Photocatalysis of Phenol Using a Spinning Disc ... - ACS Publications

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An investigation into the photocatalysis of phenol using a spinning disc photoreactor immobilized with TiO2 nanoparticles: hydrodynamic modeling and reactor optimization Mahboubeh Mirzaei, Bahram Dabir, Mitra Dadvar, and Morteza Jafarikojour Ind. Eng. Chem. Res., Just Accepted Manuscript • DOI: 10.1021/acs.iecr.6b03204 • Publication Date (Web): 23 Jan 2017 Downloaded from http://pubs.acs.org on January 28, 2017

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Industrial & Engineering Chemistry Research

An investigation into the photocatalysis of phenol using a spinning disc photoreactor immobilized with TiO2 nanoparticles: hydrodynamic modeling and reactor optimization Mahboubeh Mirzaei†, Bahram Dabir†,‡,§,*, Mitra Dadvar†, Morteza Jafarikojour† †

Chemical Engineering Department, Amirkabir University of Technology, Tehran, Iran

‡

Energy Research Center of Amirkabir University of Technology, Tehran, Iran

§

Faculty of Petroleum Engineering, Amirkabir University of Technology, Tehran, Iran

ABSTRACT: A spinning disc photoreactor using immobilized TiO2 on stainless steel disc, is employed to study the degradation of phenol in aqueous solutions. The characterizations of the immobilized TiO2 film are carried out by scanning electron microscopy (SEM), field emission scanning electron microscopy (FESEM), X-ray diffraction (XRD), BET and tensile adhesion test. The Box-Behnken design method is employed to identify the effects of three key operational parameters. The maximum predicted nano-photocatalytic degradation percent of phenol was 100% at the optimum processing condition (rotational speed 290rpm, flow rate 2000mL/min and disc diameter 22cm). For a description of the flow pattern, the residence time distribution (RTD) analysis is performed. By using tanks-in-series model the equivalent number of continuous stirred tanks reactors at obtained optimum condition was four. Hydrodynamic and kinetic models were combined to predict the degradation of phenol. A good agreement between the experimental and predicted results is shown.

1. Introduction Phenolic compounds are regarded as very harmful pollutants due to their toxicity and carcinogenic effects even at low concentrations. The maximum safe level for the concentration of phenol in effluents is 1mg/L which is proposed by EPA in USA 1. Accordingly, it is an important issue to find ways for eliminating this toxic material from industrial effluents. Recently, heterogeneous photocatalytic oxidation with TiO2 as a photocatalyst for complete decomposition of toxic organic compounds to nonhazardous species has drawn much attention among all traditional treatment methods2, 3. Relatively high speed for degradation of toxic organic compounds, low operation temperature, low cost and relatively low energy consumption are some of the advantages of photocatalytic oxidation techniques4-6. Photoreactors for water and wastewater treatment are broadly classified as slurry and immobilized catalyst systems. There are two essential issues related to reactors with immobilized photocatalyst; mass transfer resistance and photon transfer limitation7. There are various photoreactors reported in the literature which have been de-

veloped to eliminate these limitations8-11. Spinning disc reactor (SDR) is one of the mentioned reactors which is classified as immobilized photoreactors and therefore, separation of supported TiO2 from the system is easier and the immobilized photocatalyst can be reused12-15. These reactors consist of a horizontal disc that can be rotated by a motor. Liquid feed stream delivers to the center of the disc, travels rapidly across the surface, forms a very thin film irradiated with UV light and leaves the disc surface at its edge16. This thin (10-200µm) and rapid moving film provides enhanced heat and mass transfer characteristics17. The SDR was first reported as a photocatalytic reactor by Yatmaz et al. They indicated that the characteristics of the turbulent liquid films produced in the SDR reduce the influence of mass transfer over the overall photocatalytic process15. Boiarkina et al. investigated the effect of flow structure on the photocatalytic degradation of methylene blue and dehydroabietic acid in these types of reactors13, 18. Then, they compared the performance of the SDR to an annular reactor for methylene blue photocatalytic degradation19. Although, the performance of SDR for photocatalytic processes have evaluated under different conditions in previous, the optimization of these types of reactors has not been studied,yet.

