3\u003c/sub\u003e and\u003cem\u003e\u0026nbsp;x\u003c/em\u003e\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e, and in the total phenol removal model, \u003cem\u003ex\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e,\u003cem\u003e\u0026nbsp;x\u003c/em\u003e\u003csub\u003e3\u003c/sub\u003e,\u003cem\u003e\u0026nbsp;x\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e\u003cem\u003ex\u003c/em\u003e\u003csub\u003e3\u003c/sub\u003e,\u003cem\u003e\u0026nbsp;x\u003c/em\u003e\u003csub\u003e2\u003c/sub\u003e\u003cem\u003ex\u003c/em\u003e\u003csub\u003e3\u003c/sub\u003e, \u003cem\u003ex\u003c/em\u003e\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e and\u003cem\u003e\u0026nbsp;x\u003c/em\u003e\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e2\u003c/sup\u003e were the significant model terms. Also, an essential term that measures how well the model is lack of fit value. Lack of fit value is desired to be \u0026gt;0.05 (Yabalak et al. 2021). In this case, both performed model fit the data well as p values of COD and total phenol removal model were obtained as 0.0937 and 0.5535, respectively.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5.\u0026nbsp;\u003c/strong\u003eRegression and correlation coefficients of the CCD models of COD and total phenol removals\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"90%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"54.08163265306123%\"\u003e\n \u003cp\u003e\u003cstrong\u003eRegression and correlation coefficients\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.489795918367346%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCOD removal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal phenol removal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"54.08163265306123%\"\u003e\n \u003cp\u003e\u003cstrong\u003eStandard deviation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.489795918367346%\"\u003e\n \u003cp\u003e1.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003e3.36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"54.08163265306123%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.489795918367346%\"\u003e\n \u003cp\u003e32.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003e50.95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"54.08163265306123%\"\u003e\n \u003cp\u003e\u003cstrong\u003eC.V. %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.489795918367346%\"\u003e\n \u003cp\u003e4.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003e6.60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"54.08163265306123%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePRESS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.489795918367346%\"\u003e\n \u003cp\u003e150.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003e557.52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"54.08163265306123%\"\u003e\n \u003cp\u003e\u003cstrong\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.489795918367346%\"\u003e\n \u003cp\u003e0.9857\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003e0.9737\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"54.08163265306123%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdjusted R\u003csup\u003e2\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.489795918367346%\"\u003e\n \u003cp\u003e0.9727\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003e0.9500\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"54.08163265306123%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePredicted R\u003csup\u003e2\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.489795918367346%\"\u003e\n \u003cp\u003e0.9067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003e0.8702\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"54.08163265306123%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdequate precision\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.489795918367346%\"\u003e\n \u003cp\u003e33.695\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003e22.348\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe regression and correlation coefficients of the applied CCD method were demonstrated in Table 5. The fit of a model to each point in the design can be evaluated by the predicted residual error sum of squares (PRESS) value(Yabalak 