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Keywords:

  • photo-Fenton-like;
  • polyacrylic acid;
  • TOC removal;
  • statistical analysis;
  • process optimisation;
  • advanced oxidation technologies

Abstract

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS AND DISCUSSION
  6. CONCLUSIONS
  7. NOMENCLATURE
  8. ACKNOWLEDGEMENTS
  9. REFERENCES

The effectiveness of photo-Fenton-like process to degrade aqueous polyacrylic acid (PAA) is investigated in a batch recirculation system using two photoreactors in series. The response surface methodology (RSM) using the Box–Behnken experimental design combined with quadratic programming is employed for the experimental design, the statistical analysis and the optimisation. The effects of the initial concentration of PAA, the initial concentration of H2O2:Fe3+, pH and the recirculation rate on the percent removal of total organic carbon (TOC)and the ‘pseudo’-second order rate constant as the process responses are studied. The statistical analysis of the results indicates a satisfactory prediction of the system behaviour by the developed quadratic models. Optimum operating conditions to maximise the percent TOC removal are also determined.


INTRODUCTION

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS AND DISCUSSION
  6. CONCLUSIONS
  7. NOMENCLATURE
  8. ACKNOWLEDGEMENTS
  9. REFERENCES

Synthetic water-soluble polymers are gaining more attention due to industrial developments. Generally, they are known as specialty polymers with functional groups such as carboxyl, hydroxyl and amino groups that have applications in diverse fields such as packaging, agriculture, detergents, superabsorbents and others.[1, 2] Nevertheless, the knowledge of the synthetic water-soluble polymers entering the environment is still sparse and due to their versatility, the environmental pollution by this type of polymers has become a major concern.[3, 4] Also, as a result of their water solubility, they are able to reach sewage disposal systems and consequently contaminate water resources.[5] On the other hand, since they contain a low concentration of polymers, they neither can be recycled nor incinerated. Therefore, in order to achieve applicable and robust results, experimental and analytical methods must be adapted to gain insight into their degradation process.

Advanced oxidation technologies (AOTs) as an alternative method to the conventional treatment technologies have been applied successfully to enhance the biodegradability of recalcitrant pollutants in wastewater.[6-16] AOTs through the use of short-lived highly reactive oxygen derived species render the contaminants to less toxic intermediates.[17-21] However, only few studies are available in the open literature on the photochemical degradation and mineralisation of polymers[5, 22-24]. The photo-Fenton-like process, as one of the AOTs, has received great attention in recent decades due to its capacity in oxidising and mineralising recalcitrant compounds that are not amenable to biodegradation.[25, 26] In the photo-Fenton-like process, hydroxyl radical (OH), a powerful non-selective source of oxidation, is generated from H2O2 in the presence of Fe3+ ions based on the following simplified reactions[27, 28]:

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The great oxidation potential of these short-lived free radicals (especially OH) results in the fast degradation of target organic compounds. The hydrogen abstraction from the main chain of polymers by generated free radicals is the main cause of the polymer photodegradation.[29] The main advantages of the Fenton reagent are its ability to convert a wide range of pollutants to biodegradable products without environmental threat by its residual reagents, the ease of operation and the relatively low cost of the reagents.[27] The photo-Fenton-like process, similar to other AOTs, is a multifactor system where its efficiency is affected by different parameters such as initial concentrations of organic compounds, H2O2 and iron, as well as pH and other operating conditions. Therefore, cross-factor effects must be considered to characterise the process and determine the synergetic effects of the process variables.

The experimental design has been applied successfully to identify the most influential factors in multivariable systems. The response surface methodology (RSM) is able to optimise the operating conditions in multifactor systems by considering the interactions among variables.[30] Also, RSM has been proven as a reliable statistical tool in studying chemical treatment processes to achieve an optimal response with a minimum number of experiments.

In this study, the experimental design for the photodegradation of polyacrylic acid (PAA) by photo-Fenton-like process was investigated in a batch recirculation system using two photoreactors in series. PAA, a carbon chain water-soluble polymer with the pendant carboxylic acids known to be recalcitrant to biodegradation, was considered as a model water-soluble polymer. The effects of the initial concentrations of PAA and H2O2:Fe3+, pH and the recirculation rate on the percent TOC removal and the fitted values of ‘pseudo’-second order mineralisation rate constant as the process responses were studied using a four-factor three-level Box–Behnken experimental design combined with RSM and quadratic programming.