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2. Experimental There are large number of complex and different parameters which may be interacted to each other for evaluation of efficiency of photoreactors. Therefore, using an efficient experimental design technique to develop of a model that can investigate not only the effect of an individual factor but also quantify their possible interaction is important. One of the techniques which can be employed for optimization and possible up-scaling of the photocatalytic process is response surface methodology (RSM) 20-22. Chemical reactors are often different from conventional ones. So, characterization of the residence time distributions (RTD) of materials inside the reactor is necessary for successful modeling, designing and scaling-up of the devices23. According to RTD data, simulation of the reality of the reactor behavior may be possible8. RTD of real reactors can be represented by simple flow models such as tanks-in-series model. The main necessity for the estimation of intrinsic kinetic coefficients independent from the configuration of reactor for performance evaluation of it, is using the reactor which operates under conditions that photon and mass transfer limitations are not considerable24. There are some studies which consider the hydrodynamic behavior of SDR as a plug flow reactor in their kinetics studies13, 18, 19. Consideration of the reactor hydrodynamic model in kinetic study is necessary for determination of intrinsic kinetic parameters independent from their configuration8, 25. In this study, a spinning disc photoreactor is designed, constructed and modeled and the reactor capability for degradation of phenol as model compound in the liquid phase is determined. Characteristics of the synthesized TiO2 films are investigated by X-ray diffraction (XRD), scanning electron microscopy (SEM), field emission scanning electron microscopy (FESEM), BET and tensile adhesion test. Box-Behnken design (BBD) as one of the subset of RSM, is employed for modeling, optimization and study of the effect of different variables such as rotational speed, circulating flow rate and disc diameter. The results of this part could be valuable as it was not performed yet. Next, to determine the photoreactor hydrodynamic behavior in obtained optimum conditions, residence time distribution (RTD) of the fluid inside the reactor is developed. A model for the reactor is proposed, applying the tanks-in-series flow model. Finally, the intrinsic kinetic coefficients are calculated by minimizing the sum of the square errors (SSE) for two set of data i.e. experimental and predicted results from the proposed model for various concentration of phenol. In fact, the major novelty of this study is obtaining an optimal condition for nanophotocatalytic degradation of phenol in spinning disc reactor and proposing of a developmental model in this condition which considers mass transfer and hydraulics in the reactor with the kinetics. This model can be used for many reactors with different configurations to design and scale-up the immobilized photoreacrors.

2.1. Materials Materials used in this work for preparation of the TiO2 powder via modified sol-gel method are isopropanol (iPrOH, 99.8%, Merck Co.), commercial ultrapure titanium isopropoxide (TTIP, 99%, Samchun pure chemical Co.), diethanolamine (DEA,99%, Merck Co.) and titanium dioxide (Degussa P25 powder, Evonik Degussa). Phenol with above 99.5% purity purchased from Merck Co. is used as the organic contaminant in the wastewater. To determine the phenol degradation Folin-Ciocalteu reagent and sodium carbonate with above 99.9% purity are used and obtained from Merck Co. To investigate light intensity which was used in photocatalytic experiments, potassium iodide (KI, 99.5%), potassium iodate (KIO3, >99%) and di-sodium tetraborate decahydrate (Na2B4O7.10H2O, extra pure) are prepared from Merck Co. For preparing all solutions, deionized water was used. 2.2. Reactor design and experimental setup A schematic diagram of the experimental setup used in the present work is shown in Figure 1. The spinning disc reactor (SDR) consisted of a cylindrical-conical vessel made of pyrex glass (30cm ID and 35cm in length) covered with aluminum foil to prevent photolysis of phenol and minimize the energy lost. A stainless steel disc coated with TiO2 nanoparticles is mounted horizontally on an axle and driven by an electromotor. A nozzle is positioned above the center of the rotating disc, without any distance from it to deliver wastewater. The light source consisted of a strip composed by 8 UV lamps (5W, Philips) emitting UV-C radiation at 253.7nm. This strip is located above the disc so that light source irradiated the reaction surface uniformly. The average light intensity reaching the TiO2 coated surface of the spinning disc was calculated to be 1.78mW/cm2. This value was determined using an actinometric method (described elsewhere26) and based on the light source being positioned at a distance of 10cm away from the disc surface. 600mL of phenol solution as a model pollutant is kept in the feed reservoir. To start the process a diaphragm pump is turned on and the phenol solution (C0 = 30ppm, pH = 6.5) fed to the center of coated disc, irradiated with UV light and after passing through the reaction area, is recycled to the feed reservoir. The circulation of synthesized wastewater, before switching on the lamps, is being carried out for 30 min in darkness in order to establish adsorption-desorption equilibrium. The solution in the reservoir is aerated at a flow rate of 800mL/min with an air compressor. The photoreactor temperature is monitored continuously and kept constant at 30-31℃ using a cooled water bath. The reactor is operated for 4hours and at every half hour intervals, samples are taken from the reactor and analyzed. Fig. 1. Phenol concentration is determined by a UV-visible spectrophotometer (DR-2800 Hach Co.) at a wavelength of 765nm using the Folin-Ciocalteau method as per


