Accepted Manuscript Use of Response Surface Methodology for Pretreatment of Hospital Wastewater by O3/UV and O3/UV/H2O2 Processes Ayla Arslan, Sevil V...
Accepted Manuscript Use of Response Surface Methodology for Pretreatment of Hospital Wastewater by O3/UV and O3/UV/H2O2 Processes Ayla Arslan, Sevil Veli, Deniz Bingöl PII: DOI: Reference:
Please cite this article as: A. Arslan, S. Veli, D. Bingöl, Use of Response Surface Methodology for Pretreatment of Hospital Wastewater by O3/UV and O3/UV/H2O2 Processes, Separation and Purification Technology (2014), doi: http://dx.doi.org/10.1016/j.seppur.2014.05.036
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Use of Response Surface Methodology for Pretreatment of Hospital Wastewater by O3/UV and O3/UV/H2O2 Processes
Ayla Arslana, Sevil Velia, Deniz Bingölb∗ a
Department of Environmental Engineering, Kocaeli University, 41380 Kocaeli, Turkey. b
Department of Chemistry, Kocaeli University, 41380 Kocaeli, Turkey.
Abstract In this study, the ozonation of raw hospital wastewater was conducted in the batch reactor and the efficiency of this pretreatment was evaluated based on both chemical oxygen demand (COD)-in H2O2 and absorbance-non H2O2. Process variables as initial pH, reaction time, ozone concentration and H2O2 were investigated as major factors. Response surface methodology (RSM) was applied to evaluate the major factors influencing organic matter and color removal rates, and the interactions between these factors, and optimized the operating parameters as well. Quadratic models were developed with 60% absorbance and 46% COD as the responses, and the model predictions were confirmed to be appropriate to the experimental data with a deviation less than 3.5%.
1. Introduction Hospitals consume a significant amount of water in a day, ranging from 400 to 1200 L per bed per day [1-5]. The wastewater generated from hospitals is now recognized as a serious problem that may have detrimental effects either on the environment or on human beings through direct or indirect contact. Hospital wastewater contains high amount of disinfectants, pharmaceuticals, radioactive elements, solvents, microorganisms, heavy metals and toxic chemicals [5,6]. These effluents are generally discharged into urban drainage networks without prior treatment, in the same way as classical domestic wastewater [7]. Hospital wastewaters has long been treated with the conventional wastewater treatment processes, which are designed for the removal of BOD (biological oxygen demand) and SS (suspended solids), but not for refractory materials and pathogens [8]. Many pharmaceutical compounds are not able to degrade by microorganisms [5,9,10]. The different substances, which are not biodegradable, may finally enter surface water from wastewater treatment plant effluents, and enter groundwater when sewage sludge is used as fertilizers. Results of recent studies indicate the presence of low concentrations of antibiotics in municipal wastewater effluents and surface water [4]. Chemical oxidation, especially ozonation, has already been demonstrated to be an effective means of removing refractory and/or toxic chemicals from water and wastewater [11-17]. Ozone reacts with aqueous organic pollutants found in water and wastewater via two different pathways, namely direct molecular (pH≤2) and indirect (pH≥7) radical chain-type reactions [13,18]. At alkaline pH, ozone decomposes to secondary, more reactive, and hence less selective oxidants such as OH•, HO2•, HO3•, and HO4•, which initiate a free-radical reaction mechanism. The homolytic fission of hydrogen peroxide (H2O2) by UV-C irradiation known as photochemical advanced oxidation processes (AOPs) [19]. As a powerful oxidant, ozone has been proved to be capable of oxidizing many organic compounds to low molecular weight substances. Typically, ozonation doesn’t yield complete
mineralization to CO2 and H2O but leads to formation of partial oxidation products such as organic acids, aldehydes and ketones. Advantages of ozone are listed as follows, ozone can be applied directly in its gaseous state and therefore doesn’t increase the volume of wastewater and sludge, and ozone reaction time with pollutant is short [13]. Vasconcelos et al. [20] investigated the degradation of Ciprofloxacin (CIP) in hospital effluent with ozone and photobased processes. CIP is a broad-spectrum fluoroquinolone antimicrobial. Total degradation was achieved at pH 9, with 450 mg/h ozone concentrations after 30 min ozone treatment. It was shown that, photo- and ozone-based processes may be a suitable alternative to degradation of CIP. Balcıoğlu and Ötker [21] studied the treatment of pharmaceutical wastewater containing antibiotics by O3 and O3/H2O2 processes. Presence of 20 mM hydrogen peroxide in the ozonation process provided almost 100% of COD and UV absorbance removal. They showed that, ozonation could be successfully