2021). Herein, a smaller PRESS value observed for the COD removal model (150.12) than the total phenol removal model (557.52) indicates a better fit of the COD removal model. Also, R\u003csup\u003e2\u003c/sup\u003e values were 0.9857 and 0.9737 for COD and total phenol removal models, respectively, where predicted R\u003csup\u003e2\u003c/sup\u003e values were 0.9067 and 0.8702 for COD and total phenol removal models, respectively. A higher predicted R\u003csup\u003e2\u003c/sup\u003e value observed for the COD removal model proved its better efficiency in the derivation of predicted results. Also, the closeness of the adjusted and predicted R\u003csup\u003e2\u003c/sup\u003e values (the maximum acceptable difference is 0.2) indicate a better agreement between predicted and actual values. Therefore, an adequate correlation between adjusted and predicted R\u003csup\u003e2\u003c/sup\u003e values was observed in both models.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFigure 4 depicted the agreement between the actual and predicted results of both models. Also, any point that cannot be predicted well by the applied model can be easily determined using this graph. In the desired model with a fit between actual and predicted results, data points split by 45-degree line(Yabalak et al. 2021). The well-aligned points observed along the above-mentioned line in Figure 4 indicate the accordance between actual and predicted results in both models. However, the more closeness of the points in the COD removal model proved a better fit of the model. It is supported by the COD and total phenol removal standard deviation values model obtained as 1.52 and 3.36, respectively.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eResiduals of actual results versus experimental run order in both CCD model were demonstrated in Figure 5. This plot is practical in the detection of the hidden variables that have the potential to affect the response. Random distribution was observed and the majority of the points remains in the upper and lower control limits (-4 \u0026ndash; 4) in both methods. However, run 12, and 20 in the COD elimination model and run 2 in the total phenol removal model were pretty near to the above-mentioned limits.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eExperimental variables binary effects on the COD removal yields were demonstrated in Figure 6 (a), (c) and (e), where Figure 6 (b), (d) and (f) displays the binary effects of the experimental variables on the total phenol removal yields. According to Figure 6 (a), high COD removal yields can be attained at high temperature and low treatment time using 0.75 M of H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e. However, the red area indicating the high-yielded area is much wider even at 0.65 M of H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e in Figure 6 (b). The difference between the red area in Figure 6 (a) and (b) proved that even high removal rates of total phenol removal can be obtained under mild conditions, more challenging conditions are required to obtained high COD removal rates. For instance, at constant 0.75 M of H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e, 373 K and in 60 min of treatment time, 40.80% and 65.85% of COD and total phenol removal, respectively, can be increased to 44.42% and 83.51%, respectively, by increasing the only temperature to 403 K. Although the increase in temperature provides 3.62% increase in COD removal rates, it provides 17.66% increase in the total phenol removal rates. Therefore, it can be said that complete mineralisation (COD removal) is less affected by temperature increase compared to phenol removal. Because, although the temperature increase easily causes deterioration in the structure of phenolic compounds, the oxidized species in the smaller chain structure still cause the COD value to be high.