MATERIALS AND METHODS

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS AND DISCUSSION
  6. CONCLUSIONS
  7. NOMENCLATURE
  8. ACKNOWLEDGEMENTS
  9. REFERENCES

Materials

PAA (35 wt%) with an average molecular weight (Mw) of 100,000 g mol−1, H2O2 (30 wt%) and FeCl3·6H2O (Sigma–Aldrich) were used as received. NaOH (99%) and H2SO4 (99%; EMD Chemicals, Gibbstown, NJ) were used as received to adjust pH.

Experimental Setup and Procedure

As shown in Figure 1, a laboratory-scale batch recirculation photoreactor set up including two photoreactors in series providing uniform light distribution was used in this study. A small annular photoreactor (SL-LAB; Siemens Inc.) with the annular space of 1.33 cm was used as a part of the recycle system including a centrifugal magnetic pump (Model RK-72012-10; Cole-Parmer, Tokyo, Japan), an all glass heat exchanger for controlling the temperature, and a large volume tank with provisions for sampling and temperature measurements. The system was also equipped with a by-pass valve to control the flow rate and to provide a relief to the pump pressure. The low-pressure Hg lamp with input power of 14 W with UV emission peaks at 254 nm (LP4130, Siemens Inc.) sealed with the quartz sleeve was positioned at the centerline of each photoreactor with stainless steel housing. This particular geometry (very small annular space) and the method of irradiation lead to have a good approximation of an isoactinic condition (uniform light distribution) in the photoreactor.[31] The pH was adjusted at the beginning of each experiment by adding a few drops of 1 N NaOH or H2SO4 as needed and it was measured by a portable pH meter (230A plus, Thermo Orion). No further pH adjustment was made during the experiments.

image

Figure 1. Schematic diagram of the experimental setup in batch recirculation mode. (1) Reservoir tank, (2) Pump, (3) Flow meter, (4) UV photoreactor, (5) Heat exchanger, (6) Cooling water inlet, (7) Water outlet, (8) Bypass and (9) UV photoreactor.

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The following protocol was pursued in conducting each experiment. The PAA solution was diluted to achieve the desired PAA concentration in a 4-L solution. The lamp was turned on for 30 min before the beginning of each experiment to stabilise the light intensity. A desired dosage of oxidisers (H2O2 and Fe3+) was added to the solution at the beginning of each experiment. The solution was fed to the system and the temperature was kept constant at 25°C during each experiment by means of the heat exchanger. The operating pressure was at ambient. The samples were taken from the collection tank for further analysis. The TOC concentration of the samples was measured by a TOC analyser (Apollo 9000, Teledyne Tekmar, Mason, OH) immediately after taking the samples.

Experimental Design and Optimisation Studies by RSM

The optimum conditions for maximising the percent TOC removal were determined by means of a four-factor three-level Box–Behnken experimental design combined with the response surface methodology to correlate experimentally obtained criteria and experimental conditions given by Box–Behnken experimental design. The Box–Behnken design, a modified central composite design, is known as an independent and rotatable quadratic design having no fractional factorial points.[32] In this type of design, the variable combinations are at the centre and the midpoints of the edges of the variable space.[33] Also, compared to other types of experimental designs such as full factorial design, the Box–Behnken experimental design needs fewer experimental trials. Therefore, the effects of four independent variables on the response functions were investigated. The independent variables were initial concentrations of PAA (X1) and H2O2:Fe3+(X2), pH (X3), and recirculation rate (X4) that were coded as −1, 0 and +1 as shown in Table 1. The total number of experimental trials was 27 based on three levels and a four factor experimental design, with three replicates at the centre of the design, to estimate a pure error sum of squares. The percent TOC removal and the ‘pseudo’-second order rate constant were considered as the dependent factors (process responses). The independent variables and their critical experimental levels as shown in Table 1 were chosen based on the preliminary experimental results and the values reported in the open literature.[34-36] In the RSM, as the initial step, an appropriate approximation which is generally a first-order model is applied to find the true functional relationship between the response function and the set of factors. However, in the first-order model, there is a lack of fit due to the existence of the surface curvature. Therefore, the first-order model was upgraded by adding higher order terms.[37, 38] Consequently, in the next step, the experimental data from the Box–Behnken experimental design was fitted to the following quadratic equation:

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where Y, βo, βi, βii and βij are the predicted response, the constant coefficient (intercept term), the linear coefficients, the quadratic coefficients and the interaction coefficients, respectively. The parameters Xi and Xj are independent variables, where k (in this case, k = 4) and e are the number of factors and the residual term allowing uncertainties between observed and predicted values, respectively. The statistical softwares, STATISTICA (trial version 10.0) and Design-Expert (trial version 8.0) were used for the regression analysis and the parameter estimation of the response functions, respectively. The statistical significance of the model equations was completely analysed using analysis of variance (ANOVA) at 95% confidence intervals. Three-dimensional response surface plots and two-dimensional contour plots were developed while holding a variable constant in the quadratic model. The experimental and predicted values were compared to validate the developed models. The optimal operating conditions to maximise the percent TOC removal were also determined using a numerical technique built in the software Design-Expert 8.0. The ‘pseudo’-second order rate constant was calculated at the determined optimal conditions. Also, another experimental trial was carried out to verify the obtained optimal conditions by the developed models for both response functions.

Table 1. Independent variables and their coded levels based on Box–Behnken design
Independent variableSymbolCoded Levels
-101
[PAA]o (mg L−1)X1103050
[H2O2:Fe3+]o (mg L−1:(mg L−1))X2400:4900:61600:8
pHX3357
Recirculation rate (L min−1)X41610

RESULTS AND DISCUSSION

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS AND DISCUSSION
  6. CONCLUSIONS
  7. NOMENCLATURE
  8. ACKNOWLEDGEMENTS
  9. REFERENCES

Preliminary Experiments

In order to obtain a preliminary insight into the TOC removal efficiency, different processes including UV alone, UV/H2O2, UV/Fe3+ and UV/H2O2/Fe3+ for one photoreactor and two photoreactors in series were carried out. In each case, the TOC of the PAA solution was determined during the total reaction time of 120 min. Dark experiments did not show any TOC removal after 120 min. The results of TOC removal for these processes, operated in recirculating batch mode, are presented in Figure 2. As illustrated in Figure 2, the TOC removal efficiency in one single reactor using UV alone was negligible (Figure 2a). Also, the TOC removal efficiency in two photoreactors in series showed only 33% reduction using UV alone. In the UV/H2O2 process, a drastic improvement in the TOC removal efficiency was observed in both single photoreactor and two photoreactors in series. The TOC reduction by the UV/H2O2 process was due to the attack of the free radicals to the polymer molecules. The free radicals (mainly .OH) are generated by Reaction (4) and the following summarised reactions:

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Figure 2. Profiles of TOC removal in: (a) one photoreactor; and (b) two photoreactors in series for UV, UV/H2O2, UV/Fe3+ and UV/H2O2/Fe3+ processes ([PAA]o = 30 mg L−1, [H2O2]:[Fe3+] = 900:30 mg L−1:mg L−1, pH 3 and recirculation rate = 4 L min−1).

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However, the TOC reduction by the UV/H2O2 process in two photoreactors in series was higher because of the higher light intensity resulting in the generation of more free radicals (Reaction 4). As illustrated in Figure 2a and b, the photo-Fenton process (UV/Fe3+) in the single photoreactor and two photoreactors in series did not show high TOC removal efficiencies (less than 45%). This is due to the lower quantum yield of the hydroxyl radical production by Reaction (3) (0.2 mol Einstein−1) than the quantum yield of Reaction (4) (0.5 mol Einstein−1).[39] The highest TOC removal efficiency was correspondent to the photo-Fenton-like process (UV/H2O2/Fe3+) using two photoreactors in series as shown in Figure 2b. Therefore, the effectiveness of the photo-Fenton-like process with two photoreactors in series as the most efficient process was studied using response surface methodology.