used as a pretreatment step to improve biodegradability of wastewater containing antibiotics. The pretreatment processes to reduce the toxicity of pollutants and enhance biodegradability of the hospital wastewaters have been proposed [3,4,6]. Ozone oxidation processes were also optimized using RSM with Box-Behnken design (BBD) [22-25] and Box–Wilson experimental design [26]. In conventional AOP methods, the experiments were usually conducted by varying some studied parameters while keeping others constant. To avoid repeating this process for all influential parameters, the RSM can be used to optimize the effective parameters, minimizing the number of experiments [22]. As reported, there are some studies about on the ozonation of wastewaters using RSM, this study is first focused on the ozonation of real hospital wastewater. The objective of the present study is to determine the optimal experimental conditions for pretreatment of raw hospital wastewater in ozonation combining O3/UV and O3/UV/H2O2 processes using RSM based on central composite design (CCD).
2. Material and methods 2.1. Raw wastewater and ozonation Wastewater used in this study was collected from the effluents of Medicine faculty of Kocaeli University. In the Hospital of Medicine Faculty of Kocaeli University with a capacity of 750 beds, Diagnostic Center, Nuclear Medicine, Oncology, Radiology, and Medical Genetics departments are located. Everyday nearly 60 surgeries are carried out and the number of patients treated in outpatient clinics on a daily basis is around 2000. The hospital has in operation a medical waste management system according to the national regulations that deals with the hazardous waste produced in the hospital. Average water consumption in the hospital is 430 m3/day. Samples were taken from the sewage lines collecting the wastewaters of the hospital. The sampling point was downstream of an in-line screening unit. This wastewater is diluted with wastewaters of living quarters and administrative units and then arrives to the campus biological treatment plant. The characterization of hospital wastewater was shown in Table 1. < Table 1> The experimental set-up for ozone treatment consisted of ozone generator, ozone monitor and reaction tank (Figure 1). The ozone generator was a Teknozone TKZ-25G, able to produce 25 g/h when it is fed with air. Ozone was generated from air by a plate type ozone generator. Ozone injection was applied by diffusers under the bottom of the reactor. Different flows of ozone were employed, in the range 5-20 g/h (with 9% concentration). The whole automation of the system has been controlled by Ozone Monitor ranged by ppm (parts per million). The Model TKZ-PPM51 Ozone Monitor (0.01-20.00 ppm) is designed to measure dissolved ozone gas in water and to work at on & off mode at the recommended set value. The reaction tank has been produced by heat & shock-resistant glass. UV-irradiations were performed by means of a low-pressure mercury lamps (2x2.2 W), emitting at 254 nm. The
lamps were placed inside the cylindrical reactor. The ozonation experiments were conducted in a batch reactor with volume of 2 L. At the beginning of the reaction, 1 L rough filtered wastewater was added into the reactor. Liquid samples were periodically taken from the reactor to determine the COD concentration and absorbance. The O3/UV/H2O2 experiments were performed the same way as the ozonation experiments with the addition of H2O2 prior to ozonation. The effluent O3 was absorbed by potassium iodide (KI) solution. < Figure 1> 2.2. Analysis The COD parameter was determined according to the “Close Reflux titrimetric methods” as given in Standard Methods [27]. Prior the COD analysis, the residual H2O2 concentration was determined via molybdate-catalysed iodometric method [28]. Total organic carbon (TOC) and total suspended solids (TSS) were determined according to the Standard Methods. The 5-day biochemical oxygen demand (BOD5) test was performed according to the International Standard ISO 7393 using Lovibond BOD-sensor. HACH-LANGE Dr 5000 spectrophotometer was used to measure the absorbance by scanning in the wavelength range of 200 to 800 nm. H2O2 (30% w/w, purchased from Merck) was used as oxidant. pH adjustments were carried out by using 0.1 N HCl and 0.1N NaOH aqueous solutions. pH measurements were done by using a digital
pH-meter (HACH, HQd). The oxidation
experiments were performed in duplicate. 2.3. Factorial designs Many experiments involve the study of the effects of two or more factors. Traditionally, it has been conventional to use the method of one factor at a time for optimization. The statistical design of experiments (DOE) allow for the evaluation of the statistical significance of individual process parameters, as well as the interaction between factors. The levels of the factors are investigated with all possible combinations by DOE.