\u003c/p\u003e\n\u003cp\u003eFigure 6 (c) and (d) demonstrates the combined effects of temperature and H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e concentration on the COD and total phenol removal yields, respectively, at a fixed treatment time of 45 minutes. Figure 6 (c) demonstrates that the red area demonstrating high COD removal yields is much less than the red area shown in Figure 6 (d), which demonstrates high total phenol removal yields. In constant treatment time of 45 min, the obtained COD removal (20.69%) and total phenol removal (38.90%) rates at 393 K and using 0.25 M of H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u0026nbsp;\u003c/sub\u003ecan be altered to 28.96% of COD removal and 52.34% of total phenol removal by doubling the H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u0026nbsp;\u003c/sub\u003econcentration. Further, tripling the H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u0026nbsp;\u003c/sub\u003econcentration provides 45.09% of COD removal and 61.73% of total phenol removal. These finding proved that H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u0026nbsp;\u003c/sub\u003econcentration has a significant effect on the responses and it must be at a sufficient level for efficient hydroxyl and other radicals\u0026rsquo; formation based on the below-given reactions (Eq. 4-6)\u0026nbsp;(Yabalak 2018a).\u003c/p\u003e\n\u003cp\u003eH\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2 \u0026nbsp;\u0026nbsp;\u003c/sub\u003e\u0026nbsp; 2\u003csup\u003e\u0026bull;\u003c/sup\u003eOH\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;(4)\u003c/p\u003e\n\u003cp\u003eH\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2 +\u003c/sub\u003e \u003csup\u003e\u0026bull;\u003c/sup\u003eOH \u0026rarr; HO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026bull;\u0026nbsp;\u003c/sup\u003e+ H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/sub\u003e(5)\u003c/p\u003e\n\u003cp\u003eH\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2 +\u003c/sub\u003e \u003csup\u003e\u0026bull;\u003c/sup\u003eOH\u003csub\u003e\u0026nbsp; \u0026nbsp;\u003c/sub\u003e\u0026rarr; O\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026bull;\u003cstrong\u003e-\u003c/strong\u003e\u003c/sup\u003e + H\u003csup\u003e+\u003c/sup\u003e + H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e\u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u0026nbsp;\u003c/sub\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;(6)\u003c/p\u003e\n\u003cp\u003eHowever, hydroxyl and other radical species are very unstable and can be easily affected by the experimental conditions, especially the hydrogen peroxide concentration, and can quench the radicals formed by reacting as seen in Eq. 7-8, thus causing a decrease in the removal yields\u0026nbsp;(Yabalak 2018a). Therefore, it is of great importance to determine the concentration of hydrogen peroxide precisely.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e\u0026bull;\u003c/sup\u003eOH + \u003csup\u003e\u0026bull;\u003c/sup\u003eOH \u0026nbsp; \u0026nbsp;\u0026rarr; H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/sub\u003e(7)\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e\u0026bull;\u003c/sup\u003eOH + HO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026bull; \u0026nbsp; \u0026nbsp;\u003c/sup\u003e\u0026rarr; H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e\u0026nbsp;+\u003c/sub\u003e O\u003csub\u003e2 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u0026nbsp;\u003c/sub\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp; (8)\u003c/p\u003e\n\u003cp\u003eThe treatment time and H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e concentration combined effects on the COD and total phenol removal yields at a fixed temperature of 400 K were shown in Figure 6 (e) and (f), correspondingly. At a fixed temperature of 400 K and 0.25 M of H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e concentration, COD and total phenol removal yields can be altered from 18.25% and 27.15%, respectively, to 23.65%, and 37.73%, respectively, by increasing the treatment time from 40 min to 60 min. Further increasing the treatment time to 80 min, provides 28.03% and 33.23% of COD and total phenol removal yields, respectively. Therefore, although treatment time affects the removal yields, it is not as effective as hydrogen peroxide concentration. Also, further increasing the treatment time after a specific level, may not increase the removal yields.