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standard procedure27. Phenol degradation ratio (%) is calculated from the subsequent equation:

=

− (1)

Where C0 and C are the concentration of phenol in samples at UV illumination time 0 and t, respectively. 2.3. Preparation of immobilized TiO2 TiO2 nanoparticles are synthesized by a modified solgel method which has been described previously 28. First, a 0.5M solution of TTIP in i-PrOH is prepared. Then, DEA as a stabilizing agent with a DEA/TTIP molar ratio of 4 is added to the solution and stirred at room temperature. After 2hours of stirring, distilled water with a molar ratio of H2O/TTIP of 2 is added slowly to the solution drop by drop. Subsequently, for improving the photocatalytic activity, the TiO2 p25 nanopowder (100g/l) is added slowly to the slurry under vigorous stirring to avoid the formation of large titania agglomerates in it. The TiO2 sol is stirred continuously for more than 12h and then sonicated for 15min to better disperse the P25 in it and hence obtained a more uniform slurry. Before coating process, stainless steel disc is washed using ethanol and distilled water. The prepared and cleaned disc is coated using a certain amount of prepared sol to yield a TiO2 loading of 5.5g.m-2 by an in-house spin coater at 1700rpm for 1min. This loading is controlled by changing volume of the sol is used for coating process (12.2mL, 11.1mL and 10mL for disc diameter of 22,20 and 18, respectively). The coated disc is air dried for 24h, placed into a furnace, heat-treated at 100℃ by a heating rate of 3℃/min and held at this temperature for 1h. Then, for obtaining better adhesiveness by setting temperature increasing rate of 3℃/min, the final temperature of furnace reaches 500℃ and is kept in this condition for 1h. Finally, the coated disc is cooled to room temperature. 2.4. RTD measurement To understand the flow pattern of liquid in this studied photoreactor, the RTD curve is characterized from tracer stimulus-response method. After reaching steady state for the parameters which were set on the desired values, a tracer (5mL of 1000mg/L of methyl orange) at time t=0 is loaded to the inlet of the reactor within a very short time to simulate a pulse function. In measurements, the theoretical time between the injection point and the point that tracer would reach the spinning disc is subtracted to determine the RTD curves and data. The tracer output concentration at regular time intervals only in one cycle is measured (Figure 2), applying a UV-visible spectrophotometer. By using these data and following equation the RTD is obtained 29, 30:

E(t) =

C(t)

C(t)dt

=

(2) ∑ ∆

Fig. 2. 2.5. Characterization of TiO2 films The crystalline phase composition (anatase/rutile) of TiO2 films is examined by X-ray Diffraction (XRD) technique using glancing-angle thin film X-ray diffractometry (XRD, microanalyzer XMF-104, Germany). The average crystallite size of calcined TiO2 is calculated from Scherrer's formula31:

=

(3)

In above relation, L is the crystallite size of the TiO2, is a constant (= 0.89), is the X-ray wavelength (Cu Kα = 1.54065Ả) and is the line width at the half of the maximum peak. For determination of morphological characterization of TiO2 films, Scanning electron microscopy (SEM, AIS-2100, seron, Korea) and field emission scanning electron microscopy (FESEM, Hitachi S4160, Japan) images are taken. The film thickness is revealed from the cross section SEM image of the sample. The specific surface area and coating quality of TiO2 films are evaluated by the BET analysis (NOVA, series1000-Quantachrome INSTRUMENTS) and tensile adhesion test (Positest AT, Defelsko, USA). 2.6. Experimental design and response surface methodology Photocatalytic water treatment using SDR is affected by various parameters including initial concentration, speed rotational, air flow rate, catalyst loading, disc diameter, flow rate, pH, liquid volume and etc. Among all of these parameters, three important factors including rotational speed, flow rate and catalyst surface were selected due to their effects on hydrodynamic characteristics of the SDR. In order to determine the ranges of chosen parameters for experimental design, the preliminary experiments are carried out (Figure 3). In these pre-tests ranges of flow rate and rotational speed are 300-1500mL/min and 50250rpm, respectively. It can be seen that effects of these parameters were not significant in studied range (Figure 3a,b). So, a wider range is considered for final experimental design. The ranges of selected variables which are denoted as A, B and C and coded values are listed in Table 1. Then, for determining the optimal conditions of the experimental considerable parameters, interaction between them and assess an equation between response (phenol degradation ratio) and independent parameters, BBD model is employed. BBD model, based on RSM is chosen because it is efficient and needs a minimum number of experiment runs. total number of experiments for BBD is defined as32:

= ! " + ! + $ (4)



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Where k is the number of factors (k=3) and c is the replicate number at the center point. So, 15 runs are formulated. For description of correlation between predicted response (phenol degradation ratio) and the operational factors a quadratic model is required33:

Degradation ratio = b + b' A + b" B + b* C +

b'" AB + b'* AC + b"* BC + b'' A" + b"" B " +

b** C" (5)

Where , ,, and , are model constant, linear coefficient and quadratic coefficient, respectively. ,- is the interaction coefficient for the fitted quadratic model. For the evaluation of the quality of fit to the quadratic model, the R2 coefficient is calculated. Based on ANOVA results, statistical analysis of experimental data is performed. Fig. 3. Table 1 3. Results and discussion 3.1. Film characterization Figure 4 demonstrates XRD patterns of immobilized TiO2 films on stainless steel before being employed in photocatalytic experiments. In this pattern, the main peaks at two-theta (2θ) of 25.2 and 27.3 are attributed to anatase and rutile crystalline phases, respectively. According to Scherrer's equation, the average crystallite size of TiO2 particles is calculated and is 39nm. Fig. 4. The surface morphology of TiO2 coating films before being used in photocatalytic degradation of the phenol can be observed in Figure 5 which shows SEM and FESEM images of the sample. In order to obtain high-resolution images and describe the morphology of a single particle, the FE-SEM technique is used. Figure 5a demonstrates the cross-section SEM image of the coating sample. It shows that the coating is a dense structure with the thickness of about 3µm. Figure 5b shows that the surface has been completely coated with TiO2 films. In Figure 5c and 5d, it is obvious that the supported TiO2 films are full of agglomerates with many grains while the formation of microcracks in the films is visible in the FESEM images (darker areas). These microcracks create new areas at the photocatalyst surface and hence increase active sites and photocatalytic activity14, 34. Fig. 5. To evaluate the specific surface area for the prepared catalyst, BET test was used. This characteristic plays a significant role in the photocatalytic activity and other studies show that there is a positive relationship between the catalyst surface area and photocatalytic reaction rate 14, 28 . The BET specific surface area of the scratched TiO2 coatings before photocatalytic degradation experiment was 114m2/g.

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Since adhesion is one of the most important properties of the immobilized films, the tensile adhesion test was used. Measurements showed that the coated films before and after the photocatalytic experiment (under optimum condition) have an actual adhesion of 3.2 and 2.74Mpa, respectively. These values are essentially high considering the thickness of the coatings35. A decrease in this property may be related to centrifugal force created by the spinning action which weakens the interface adhesion. 3.2. RTD Modeling To perform the residence time distribution (RTD) experiment, the tracer concentration is determined by setting the interval time between two measurements on 0.62 second. RTD curve which quantifies how much time different particles have spent in the reactor, is plotted according to Equation (2)36. The parameters of the curve, such as the mean residence time (./ ), the variance of the residence time (0 " ) and the dimensionless variance (01" ) are calculated as follows20:

./ =

∑ ∆ ∑ ∆

∑ " ∆

(6)

0"

=

01"

0" = " (8) ./

∑ ∆

" − ./ (7)

Mean residence time (./ ) is the average time for each molecule to remain under selected processing condition. The variance (0 " ) is the measure of the dispersion of the values of the random variables about the mean. The RTD dimensionless variance (01" ) shows a measure of dispersion. Lower value for this parameter demonstrates the flow approaches to perfectly plug flow and with the increasing of it, the flow approaches to near mixed flow.20, 37 From above equations, ./ and 01" for optimum conditions of the reactor were obtained as 5.28s and 0.25, respectively. There are studies that use tanks-in-series model to simulate the behavior of SDR for indicating if a flow is close to plug flow or not36, 38. In this model, the photoreactor actual volume is replaced by a series of consecutive and equal sized of ideally stirred tank reactors (Figure 6). Degree of mixing can be understood from the number of tanks (N) which is calculated from Equation (9).