Another major advantage of the DOE approach, the optimum system response is predicted based on a mathematical model. Response surface methodology (RSM) is a collection of mathematical and statistical techniques useful for the modeling and analysis of problems in which a response of interest is influenced by several variables and the objective is to optimize this response. This technique is employed to study the relationship between experimental factors and observed results, and the interactions among various factors [29]. In this study, RSM based on central composite design (CCD) was applied to develop mathematical models with linear, quadratic, and interaction terms for the ozonation of raw hospital wastewater at 5% probability level. The experimental factors of initial pH, reaction time, ozone concentration and dosage of H2O2 were investigated. Both the absorbance (threefactors: initial pH, O3 concentration, reaction time) and the COD (four-factors: initial pH, O3 concentration, reaction time, dosage of H2O2) measurements were evaluated as the removals by O3/UV and O3/UV/H2O2, respectively. 3. Results and discussion 3.1. Experimental design RSM use specific experimental design combinations to seek optimum performance from a given set of factors and response variables. A second-order polynomial model, as given below, was fitted to the experimental data for removals:
y = β 0 + β1 x1 + β 2 x2 + β3 x3 + β11 x12 + β 22 x22 + β33 x32 + β12 x12 + β13 x13 + β 23 x23 ……………………(1) where y is the removals in coded units, β 0 is a constant, β1 , β 2 and β3 are the regression coefficients for linear effects β11 , β 22 and β33 are the quadratic coefficients and, β12 , β13 and
β 23 are the interaction coefficients [30]. The following equation, the chosen independent variables X i is commonly used for coding as xi : xi = ( X i − X 0 ) Δx
(2)
where X 0 is the uncoded value of X i at the center point and Δx presents the step change [29]. RSM based on CCD is require an experiment number according to N = k 2 + 2k + c p , where k is the factor number and c p is the replicate number of the central point. All factors are studied in five levels ( −α , −1, 0, +1, +α ) . α -values can be calculated by α = 2k 4 . For two,
three, and four variables, they are, respectively, 1.41, 1.68, and 2.00 [31]. In this study, CCD design with three- and four-factors at five levels was applied using a statistical program package Minitab-16. The removals (%) were determined as the average of two parallel experiments. In order to minimize systematic errors, the experiments were performed in a random order. 3.1.1. COD removal by O3/UV/H2O2 processes
The experimental factors of initial pH, reaction time, ozone concentration and dosage of H2O2 were investigated. The COD measurements were used as the means of removals by O3/UV/H2O2. Degradation of organic matter in the absence of hydrogen peroxide was also studied. In this case, it was observed that too high concentration of ozone was necessary. Thus, a four-factor CCD was used to optimize O3/UV/H2O2 process of raw hospital wastewater. Coded and actual levels of factors (independent variables) for the rate of COD removal (dependent variable) were presented in Table 2. Experimental ranges of factors were selected as initial pH of 2-10; O3 concentration of 0-20 mg/L; reaction time of 30-60 min; and dosage of H2O2 of 0.500-3.000 mL. < Table 2>
Once the design is performed, it is then possible to assess the significance of each term using analysis of variance (ANOVA). A summary of the analysis results is given in Table 3 including the estimated effects, coefficients and ANOVA of the model with coded units. For the rate of COD removal, the process showed a statistically significant dependence upon