\u003c/p\u003e\n\u003cp\u003eIn Figure 7, the cube graph designed based on the predicted responses obtained from CCD models is given. By setting the experimental variable between the lowest (-1) and the highest (+1) levels, the optimum conditions for each variable for a given response level can be easily determined (Yabalak 2021). COD and total phenol removal efficiencies at the lowest values of all variables remain as 18.36% and 44.58%, respectively. However, they can be increased to 42.17% and 53.17%, respectively, by increasing only treatment time to its highest limit. Also, COD and total phenol removal performances at the highest values of all variables can be obtained as 33.51% and 75.07%, respectively. Nevertheless, higher removal rates in both COD and total phenol can be obtained even in the lowermost level of the treatment time, but the highest level of the other two variables.\u0026nbsp;\u003c/p\u003e"},{"header":"4. Conclusion","content":"\u003cp\u003eThis study has demonstrated the importance of treating pistachio wastewater using a hybrid system between electrocoagulation as pre-treatment stage followed by subcritical water oxidation for both COD and total phenol removal. Current density and initial solution pH were optimised in the first stage of treatment. In the next stage, temperature, treatment time and concentration of H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e were the main parameters to study the efficiency of the selected treatment process.\u003c/p\u003e\n\u003cp\u003eThe interactions between the experimental variables, the accuracy and suitability of the applied CCD models were evaluated using the ANOVA test, the results of the \u003cem\u003eF\u0026nbsp;\u003c/em\u003etest and\u0026nbsp;lack-of-fit tests, coefficients of determination (R\u003csup\u003e2\u003c/sup\u003e, R\u003csup\u003e2\u003c/sup\u003e\u003csub\u003eadj\u003c/sub\u003e) and supplementary statistical terms were evaluated to determine the adequacy of both models. It was found that the\u0026nbsp;COD removal model is more compatible to designate the experimental variables compared to the total phenol removal model.\u0026nbsp;Although \u003cem\u003ep\u003c/em\u003e-values of both models were\u0026nbsp;\u0026lt;0.0001, \u003cem\u003eF\u003c/em\u003e values were 76.33 and 41.1 in COD and total phenol removal model, respectively.\u003c/p\u003e\n\u003cp\u003eThe regression and correlation coefficients of the applied CCD method were investigated. The fit of a model to each point in the design was evaluated by the predicted residual error sum of squares (PRESS) value. Herein, a smaller PRESS value observed for the COD removal model (150.12) than the total phenol removal model (557.52) indicated a better fit of the COD removal model. Also, R\u003csup\u003e2\u003c/sup\u003e values were 0.9857 and 0.9737 for COD and total phenol removal models, respectively, where predicted R\u003csup\u003e2\u003c/sup\u003e values were 0.9067 and 0.8702 for COD and total phenol removal models, consecutively. A higher predicted R\u003csup\u003e2\u003c/sup\u003e value observed for the COD removal model proved its better efficiency in the derivation of predicted results.\u003c/p\u003e\n\u003cp\u003eThe CCD was used to study the impact of the different studied parameters and predicted results were compared to the experimental ones. It was found that the model fits more with COD removal compared to total phenol removal.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical Approval:\u0026nbsp;\u003c/strong\u003eThis article does not contain any studies with human participants or animals performed by any of the authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate:\u0026nbsp;\u003c/strong\u003eAll authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish:\u0026nbsp;\u003c/strong\u003eAll authors agreed with the content and that all gave explicit consent to submit and that they obtained consent from the responsible authorities at the institute/organization where the work has been carried out, before the work is submitted.