=

1 (9) 01"

Fig. 6. This relation demonstrates that the number of series tanks to model the reactor is reversely related to the dimensionless variance which is obtained from RTD curve. N should be as high as possible to reach an ideal plug


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flow. As a general rule, above the 50 mixed reactors the RTD becomes Gaussian and symmetric and the flow regime could be considered as plug flow 36, 38. The number of series tanks which is required for modeling of RTD under optimum values of variables for complete photodegradation of phenol, is 4. The age exit distribution (E) for presented model is determined theoretically by using Martin Method 8, 36, 38:

(78') 1 6() = 7 exp (− 7 ) (10) ./9 ( − 1)! ./9

Figure 7 indicates the comparison between experimental data and the predicted RTD based on tanks- inseries model. The RTD is one of the key characteristics that determines the flow and mixing behavior of reaction components and hence defines the yield and selectivity of the chemical process. As it can be seen from the Figure, the RTD data revealed that CSTRs in a series model with a cascade of four ideal tanks is well correlated with the reactor behavior. Fig. 7. 3.3. Model fitting and analysis of variance (ANOVA) A total number of 15 experiments in Box-Behnken design were performed and results of the experimental matrix are presented in Table 2. In each test, the phenol degradation ratio is chosen as output response variable. To evaluate the model, analysis of variance (ANOVA) of regression parameters of the predicted response surface for phenol degradation was applied and statistical results are presented in Table 3. Based on the ANOVA Table, the polynomial second-order quadratic model for representing an empirical relation between phenol degradation % and significant parameters is regressed:

Degradation percentage= 83 ∙ 83 + 3 ∙ 65A + 3 ∙ 16B + 7 ∙ 04C – 1 ∙ 58AB + 1 ∙ 46AC + 1 ∙ 81BC + 0 ∙ 12A" + 0 ∙ 27B" + 3 ∙

36C" (11) Table 2

The F value is the ratio between the mean square due to regression and the mean square due to error term. The "Model F-value" of 33.79 and the value of "Prob > F" show that the obtained model for degradation of phenol is significant. According to terms which have p-values less than 0.0500 are significant model terms and other terms with a p-value greater than 0.1000 are not significant, in fitted model A, B, C, AB, AC, BC and C2 were significant model terms5, 39. The polynomial equation shows that all

three parameters have positive coefficients. The positive sign for these parameters shows that all of them positively affect the phenol degradation. As it can be seen in ANOVA results, the significance of selected parameters is (from the most to the least significant): disc diameter > rotational speed > flow rate. This model has been selected because of the subsequent reasons: The adequate precision value that measures signal to noise ratio was 19.707 (adequate precision > 4), which indicates an adequate signal for obtained model. The value of lack of fit is not significant, indicating that the obtained model has good predictability for degradation of phenol under any combination of values of parameters. A significant lack of fit indicates that the model fails to show the data in the experimental domain at which points are not included in the regression. Higher values for correlation coefficients " A" (0.9838) and ABC(0.9543) show the established model is adjusted well with experimental data. In other words, the model is able to explain 98.38%, representing just 1.62% of total dissimilarity might not be depicted by the quadratic model. Subsequent confirmation for adequacy of the regression model is uniformly distribution of the predicted value and the experimental results around a 45° line (Figure 8a). The coefficient of variation (CV) is an indicative of accuracy of experimental procedure and in general when the CV of a model is not greater than 10%, the model has dependability and reproducibility 33. In this study, CV value of 1.71% shows that a high reliability of experiments have been performed. Table 3 3.4. Interactive effects of process variables To have a complete understanding of the effect of each parameter, i.e. rotational speed (A), flow rate (B) and disc diameter (C) and their interactions, based on regression equation two-dimensional and three-dimensional (3D) response surface plots are generated by the Design Expert 7.0.0 software as graphically represented40. Figure 8b shows the individual effect of rotational speed on phenol removal. As it can be seen, by increasing the rotational speed from 10 to 290rpm, the degradation ratio increases from 0.80 to 0.88. For explanation of this trend, it should be noted that by increasing rotational speed the centrifugal force increase (where DEFGHIJKBL ∝ ω2r). The centrifugal force of the spinning disc forces the liquid that impinged onto the center of disc surface to form a thin and highly sheared film. This highly sheared film possesses high degree of turbulence. The turbulence plays an important role in increasing the mixing and mass transfer characteristics and leads to promotion of the reaction rate36, 38. On the other hand, formation of thin film thickness leads to ultraviolet (UV) penetration throughout the liquid film becomes higher and hence value of phenol degradation increases13. According to Equation (12), which shows the Nusselt model for prediction of film thickness13, by increasing rotational speed from 10rpm to 290rpm, its value decreases from 1102µm to 116.7µm.