reaction time (P<0.05). The interaction between O3 concentration and dosage of H2O2 was also found to have a statistically significant effect upon the rate of COD removal (P<0.05). However, initial pH, ozone concentration and dosage of H2O2 did not exhibit a statistically significant effect upon the rate of COD removal (P>0.05). Their square interactions (pH*pH, O3*O3, H2O2*H2O2) and the interaction between O3 concentration and H2O2 (O3*H2O2) exhibit a statistically significant effect upon the rate of COD removal (P<0.05). Thus, the main effects (initial pH, reaction time, O3 concentration and dosage of H2O2) were also used for modelling the ozonation of raw hospital wastewater as statistically significant effects. A quantitative reduced model equation on ozonation of raw hospital wastewater can be written for the significant effects and interactions. The regression model is reduced to the significant terms and a prediction equation is written in coded term for the rate of COD removal: Re moval ( % ) = 38.5606 − 1.2360 X 1 + 1.9681X 2 + 3.8810 X 3
−0.0402 X 4 − 5.6439 X 12 − 14.2057 X 22 − 3.7857 X 42 + 5.9253 X 2 X 4
(3)
Initial pH and dosage of H2O2 exhibited a negative effect, while reaction time and ozone concentration had a positive effect upon the rate of COD removal. ANOVA results of the quadratic model presented in Table 3 indicated that the model could be used to estimate the rate of COD removal. The quality of the quadratic model expressed by the determination coefficient ( R 2 ) gives the proportion of the total variation in the response predicted by the model, and a high R 2 value is desirable. The model had a good fit considering the determination coefficient
( R ( adj ) = 95.61% ) 2
and only 4.39% of total
variation was not explained by the model. < Table 3>
The mathematical prediction model derived from the statistical analysis was used to generate the three-dimensional plots shown in Figures 2 and 3 below, demonstrating the
interaction between O3 concentration to dosage of H2O2 and initial pH to reaction time, respectively. Figure 2 shows the response surface analysis between O3 concentration to dosage of H2O2 had a statistically significant effect on the rate of COD removal at center level of initial pH and reaction time. As shown in the figure, the rate of COD removal favored for ozone concentration up to center level, and then rapidly decreased with increasing ozone concentration. Therefore, the rate of COD removal was affected from O3 concentration up to 10 mg/L. In the other hand, Figure 2 showed that the increase in dosage of H2O2 resulted in increased the rate of COD removal. < Figure 2>
Figure 3 shows the response surface analysis between initial pH to reaction time on the rate of COD removal at center level of O3 concentration and dosage of H2O2. The rate of COD removal increased for pH up to center level (pH≈ 6), and then rapidly decreased with increasing pH. The oxidising potential of ozone decreases from 2.08 V at lower pH values to about 1.4 V in alkaline solutions. This indicates that as the pH increases ozone stability decreases resulting in generation of secondary oxidants [32]. Also the high alkalinities of effluent, indicating the presence of bicarbonate ions terminate the chain reactions and inhibit ozone decay to hydroxyl radicals [17]. pH determines both kinetics and pathways of ozone reactions. Indeed, depending on the solution pH, the double action of ozone over organic matter, can lead to a direct or a free radical pathway. The direct pathway occurs at low pH when ozone molecule reacts exclusively with compounds with specific functional groups through selective reactions such as electrophilic, nucleophilic and dipolar addition. At alkaline conditions, takes place the indirect ozonation route, in which ozone decomposes yielding hydroxyl radicals, that are highly oxidizing species reacting in a nonselectively way with a wide range of organic and inorganic compounds in water [33,34].