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors Contributions:\u0026nbsp;\u003c/strong\u003eAll authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Zelal Isik, Zhalaladdin Jabbiyev and Yasin Ozay. The first draft of the manuscript was written by Raouf Bouchareb, Erdal Yabalak, and Nadir Dizge and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eNo funding was received to assist with the preparation of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests:\u0026nbsp;\u003c/strong\u003eThe authors have no competing interests to declare that are relevant to the content of this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u0026nbsp;\u003c/strong\u003eAll authors are requested to make sure that all data and materials as well as software application or custom code support their published claims and comply with field standards\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAl-Doury MMI (2019) Treatment of oily sludge using solvent extraction. Pet Sci Technol 37:190\u0026ndash;196. https://doi.org/10.1080/10916466.2018.1533859\u003c/li\u003e\n\u003cli\u003eBabuponnusami A, Muthukumar K (2012) Advanced oxidation of phenol: A comparison between Fenton, electro-Fenton, sono-electro-Fenton and photo-electro-Fenton processes. Chem Eng J 183:1\u0026ndash;9. https://doi.org/10.1016/j.cej.2011.12.010\u003c/li\u003e\n\u003cli\u003eBayar S, Boncukcuoǧlu R, Yilmaz AE, Fil BA (2014) Pre-Treatment of Pistachio Processing Industry Wastewaters (PPIW) by Electrocoagulation using Al Plate Electrode. Sep Sci Technol 49:1008\u0026ndash;1018. https://doi.org/10.1080/01496395.2013.878847\u003c/li\u003e\n\u003cli\u003eBayar S, Massara TM, Boncukcuoglu R, et al (2018) Advanced treatment of industrial wastewater from pistachio processing by Fenton process. Desalin Water Treat 112:106\u0026ndash;111. https://doi.org/10.5004/dwt.2018.22197\u003c/li\u003e\n\u003cli\u003eBelhadj Tahar N, Savall A (2009) Electrochemical removal of phenol in alkaline solution. Contribution of the anodic polymerization on different electrode materials. Electrochim Acta 54:4809\u0026ndash;4816. https://doi.org/10.1016/j.electacta.2009.03.086\u003c/li\u003e\n\u003cli\u003eBouchareb EM, Kerroum D, Bezirhan Arikan E, et al (2021) Production of bio-hydrogen from bulgur processing industry wastewater. Energy Sources, Part A Recover Util Environ Eff 1\u0026ndash;14. https://doi.org/10.1080/15567036.2021.1877853\u003c/li\u003e\n\u003cli\u003eBouchareb R, Derbal K, \u0026Ouml;zay Y, et al (2020) Combined natural/chemical coagulation and membrane filtration for wood processing wastewater treatment. J Water Process Eng 37:101521. https://doi.org/10.1016/j.jwpe.2020.101521\u003c/li\u003e\n\u003cli\u003eCan OT (2014) COD removal from fruit-juice production wastewater by electrooxidation electrocoagulation and electro-Fenton processes. Desalin Water Treat 52:65\u0026ndash;73. https://doi.org/10.1080/19443994.2013.781545\u003c/li\u003e\n\u003cli\u003eCa\u0026ntilde;izares P, Dom\u0026iacute;nguez JA, Rodrigo MA, et al (1999) Effect of the current intensity in the electrochemical oxidation of aqueous phenol wastes at an activated carbon and steel anode. Ind Eng Chem Res 38:3779\u0026ndash;3785. https://doi.org/10.1021/ie9901574\u003c/li\u003e\n\u003cli\u003eChen G (2004) Electrochemical technologies in wastewater treatment. Sep Purif Technol 38:11\u0026ndash;41. https://doi.org/10.1016/j.seppur.2003.10.006\u003c/li\u003e\n\u003cli\u003eCrini G, Lichtfouse E (2019) Advantages and disadvantages of techniques used for wastewater treatment. Environ Chem Lett 17:145\u0026ndash;155. https://doi.org/10.1007/s10311-018-0785-9\u003c/li\u003e\n\u003cli\u003ede Caprariis B, De Filippis P, Hernandez AD, et al (2017) Pyrolysis wastewater treatment by adsorption on biochars produced by poplar biomass. J Environ Manage 197:231\u0026ndash;238. https://doi.org/10.1016/j.jenvman.2017.04.007\u003c/li\u003e\n\u003cli\u003eElazzouzi M, Haboubi K, Elyoubi MS (2019) Enhancement of electrocoagulation-flotation process for urban wastewater treatment using Al and Fe electrodes: Techno-economic study. Mater Today Proc 13:549\u0026ndash;555. https://doi.org/10.1016/j.matpr.2019.04.012\u003c/li\u003e\n\u003cli\u003eFil BA, Boncukcuoglu R, Yilmaz AE, Bayar S (2014) Electro-oxidation of pistachio processing industry wastewater using graphite anode. Clean - Soil, Air, Water 42:1232\u0026ndash;1238. https://doi.org/10.1002/clen.201300560\u003c/li\u003e\n\u003cli\u003eFrangos P, Shen W, Wang H, et al (2016) Improvement of the degradation of pesticide deethylatrazine by combining UV photolysis with electrochemical generation of hydrogen peroxide. Chem Eng J 