RSM Model Development

The efficiency of the photo-Fenton-like process (UV/H2O2/Fe3+) for the treatment of aqueous PAA in a batch recirculation system using two photoreactors in series was investigated by Box–Behnken experimental design including four factors at three levels. The process parameters taken into account were the initial concentrations of PAA and H2O2:Fe3+, pH and the recirculation rate. The effectiveness of the process was evaluated through the percent TOC removal for the model wastewater. Also, the performance of the RSM on the ‘pseudo’-second order rate constant was studied in order to indirectly introduce another parameter (treatment time) in the experimental design.[40] In order to compare the reaction rates, the reaction order has to be determined and then the rate constant could be easily calculated and compared through the linear regression if all of them could be fitted to the same reaction order.[41] Therefore, several functional dependences of the TOC concentration on time representing different integral order of reactions (zero, first and second) were examined. It was determined that the reaction rates of the TOC removal for all 27 experimental trials were fitted well to the ‘pseudo’-second order kinetics ((1/TOCt) − (1/TOCo) = kobs × t) as shown in Figure 3. The expression to achieve the ‘pseudo’-second order rate constant is found by integrating the following equation:

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Figure 3. ‘Pseudo’-second order kinetic fit for experimentally achieved TOC in 27 experimental trials. (See Table 2 for the conditions of each experimental trial.)

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The range of linear regression coefficients (R2) was from 0.91 to 0.99. The four-factor Box–Behnken experimental design and the observed and predicted values for the percent TOC removal and the ‘pseudo’-second order reaction constant by the developed quadratic models are shown in Table 2. As mentioned earlier, the RSM was used to estimate the parameters indicating an empirical relationship between the input variables and the responses as shown in Equation (5). Therefore, the quadratic model equations for predicting the percent TOC removal (Y1) and the ‘pseudo’-second order rate constant (Y2) are presented in the following second order polynomial equations:

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where X1, X2, X3 and X4 are coded terms for the four independent variables indicating the initial concentrations of PAA and H2O2:Fe3+, pH and the recirculation rate, respectively. The negative coefficients for the model components X1, X3, X1X3, X3X4, inline image, inline image and inline image in Y1 and X1, X3, X1X4 and X2X4 in Y2 indicate the unfavourable effects on the percent TOC removal and ‘pseudo’-second order rate constant, respectively. While, the positive coefficients for X2, X4, X1X2, X1X4, X2X3, X2X4 and inline image in Y1 and X2, X4, X1X2, X1X3, X2X3, X3X4, inline image, inline image, inline image and inline image in Y2 indicate favourable effects on the percent TOC removal and ‘pseudo’-second order rate constant, respectively. The significance of each model parameter will be determined by the statistical analysis.

Table 2. Four-factor Box–Behnken design for RSM and the observed and predicted responses
RunIndependent coded variablesTOC removal (%)kobs  × 10–4 (M−1 s−1)
X1X2X3X4ObservedPredictedObservedPredicted
1010−189.9891.082429
20−10187.5587.182124
3001180.9880.841418
4−110095.1392.826962
5010190.3092.162429
6−10−1095.6594.467476
7110088.2188.022216
8100−187.5385.401314
9100187.8685.301315
10001−180.1780.271410
11000089.2689.631919
12−100191.2093.146764
13−1−10089.8691.025555
14011083.3482.351817
15000088.9789.631919
1600−1−191.9492.943535
170−11079.4977.27127
18−100−192.6892.206258
19−101082.8984.102940
2000−1192.3593.213634
2101−1093.6694.103235
221−1 077.9181.1867
230−1−1091.3490.552928
24101072.6174.6249
250−10−188.4487.421517
26000090.7089.261919
2710−1089.7189.301714