< Figure 3>
3.1.2. Absorbance Removal by O3/UV process
A three-factor-five-level CCD was used for optimization of the removal conditions of absorbance from raw hospital wastewater using O3/UV process. Absorbance removal in the presence of hydrogen peroxide was also studied. However, addition of hydrogen peroxide was indicated negative effect on the removal, due to the scavenger effect of H2O2 dosage on OH• radicals [35]. Coded and actual levels of the three factors (initial pH, reaction time, O3 concentration) for the absorbance removal (dependent variable) are shown in Table 4. Experimental ranges of factors were selected as initial pH of 2-11; ozone concentration of 015 mg/L and reaction time of 20-60 min. < Table 4>
Table 4 shows estimated effects, coefficients and ANOVA of the model with coded units. For the absorbance removal, the process showed a statistically significant dependence upon O3 concentration (P<0.05). The interaction between pH and reaction time was also found to have a statistically significant effect upon the absorbance removal (P<0.05). However, initial pH and reaction time did not exhibit a statistically significant effect upon absorbance removal (P>0.05). Square interactions of pH (pH*pH) and the interaction between initial pH and reaction time (pH*time) exhibit a statistically significant effect upon the absorbance removal (P<0.05). Thus, the main effects (initial pH, reaction time and ozone concentration) were also used to model the ozonation of raw hospital wastewater as statistically significant effects for the absorbance removal. A quantitative reduced model equation on the ozonation of raw hospital wastewater using O3/UV process can be written for the significant effects. The regression model is reduced to the significant terms and a prediction equation is written in coded term for the absorbance removal:
Re moval ( % ) = 60.741 − 2.790 X 1 + 4.982 X 2 −1.028 X 3 + 7.212 X 12 − 3.625 X 1 X 2 + 4.900 X 1 X 3
(4)
Initial pH and reaction time exhibited a negative effect, while reaction time and ozone concentration had a positive effect upon the absorbance removal. ANOVA results of the quadratic model presented in Table 5 indicated that the model could be used to estimate the absorbance removal. The model was well fitted considering the determination coefficient ( R 2 ( adj ) = 89.44% ) and only 10.56% of total variation was not explained by the model.
< Table 5> Figures 4 and 5 shows the response surface analysis between initial pH to O3 concentration and initial pH to reaction time had a statistically significant effect on the absorbance removal at center level of reaction time and O3 concentration, respectively. It was interesting to note that in the absorbance removal, there was a negative interaction between initial pH and O3 concentration (Figure 4). As the value of one of these factors increased in the presence of the other, the absorbance removal reduced. Interestingly, pH exhibited a negative influence upon the absorbance removal up to approximately pH 7, meaning that lower pH favored higher the absorbance removal (for target 60%). The reaction time had a statistically minor effect upon the absorbance removal, and was relatively found to be not significant. In fact, twenty minutes were found to be sufficient on the absorbance removal in acidic pH (Figure 5). As shown in Figure 5, the absorbance removal favored up to approximately pH 9 values at O3 concentration 7.5 mg/L. At neutral pH, efficiency of the ozone reaction is lesser than acidic pH because of the reduction in ozone dissolution and less amount of hydroxyl free radical produced [32].
< Figure 4> < Figure 5>
The validation experiments confirmed the predictability of the proposed regression models. The optimal conditions for chemical oxygen demand (COD)-in H2O2 were initial pH 6.0, O3 concentration 10 mg/L and dosage of H2O2 1.8 mL within 60 min. The optimized set of conditions was verified experimentally to validate the model prediction. The rate of COD removal achieved 47.5% in test run, predicted as 46.3%. The optimal conditions for absorbance-non H2O2 were initial pH 8.0 and O3 concentration 4.2 mg/L within 27 min. The absorbance removal achieved 62.0% in test run, predicted as 60.0%. The optimal experiments performed in duplicate were consistent to the predicted response.
4. Conclusions The ozonation pretreatment of raw hospital wastewater by O3/UV and O3/UV/H2O2 process was optimized by RSM using CCD. These models based on CCD were in agreement with the experimental case with the high determination coefficients and high adjusted determination coefficients at 5% probability level. According to the experiment, the O3/UV/H2O2 process was effective for COD removal. The interaction between O3 concentration and dosage of H2O2 was found the most important factor affecting the process performance. However, the O3/UV process was effective for absorbance removal. There was a negative interaction between initial pH and O3 concentration in the absorbance removal. The results of validation experiments were close to the predicted response at optimal conditions. This study clearly shows that, the combined effect of variables on the ozonation pretreatment of raw hospital wastewater by O3/UV and O3/UV/H2O2 process by using RSM can be predicted; which is difficult to achieve with traditional methods.
Acknowledgements This work was financially supported by the Scientific Research Projects Administration Unit of Kocaeli University (No. 2010/54).