291:215\u0026ndash;224. https://doi.org/10.1016/j.cej.2016.01.089\u003c/li\u003e\n\u003cli\u003eGodini K, Azarian G, Rahmani AR, Zolghadrnasab H (2013) Treatment of waste sludge: A comparison between anodic oxidation and electro-fenton processes. J Res Health Sci 13:188\u0026ndash;193. https://doi.org/10.34172/jrhs13876\u003c/li\u003e\n\u003cli\u003eG\u0026uuml;r E, Demirer GN (2019) Anaerobic Digestability and Biogas Production Capacity of Pistachio Processing Wastewater in UASB Reactors. J Environ Eng 145:04019042. https://doi.org/10.1061/(asce)ee.1943-7870.0001559\u003c/li\u003e\n\u003cli\u003eIsik Z, Arikan EB, Ozay Y, et al (2020) Electrocoagulation and electrooxidation pre-treatment effect on fungal treatment of pistachio processing wastewater. Chemosphere 244:125383. https://doi.org/10.1016/j.chemosphere.2019.125383\u003c/li\u003e\n\u003cli\u003eIsik Z, Bouchareb R, Saleh M, Dizge N (2021) Investigation of sesame processing wastewater treatment with combined electrochemical and membrane processes. Water Sci Technol 84:2652\u0026ndash;2660. https://doi.org/10.2166/wst.2021.152\u003c/li\u003e\n\u003cli\u003eLinares-Hern\u0026aacute;ndez I, Barrera-D\u0026iacute;az C, Bilyeu B, et al (2010) A combined electrocoagulation-electrooxidation treatment for industrial wastewater. J Hazard Mater 175:688\u0026ndash;694. https://doi.org/10.1016/j.jhazmat.2009.10.064\u003c/li\u003e\n\u003cli\u003eMart\u0026iacute;nez-Huitle CA, Ferro S (2006) Electrochemical oxidation of organic pollutants for the wastewater treatment: Direct and indirect processes. Chem Soc Rev 35:1324\u0026ndash;1340. https://doi.org/10.1039/b517632h\u003c/li\u003e\n\u003cli\u003eOzay Y, \u0026Uuml;nşar EK, Işık Z, et al (2018) Optimization of electrocoagulation process and combination of anaerobic digestion for the treatment of pistachio processing wastewater. J Clean Prod 196:42\u0026ndash;50. https://doi.org/10.1016/j.jclepro.2018.05.242\u003c/li\u003e\n\u003cli\u003ePeriyasamy S, Muthuchamy M (2018) Electrochemical oxidation of paracetamol in water by graphite anode: Effect of pH, electrolyte concentration and current density. J Environ Chem Eng 6:7358\u0026ndash;7367. https://doi.org/10.1016/j.jece.2018.08.036\u003c/li\u003e\n\u003cli\u003eRadha K V., Sridevi V, Kalaivani K (2009) Electrochemical oxidation for the treatment of textile industry wastewater. Bioresour Technol 100:987\u0026ndash;990. https://doi.org/10.1016/j.biortech.2008.06.048\u003c/li\u003e\n\u003cli\u003eRahmani AR, Godini K, Nematollahi D, Azarian G (2015a) Electrochemical oxidation of activated sludge by using direct and indirect anodic oxidation. Desalin Water Treat 56:2234\u0026ndash;2245. https://doi.org/10.1080/19443994.2014.958761\u003c/li\u003e\n\u003cli\u003eRahmani AR, Nematollahi D, Azarian G, et al (2015b) Activated sludge treatment by electro-Fenton process: Parameter optimization and degradation mechanism. Korean J Chem Eng 32:1570\u0026ndash;1577. https://doi.org/10.1007/s11814-014-0362-2\u003c/li\u003e\n\u003cli\u003eRahmani AR, Nematollahi D, Samarghandi MR, et al (2018) A combined advanced oxidation process: Electrooxidation-ozonation for antibiotic ciprofloxacin removal from aqueous solution. J Electroanal Chem 808:82\u0026ndash;89. https://doi.org/10.1016/j.jelechem.2017.11.067\u003c/li\u003e\n\u003cli\u003eShan J, Zheng Y, Shi B, et al (2019) Regulating electrocatalysts via surface and interface engineering for acidic water electrooxidation. ACS Energy Lett 4:2719\u0026ndash;2730. https://doi.org/10.1021/acsenergylett.9b01758\u003c/li\u003e\n\u003cli\u003eToth AJ, Haaz E, Szilagyi B, et al (2018) COD reduction of process wastewater with vacuum evaporation. Waste Treat Recover 3:1\u0026ndash;7. https://doi.org/10.1515/wtr-2018-0001\u003c/li\u003e\n\u003cli\u003eWang M, Payne KA, Tong S, Ergas SJ (2018) Hybrid algal photosynthesis and ion exchange (HAPIX) process for high ammonium strength wastewater treatment. Water Res 142:65\u0026ndash;74. https://doi.org/10.1016/j.watres.2018.05.043\u003c/li\u003e\n\u003cli\u003eXiao W, Ke S, Quan N, et al (2018) The Role of Nanobubbles in the Precipitation and Recovery of Organic-Phosphine-Containing Beneficiation Wastewater. Langmuir 34:6217\u0026ndash;6224. https://doi.org/10.1021/acs.langmuir.8b01123\u003c/li\u003e\n\u003cli\u003eYabalak E (2018a) An approach