Statistical Analysis

The ANOVA was applied as the most important test to evaluate the significance of the developed quadratic model for the prediction of the percent TOC removal and the ‘pseudo’-second order rate constant. The results of the ANOVA are presented in Tables 3 and 4 for the percent TOC removal and the ‘pseudo’-second order rate constant, respectively. The significance of each coefficient in Equations (12) and (13) was determined by the Fisher's F-test and values of probability greater than F. The acceptance or rejection of the model terms were based on the P-value with the 95% confidence level. As shown in Tables 3 and 4, small probability values (P < 0.0001) indicate that the models were highly significant which confirm the accuracy of the developed models to predict the response functions. Also, high values of regression coefficient (R2) as well as adjusted regression coefficient (inline image) ensure an acceptable modification of the quadratic model to the experimental data. In fact, inline image modifies R2 for the sample size and the number of terms in the model. The values of R2 and inline image were found to be 0.9363 and 0.8620 for the percent TOC removal and 0.9576 and 0.9081 for the ‘pseudo’-second order rate constant, respectively. The closer the values of R2 and inline image are to 1, the better the prediction of models for the percent TOC removal and the ‘pseudo’-second order rate constant is. The high values of regression and adjusted regression coefficients indicate that the developed quadratic models could adequately describe the system behaviour within the selected range of operating parameters. Also, the adequate precisions, the signal to the noise ratio, greater than 4 (12.80 for percent TOC removal and 15.69 for the ‘pseudo’-second order rate constant) show that the models could be used to navigate the design space. The high correlations between the observed and the predicted data are illustrated in Figures 4a and 5a. As a result of low discrepancies, the points are very close to the diagonal line, indicating a good agreement between the actual and the predicted data. The normality of the data could be checked through the normal probability plot of the residuals as shown in Figures 4b and 5b. Since the points on the plot follow a straight line, it could be concluded that the residuals are normally distributed. Also, as the S-shaped curve is not formed in Figures 4b and 5b, the response transformations are not needed.[30] Therefore, it could be concluded that the predictions of the experimental data by developed quadartic models for both the percent TOC removal and the ‘pseudo’-second order rate constant are quite satisfactory.

Table 3. ANOVA results for prediction of percent TOC removal
Factors (coded)Statistics
SSadfbMSc = SS/dfF-valueP-valued Prob > F
  1. a

    Sum of squares.

  2. b

    Degree of freedom.

  3. c

    Mean square.

  4. d

    P < 0.05 is considered as significant.

Model761.571454.4012.60<0.0001
X1160.601160.6037.20<0.0001
X256.03156.0312.980.0036
X3469.631469.63108.77<0.0001
X40.5510.550.130.7283
X1X26.3316.331.470.2494
X1X34.7114.711.090.3169
X1X40.2810.280.0640.8048
X2X30.5910.590.140.7192
X2X40.4310.430.0990.7580
X3X40.02310.0235.21E-0030.9436
inline image4.3914.391.020.3331
inline image1.1511.150.270.6156
inline image51.21151.2111.860.0049
inline image0.4310.430.100.7569
Residual51.81124.32  
Pure error0.6620.33  
Table 4. ANOVA results for prediction of ‘pseudo’-second order rate constant
Factors (coded)Statistics
SSadfbMSc = SS/dfF-valueP-valued Prob > F
  1. a

    Sum of squares.

  2. b

    Degree of freedom.

  3. c

    Mean square.

  4. d

    P < 0.05 is considered as significant.

Model9625.9414687.5719.34<0.0001
X16580.0816580.08185.10<0.0001
X2216.751216.756.100.0295
X31240.3311240.3334.89<0.0001
X440.33140.331.130.3078
X1X21.0011.000.0280.8696
X1X3256.001256.007.200.0199
X1X46.2516.250.180.6824
X2X32.2512.250.0630.8056
X2X49.0019.000.250.6240
X3X420.25120.250.570.4650
inline image1134.2611134.2631.910.0001
inline image13.37113.370.380.5511
inline image7.7917.790.220.6481
inline image94.45194.452.660.1290
Residual426.581235.55  
Pure error0.00020.000  
image

Figure 4. Validation of models for the TOC percent removal using different plots of: (a) observed experimental data versus predicted values and (b) normal probability.

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image

Figure 5. Validation of the prediction of ‘pseudo’-second order rate constant using different plots of: (a) observed experimental data versus predicted values; and (b) normal probability.