References [1] E. Emmanuel, Y. Perrodin, G. Keck, J.M. Blanchard, P. Vermande, Effects of hospital wastewater on aquatic ecosystem. XXXVIII Congreso Interamericano de Ingenieria Sanitariay Ambiental, Femisca, 27-31 Mexico, 2002. [2] E. Emmanuel, Y. Perrodin, G. Keck, J.M. Blanchard, P. Vermande, Ecotoxicological risk assessment of hospital wastewater: a proposed framework for raw effluents discharging into urban sewer network, J. Hazard. Mater. A 117 (2005) 1-11. [3] S., Suarez, J.M. Lema, F. Omil, Pre-treatment of hospital wastewater by coagulationflocculation and flotation, Bioresource Technol. 100 (2009) 2138-2146. [4] A.K. Gautam, S. Kumar, P.C. Sabumon, Preliminary study of physico-chemical treatment options for the hospital wastewater, J. Environ. Manage. 83 (2007) 298-306. [5] P. Gupta, N. Mathut, P. Bhatnagar, P. Nagar, S. Srivastava, Genotoxicity evaluation of the hospital wastewater, Ecotox. Environ. Safe. 72 (2009) 1925-1932. [6] P. Kajitvichyanukul, N. Suntronvipart, Evaluation of biodegradability and oxidation degree of the hospital wastewater using photo-Fenton process as the pretreatment method, J. Hazard. Mater. B 138 (2006) 384-391. [7] C. Boillot, C. Bazin, F. Tissot-Guerraz, J. Droguet, M. Perraud, J.C. Cetre, D. Trepo, Y. Perrodin, Daily physicochemical, microbiological and ecotoxicological fluctuations of a hospital effluent according to technical and care activities, Sci. Total Environ. 403 (2008) 113-129. [8] Q. Liu, Y. Zhou, L. Chen, X. Zheng, Application of MBR for hospital wastewater treatment in China, Desalination 250 (2010) 605-608. [9] J. Berto, G.C. Rochenbach, M.A.B. Barreiros, A.X.R. Correa, S. Pelusa-Silva, C.M. Radetski, Physico-chemical, microbiological and ecotoxicological evaluation of a septic
tank/Fenton reaction combination for the treatment of the hospital wastewaters, Ecotox. Environ. Safe. 72 (2009) 1076-1081. [10] H.K. Langford, V.K. Thomas, Determination of pharmaceutical compounds in hospital effluents and their contribution to wastewater treatment works, Environ. Int. 35 (2009) 766770. [11] A.M. Amat, A. Arques, M.A. Miranda, F. Lopez, Use of ozone and/or UV in the treatment of effluents from board paper industry, Chemosphere 60 (2005) 1111-1117. [12] T. Sreethawong, S. Chavadej, Color removal of distillery wastewater by ozonation in the absence and presence of immobilized iron oxide catalyst, J. Hazard. Mater. 155 (2008) 486493. [13] V. Preethi, K.S. Parama Kalyani, K. Iyappan, C. Srinivasakannan, N. Balasubramaniam, and N. Vedaraman, Ozonation of tannery effluent for removal of cod and color, J. Hazard. Mater. 166 (2009) 150-154. [14] M.S. Lucas, J.A. Peres, G.L. Puma, Treatment of winery wastewater by ozone-based advanced oxidation processes (O3, O3/UV and O3/UV /H2O2) in a pilot-scale buble column reactor and process economics, Sep. Purif. Technol. 72 (2010) 235-241. [15] T. Ratpukdi, S. Siripattanakul, E. Khan, Mineralization and biodegradability enhancement of natural organic matter by ozone-VUV in comparison with ozone, VUV, ozone-VUV, and UV: Effects of pH and ozone dose, Water Res. 44 (2010) 3531-3543. [16] C.A. Somensi, E.L. Simionatto, S.L. Bertoli, A. Wisniewski Jr, Use of ozone in pilotscale plant for textile wastewater pre-treatment: Physico-chemical efficiency, degradation byproducts identification and environmental toxicity of treatted wastewater, J. Hazard. Mater. 175 (2010) 235-240. [17] S. Tripathi, V. Pathak, D.M. Tripathi, B.D. Tripathi, Application of ozone based treatments of secondary effluents, Bioresource Technol. 102 (2011) 2481-2486.