to apply eco-friendly subcritical water oxidation method in the mineralization of the antibiotic ampicillin. J Environ Chem Eng 6:7132\u0026ndash;7137. https://doi.org/10.1016/j.jece.2018.10.010\u003c/li\u003e\n\u003cli\u003eYabalak E (2018b) Degradation of ticarcillin by subcritical water oxidation method: Application of response surface methodology and artificial neural network modeling. J Environ Sci Heal - Part A Toxic/Hazardous Subst Environ Eng 53:975\u0026ndash;985. https://doi.org/10.1080/10934529.2018.1471023\u003c/li\u003e\n\u003cli\u003eYabalak E (2021) Treatment of agrochemical wastewater by thermally activated persulfate oxidation method: Evaluation of energy and reagent consumption. J Environ Chem Eng 9:105201. https://doi.org/10.1016/j.jece.2021.105201\u003c/li\u003e\n\u003cli\u003eYabalak E, G\u0026ouml;rmez \u0026Ouml;, Gizir AM (2018) Subcritical water oxidation of propham by H2O2 using response surface methodology (RSM). J Environ Sci Heal - Part B Pestic Food Contam Agric Wastes 53:334\u0026ndash;339. https://doi.org/10.1080/03601234.2018.1431468\u003c/li\u003e\n\u003cli\u003eYabalak E, Ozay Y, Vatanpour V, Dizge N (2021) Membrane concentrate management for textile wastewater with thermally activated persulfate oxidation method. Water Environ J 35:1281\u0026ndash;1292. https://doi.org/10.1111/wej.12718\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Pistachio wastewater, electrooxidation, subcritical water oxidation, RSM","lastPublishedDoi":"10.21203/rs.3.rs-1479310/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1479310/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"In this study, a hybrid system was used for pistachio wastewater treatment based on the electrooxidation treatment process and subcritical water oxidation. The effects of current density (50–150 A/m 2 ), experiments time (0–180 min), and wastewater initial pH (4-8) were optimized for maximum total phenols and chemical oxygen demand (COD) removal. The experimental study proved that the best conditions of current density and solution pH for COD and total phenol removal efficiencies were 150 A/m 2 and original pH of 5 giving removal efficiencies of 50% and 19.02%, respectively. The electrochemically pretreated wastewater was subjected to subcritical water oxidation (SWO). The effects of temperature (376–410 K), treatment time (26.4-93.65 min), and concentration of H 2 O 2 (0.08-0.92 M) were examined for COD and total phenol removal. The experimental and predicted COD and total phenol removal of the central composite design (CCD) models demonstrated that temperature, treatment time and concentration of H 2 O 2 have highly affected each of COD and total phenol removals. The highest removal efficiencies were as much as 54.3% and 76.87% for COD and total phenol at the following optimum conditions: temperature of 403 K, 40 min of treatment time and 0.75 M of H 2 O 2 concentration. The SWO results showed that, although treatment time affects the removal yields, it is not as effective as hydrogen peroxide concentration. In addition, it was found that COD and total phenol removal yields at the lowest values of all variables remained at 18.36% and 44.58%, respectively. Besides, they increased to 42.17% and 53.17%, respectively, by increasing only treatment time to its highest level. However, further increasing the treatment time after a specific level, may not increase the removal yields. Nevertheless, higher removal rates in both COD and total phenol were achieved even in the lowest level of the treatment time, but the highest level of the other two variables.","manuscriptTitle":"Electrooxidation and subcritical water oxidation hybrid process for pistachio wastewater treatment","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-05-27 16:36:09","doi":"10.21203/rs.3.rs-1479310/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"08f1a505-8ee1-4bdd-b221-9a7e950ba884","owner":[],"postedDate":"May 27th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2022-06-28T04:10:57+00:00","versionOfRecord":[],"versionCreatedAt":"2022-05-27 16:36:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1479310","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1479310","identity":"rs-1479310","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}