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Effects of Model Parameters and Their Interactions

As the significance of the models was confirmed (Tables 3 and 4) and the predictivity of the experimental data by the developed models found to be accurate (Figures 4 and 5), the next step is to evaluate the significance of each model parameter. Hence, the most influential process parameters on the percent TOC removal and the ‘pseudo’-second order rate constant could be determined. This evaluation is based on the F and P-values for each of the linear, quadratic and interaction model paramaters as listed in Tables 3 and 4. Generally, Fischer's F-value is used to determine the significance of the regression coefficients of the variables while P-value determines the significance of the variable that may indicate the pattern of interaction among variables. The larger the value of F and the smaller the value of P, the more siginificant is the corresponding coefficient.[42] As shown in Tables 3 and 4, the P-value less than 0.05 indicate the model parameters are significant. As presented in Tables 3 and 4, the initial concentrations of PAA and H2O2:Fe3+ as well as pH have significent linear effects on the percent TOC removal and ‘pseudo’-second order rate constant. The effect of the initial concentration of PAA on the percent TOC removal and ‘pseudo’-second order rate constant is mainly due to the absorption of UV radiation by PAA molecules. An increase in the initial concentration of PAA results in inducing an inner filter effect, that is the UV light penetration becomes less due to less permeability of the solution.[43] This leads to a decrease in the UV radiation absorption by H2O2 and Fe3+ and subsequently less hydroxyl radical formation which is the main cause of the PAA degradation. Also, the high amount of PAA molecules reduces their chances to be in contact with hydroxyl radicals and consequently fewer molecules are attacked by the hydroxyl radicals. Based on Reactions (1) and (2), higher concentrations of hydrogen peroxide and ferric iron result in the formation of more ferrous iron and subsequently more hydroxyl radicals, which are the major cause of the polymer degradation. In more acidic pH, a higher level of dissolved iron will be maintained in the aqueous solution. Also, the oxidation potential of hydroxyl radicals is higher at more acidic conditions.[36]

The recirculation rate did not show any significant effect on the response functions, showing that this factor can be ignored in comparison to the other influential factors. In liquid phase reactions, the recirculation rate or the rate of mixing is usually considered as an important factor. The higher rate of mixing increases the rate of reaction due the increase of mass transfer rate. The insignificant effect of the rate of mixing in this study indicates that the rate of mixing in the selected range in a small photoreactor volume provides a good mixing condition in all studied levels.

As indicated in Table 3, the interaction model parameters (X1X2, X1X3, X1X4, X2X3, X2X4 and X3X4) did not show any significant effect on the percent TOC removal while the interaction effect of the initial concentration of polymer and the pH (X1X3) shows a significant effect on the ‘pseudo’-second order rate constant. The significant effect of X1X3 indicates that the interaction effect of the initial concentration of polymer and pH is beneficial to the percent TOC removal or the treatment time. The quadratic model parameters inline image (the quadratic effect of the pH) and inline image (the quadratic effect of the initial concentration of the polymer) show significant effects on the percent TOC removal and ‘pseudo’-second order rate constant.This finding shows that the percent TOC removal is more pronounced by the pH and the ‘pseudo’-second order rate constant or the treatment time is more affected by the initial concentration of the polymer.

3D Response Surface and 2D Contour Plots

The three-dimensional (3D) response surface and two-dimensional (2D) contour plots as the graphical representations of the regression analysis are presented in Figures 6 and 7 for the percent TOC removal and the ‘pseudo’-second order rate constant, respectively. In such plots, the response function of two factors are presented while all others are at fixed levels. The 3D response surface plots are formed based on the quadratic model Equations (12) and (13) while the relationship between the model factors and the response functions are illustrated by corresponding contour plots. The nonlinear nature of all 3D response surfaces in Figures 6 and 7 show considerable interactions between independent variables and their corresponding response functions. Moreover, the nonlinear contour plots in Figures 6 and 7 demontrate that there is no direct linear relationship among the selected independent variables. Figures 6 and 7 present the influence of the initial concentration of PAA (X1) and the initial concentration of H2O2:Fe3+ (X2) on the percent TOC removal and ‘pseudo’-second order rate constant in photo-Fenton-like process, respectively. As is evident in these figures, higher initial concentrations of PAA have negative effects on the response functions while higher H2O2:Fe3+ dosage results in higher TOC removal and higher rate constant. The high negative coefficient of X1 in the response functions(Equations (12) and (13)) also confirms the antagonistic effect of the initial concentrations of PAA on the response functions. Furthermore, as shown in these figures, the effect of the initial concentration of PAA is more influential than the other factor as it is in agreement with the significance of these two process parameters on the basis of P-values (Tables 3 and 4). However, as shown by the ANOVA results (Tables 3 and 4), the interaction between the initial concentrations of PAA and the oxidant ratio (H2O2:Fe3+) does not show a significant effect on the response functions.