[18] C. Tizaoui, N. Grima, Kinetics of the ozone oxidation of reactive orange 16 azo-dye in aqueous solution, Chem. Eng. J. 173 (2011) 463-473. [19] I. Arslan-Alaton, İ. Alaton, Degradation of xenobiotics originating from the textile preparation, dyeing, and finishing industry using ozonation and advanced oxidation, Ecotox. Environ. Safe. 68 (2007) 98-107. [20] T.G. Vasconselos, K. Kümmerer, D.M. Henriques, A.F. Martins, Ciprofloxacin in Hospital Effluent: Degradation by ozone and photoprocesses, J. Hazard. Mater. 169 (2009) 1154-1158. [21] I. Akmehmet Balcıoğlu, M. Ötker, Treatment of pharmaceutical wastewater containing antibiotics by O3 and O3/H2O2 processes, Chemosphere 50 (2003) 85-95. [22] J.-K. Im, I.-H. Cho, S.-K. Kim, K.-D. Zoh, Optimization of carbamazepine removal in O3/UV/H2O2 system using a response surface methodology with central composite design, Desalination 285 (2012) 306-314. [23] B.K. Tiwari, K. Muthukumarappan, C.P. O’Donnell, P.J. Cullen, Modelling colour degradation of orange juice by ozone treatment using response surface methodology, J. Food Eng. 88 (2008) 553-560. [24] Z. Zeng, H. Zou, X. Li, B. Sun, J. Chen, L. Shao, Ozonation of acidic phenol wastewater with O3/Fe(II) in a rotating packed bed reactor: Optimization by response surface methodology, Chem. Eng. Process. 60 (2012) 1-8. [25] X.-J. Zhou, W.-Q. Guo, S.-S. Yang, N.-Q. Ren, A rapid and low energy consumption method to decolorize the high concentration triphenylmethane dye wastewater: Operational parameters optimization for the ultrasonic-assisted ozone oxidation process, Bioresource Technol. 105 (2012) 40-47.
[26] E.Ç. Çatalkaya, F. Şengül, Application of Box–Wilson experimental design method for the photodegradation of bakery’s yeast industry with UV/H2O2 and UV/H2O2/Fe(II) process, J. Hazard. Mater. B 128 (2006) 201-207. [27] APHA, Standard Methods for the Examination of Water and Wastewater, twenty-first ed. American Public Health Association, Washington, DC, 2005. [28] Y.W. Kang, M-J. Cho, K-Y. Hwang, Correction of hydrogen peroxide interference on standard chemical oxygen demand test, Wat. Res. 33 (1999) 1247-1251. [29] D.C. Montgomery, Design and Analysis of Experiments, 7nd ed., John Wiley & Sons. 2008. [30] P.A. Soloman, C. Ahmed Basha, M. Velana, N. Balasubramanian, P. Marimuthu, Augmentation of biodegradability of pulp and paper industry wastewater by electrochemical pre-treatment and optimization by RSM, Sep. Purif. Technol. 69 (2009) 109-117. [31] M.A. Bezerra, R.E. Santelli, E.P. Oliveira, L.S. Villar, L.A. Escaleira, Response surface methodology (RSM) as a tool for optimization in analytical chemistry, Talanta 76 (2008) 965977. [32] M. Muthukumar, N. Selvakumar, Studies on the effect of inorganic salts on decolouration of acid dye effluents by ozonation, Dyes Pigm. 62 (2004) 221-228. [33] M. Pera-Titus, V. García-Molina, J. Baños, M., Giménez, S. Esplugas, Degradation of chlorophenols by means of advanced oxidation processes: a general review, Appl. Catal. BEnviron. 47 (2004) 219-256. [34] T. Agustina, H. Ang, V. Vareek, A review of synergistic effect of photocatalysis and ozonation on wastewater treatment, J. Photochem. Photobiol. C: Photochem. Rev. 6 (2005) 264-273.
[35] T.P. Sauer, L. Casaril, A.L.B. Oberziner, H.J. Jose and R.F.P.M. Moreira, Advanced oxidation processes applied to tannery wastewater containing Direct Black 38-Elimination and degradation kinetics, J. Hazard. Mater. B135 (2006) 274-279.