image

Figure 6. Interaction effects of different parameters on the percent TOC removal using 3D response surface and 2D contours. (a) [PAA]o and [H2O2:Fe3+]o; (b) [PAA]o and pH; (c) [PAA]o and recirculation rate; (d) [H2O2:Fe3+]o and pH; (e) [H2O2:Fe3+]o and recirculation rate; (f) pH and recirculation rate. X1 = [PAA]o, X2 = [H2O2:Fe3+]o, X3 = pH and X4 =  recirculation rate.

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image

Figure 7. Interaction effects of different parameters on the ‘pseudo’-second order rate constant using 3D response surface and 2D contours. (a) [PAA]o and [H2O2:Fe3+]o; (b) [PAA]o and pH; (c) [PAA]o and recirculation rate; (d) [H2O2:Fe3+]o and pH; (e) [H2O2:Fe3+]o and recirculation rate; (f) pH and recirculation rate. X1 = [PAA]o, X2 = [H2O2:Fe3+]o, X3 = pH and X4 = recirculation rate.

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Figures 6b and 7b present the interaction effects between the initial concentration of the polymer and the pH. As is evident in Figure 6b, the higher percent of TOC removal at different initial concentrations of the polymer is achieved at lower pH (3–5). As illustrated in Figure 7b, the variation of the initial concentration of the polymer on the ‘pseudo’-second orderrate constant is more influential at lower pH. The P-value <0.05 in Table 4 also shows the significant effect of the interaction between the initial concentration of PAA and the pH on the ‘pseudo’-second order rate constant. At low concentration of PAA and low pH, a higher rate constant is achieved which could be confirmed by the high negative values of coefficients of X1 and X3 in Equation (13). The pH was selected in the acidic range since it has been proven that in the photo Fenton-like process, a high level of dissolved ferric iron species is achieved at pH buffered in acidic range.[44] Also, in the acidic condition, the prolonged reactivity of H2O2 is higher and the lifetime of H2O2 is decreased at buffer pH as it can be seen in Figures 6b and 7b.

As illustrated in Figures 6c and 7c and as confirmed previously in Tables 3 and 4, the recirculation rate does not show a significant effect on the percent TOC removal and the ‘pseudo’-second order rate constant. Also, the interaction effects of the initial concentration of the polymer and the recirculation rate on the response functions are insignificant.

Figure 6d illustrates the significant effects of the initial dosage of H2O2:Fe3+ and the pH on the percent TOC removal. As shown in this figure, the pH changes are more important than the other factor while in Figure 7d, both factors show small changes on the ‘pseudo’-second order rate constant (Figure 7d). Figure 6e also confirms the significant effect of the oxidants concentration (H2O2:Fe3+) on the percent TOC removal while Figure 7e shows that the changes of the oxidant concentration are less important in the ‘pseudo’-second order rate constant. Figure 6f also shows the significant effect of the pH on the percent TOC removal at different recirculation rates while as is evident in Figure 7f, the pH is less influencial factor on the ‘pseudo’-second order rate constant. Also, as this figure shows, at higher recirculation rates,decreasing pH has more positive effect on the ‘pseudo’-second order rate constant.

Optimisation of Process Parameters

The developed quadratic model for the percent TOC removal (Equation (12)) was used as an objective function to find the optimum operating conditions (the initial concentration of PAA, the initial dosage of H2O2:Fe3+ the pH and the recirculation rate) that maximise the percent TOC removal. Therefore, the percent TOC removal (Y1 in Equation (12)) was maximised at defined optimisation criteria for the factors (10 < X1 < 50; 4:400 < X2 < 1600:8; 3 < X3 < 7; and 1 < X4 < 10). Consequently, the optimum process parameters were found by using the numerical technique built in the Design Expert Software 8.0.5 based on the predicted model and the factors in their critical range as the constraints. The numerical optimisation searches the design space using the developed model in the analysis to find factor settings that meet the goal of maximising the percent TOC removal. The optimum values to achieve the maximum 95.7% TOC removal after 120 min were 10 mg L−1 PAA, 1600 m L−1 H2O2, 8 mg L−1 Fe3+, pH 3.42 and 10 L min−1 recirculation rate. The ‘pseudo’-second order rate constant at the optimal condition based on the developed quadratic model was found to be 82 M−1 s−1. The TOC removal efficiency was tested experimentally at the optimum operating conditions to validate the model prediction. 92.5% TOC removal and 86 M−1 s−1 ‘pseudo’-second order rate constant achieved experimentally at the optimal operating conditions confirm the results of the developed models.

CONCLUSIONS

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS AND DISCUSSION
  6. CONCLUSIONS
  7. NOMENCLATURE
  8. ACKNOWLEDGEMENTS
  9. REFERENCES

The photoassisted Fenton-like degradation of PAA as a model water-soluble compound was investigated using two photoreactors in series. The initial concentrations of the polymer, H2O2:Fe3+, the pH and the recirculation rate were considered as the process parameters. The percent TOC removal and the ‘pseudo’-second order rate constant were the response functions. The three-level Box–Behnken statistical experiment design applied in this study was found to be an appropriate response surface methodology to determine the effects of process parameters on the response functions in the studied range. Two quadratic models predicting the percent TOC removal and the ‘pseudo’-second order rate constant were developed. The statistical analysis using ANOVA indicated that the developed quadratic models were highly accurate and predictive. Also, the efficiency of the photo-Fenton like process was strongly affected by the initial concentration of the polymer and the pH. The oxidant ratio (H2O2:Fe3+) showed less significant effect on the response functions while the recirculation rate had no significant effect on the response functions. The optimal operating conditions to achieve the maximum percent of TOC removal (95.7%) in the selected range was determined to be 10 mg L−1 PAA, 1600 mg L−1 H2O2, 8 mg L−1 Fe3+, pH 3.42 and 10 L min−1 recirculation rate. The “pseudo”-second order rate constant at the optimum conditions was found to be 82 M−1 s−1. The obtained optimal operating conditions were also validated experimentally. The good agreement between the model predictions and the experimental results confirmed the accuracy of the developed models. Obtaining the significant operating conditions and their optimal values could be considered in the photoreactor design and modelling for the purpose of photoreactor scale-up.

NOMENCLATURE

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS AND DISCUSSION
  6. CONCLUSIONS
  7. NOMENCLATURE
  8. ACKNOWLEDGEMENTS
  9. REFERENCES
e

residual term

F-value

Fisher test value

k

number of factors

kobs

‘pseudo’-second order rate constant (M−1 s−1)

Mw

molecular weight (g mol−1)

P-value

probability value

R2

regression coefficient

inline image

adjusted regression coefficient

t

time (min)

Xi, Xj

independent variables

X1

initial concentration of PAA (mg L−1)

X2

initial concentration of H2O2:Fe3+ (mg L−1:mg L−1)

X3

pH

X4

recirculation rate (L min−1)

Y

response function

Y1

percent TOC removal (%)

Y2

‘pseudo’-second order rate constant (M−1 s−1)

Greek Symbols

βo

intercept term

βi

linear coefficient

βii

quadratic coefficient

βij

interaction coefficient

Acronyms

2D

two-dimensional

3D

three-dimensional

ANOVA

analysis of variance

AOT

advanced oxidation technology

df

degree of freedom

MS

mean square

PAA

polyacrylic acid

RSM

response surface methodology

SS

sum of square

TOC

total organic carbon

ACKNOWLEDGEMENTS

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS AND DISCUSSION
  6. CONCLUSIONS
  7. NOMENCLATURE
  8. ACKNOWLEDGEMENTS
  9. REFERENCES

The financial support of the Natural Sciences and Engineering Research Council of Canada (NSERC) and Ryerson University are gratefully appreciated.

REFERENCES

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS AND DISCUSSION
  6. CONCLUSIONS
  7. NOMENCLATURE
  8. ACKNOWLEDGEMENTS
  9. REFERENCES