Figure Captions
Figure 1. Schematic view of the ozonation unit Figure 2. Three-dimensional plot of O3 concentration-dosage of H2O2 interaction the rate of COD removal Figure 3. Three-dimensional plot of initial pH-reaction time interaction the rate of COD removal Figure 4. Three-dimensional plot of initial pH-O3 concentration interaction for the absorbance removal Figure 5. Three-dimensional plot of initial pH- reaction time for the absorbance removal
Table 3. Estimated effects, coefficients and analysis of variance with coded units for suggested quadratic model Terms Constant pH ( X 1 )
Coeff. 39.2279 -1.2360
SE 2.761 1.491
T 14.207 -0.829
P 0.000 0.411
O3 ( X 2 )
1.9681
1.491
1.320
0.193
time ( X 3 )
3.8810
1.491
2.603
0.012
H2O2 ( X 4 )
-0.0402
1.491
-0.027
0.979
pH*pH ( X 12 )
-5.7134
1.366
-4.182
0.000
O3*O3 ( X 22 )
-14.2752
1.366
-10.449
0.000
time*time ( X 32 )
-0.6534
1.366
-0.478
0.635
2 4
H2O2*H2O2 ( X )
-3.8552
1.366
-2.822
0.007
pH*O3 ( X 1 X 2 )
-1.6553
1.826
-0.906
0.369
pH*time ( X 1 X 3 )
1.7109
1.826
0.937
0.354
pH*H2O2 ( X 1 X 4 )
2.0797
1.826
1.139
0.261
O3*time ( X 2 X 3 )
2.4753
1.826
1.355
0.182
O3*H2O2 ( X 2 X 4 )
5.9253
1.826
3.244
0.002
Time*H2O2 ( X 3 X 4 )
1.3566
1.826
0.743
0.461
Values for the reduced model with significant coefficients, S = 3.3622, R-Sq= 96.18%, R-Sq(pred)= 94.91%, R-Sq(adj)= 95.61% Analysis of variance used for suggested quadratic model Source DF Seq SS Adj SS Adj MS F P Regression 8 15093.8 15093.8 1886.7 166.90 0.000 Residual Error 53 599.1 599.1 11.3 Total 61 15693,0 Coeff.: Coefficients, SE: Standart errors, T: Value T, P: Probability, DF: Degrees of freedom, Seq SS: Sequential sum of Squares, Adj MS: Adjusted sum of squares, F: Factor,F
Table 4. Design matrix and levels based on the Central Composite Design Run 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Table 5. Estimated effects, coefficients and analysis of variance with coded units for suggested quadratic model Terms Constant pH ( X 1 )
Coeff. 62.0191 -2.7901
SE 2.125 1.410
T 29.187 -1.979
P 0.000 0.057
O3 ( X 2 )
4.9818
1.410
3.534
0.001
-1.0280
1.410
-0.729
0.472
pH*pH ( X
7.0566
1.372
5.142
0.000
O3*O3 ( X
-2.5778
1.372
-1.878
0.070
time*time ( X 32 )
0.8605
1.372
0.627
0.535
pH*O3 ( X 1 X 2 )
-3.6250
1.842
-1.968
0.058
pH*time ( X 1 X 3 )
4.9000
1.842
2.660
0.012
O3*time ( X 2 X 3 )
2.1000
1.842
1.140
0.263
time ( X 3 ) 2 1 ) 2 2 )
Values for the reduced model with significant coefficients, S = 3.0069, R-Sq= 91.06%, R-Sq(pred)= 86.82%, R-Sq(adj)= 89.44% Analysis of variance used for suggested quadratic model Source DF Seq SS Adj SS Adj MS F P Regression 6 3040.17 3040.17 506.69 56.04 0.000 Residual Error 33 298.37 298.37 9.04 Total 39 3338.53 Coeff.: Coefficients, SE: Standart errors, T: Value T, P: Probability, DF: Degrees of freedom, Seq SS: Sequential sum of Squares, Adj MS: Adjusted sum of squares, F: Factor,F
Graphical Abstract
Hospital wastewater
Ozonation
Highlights: > Ozonation pre-treatment of raw hospital wastewater was conducted in a batch reactor. > Ozonation of raw hospital wastewater was generated by O3/UV and O3/UV/H2O2 process. > Chemical oxygen demand (COD)-in H2O2 and absorbance-non H2O2 was evaluated. > Ozonation processes were optimized using RSM (response surface methodology).