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'V (12) * 3PQ ℎ=O U 2RS " T " Where ℎ is the film thickness,P is the circulating flow rate, Q is the kinematic viscosity, S and T are radius and

speed of disc, respectively.

Third potential reason for this observation is faster return of unreacted molecules to reaction zone. Although at fixed flow rate the number of reaction cycle is fixed, according to RTD curves and mean residence time values for different disc speeds at fixed optimum values of two other parameters (Figure 9a), increasing the rotational speed decreases the residence time. It indicates that the molecules will come back into the feed reservoir quicker and will be under the conditions of reactor entrance so they will return to the reaction zone sooner. On the other hand, reduction of residence time in reaction zone leads to less degradation of phenol. Therefore, there are three phenomena which positively affect phenol degradation (increasing mixing, decrease in film thickness, faster return of molecules) and a phenomenon which affect degradation negatively (reduction of residence time). Figure 8c demonstrates the individual effect of the flow rate at a fixed rotational speed and disc diameter. The Figure shows that increasing flow rate from 500 to 2500mL/min leads to an increase in degradation ratio from 0.81 to 0.87. Increasing the flow rate leads to more intense ripples on the surface of the film which can induce turbulence in the layers of the film and thus enhances mass transfer coefficient and phenol degradation36. Another possible explanation for this observation is an increase in the reaction cycle number and faster return of unreacted molecules13. By increasing flow rate from 500 to 2500mL/min, the number of cycles increases from 200 to 1000 with respect to the solution volume and RTD results (Figure 9b). Increasing flow rate from 500 to 2500mL/min increases the thickness of film from 125.6µm to 214.8µm, employing Equation (12). According to molar absorption coefficient of phenol solution (ε=561 M-1.cm1 41 ) , its concentration in water (30 mg.L-1), mentioned film thicknesses and using Beer's law, the amounts of light intensity which passes through the film and reaches the catalyst surface, are close to each other for different values of film thicknesses induced by different flow rates. Therefore, decrease of UV penetration due to the increasing in thickness of film is negligible against two other phenomena. Figure 8d illustrates the effect of disc diameter on phenol conversion. The Figure indicates that increasing in this parameter from 18 to 22cm enhances the desirable response from 0.80 to 0.94. This variable is the most significant parameter among the examined parameters. It is obvious that by increasing disc diameter, catalyst surface and active sites for the production of electron-hole and hence ●OH radicals increase. Therefore, it can be concluded that photocatalytic degradation ratio of phenol is increased. Enhancing UV diffusion induced by decreasing the thickness of film (from 194µm to 170µm) is another

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reason for description of disc diameter effect. The RTD profiles and mean residence time to investigate the effect of disc diameter on residence time are presented in Figure 9c. Increasing disc diameter slightly enhances residence time in reaction zone (approximately 0.1s for each cm). Therefore, there are three reasons for disc diameter effect on degradation of phenol. Fig. 8. Fig. 9. The second-order response surface plots for phenol degradation as a function of significant interaction between examined parameters are shown in Figure 10. In each plot, one parameter is kept at zero level, while other two parameters changed in experimental ranges. The effects of rotational speed and flow rate on phenol degradation ratio are illustrated in Figure 10a. The significance of this interaction term indicates competition between these two parameters. It is clear that degradation ratio of phenol increases with both rotational speed and flow rate increasing. Figure 10b and 10c show the interaction of rotational speed and flow rate with disc diameter, respectively. According to the previous discussion, disc diameter plays a key role in this photocatalytic study. As shown in these Figures, at higher disc diameter (22cm), higher degradation ratio (>0.88) were obtained at different flow rates and rotational speeds. Fig. 10. 3.5. Optimization and validation of results To achieve the optimum conditions for obtained model, the optimization tool of Design Expert was used. The goal of optimization (phenol degradation ratio) is defined as maximum while the process parameters were selected "within the range". Based on RSM analysis, complete phenol removal could be obtained under different conditions. One of these predicted optimum conditions of parameters was as follows: 290rpm A, 2000mL/min B, and 22cm C. The desirability function value becomes to be one. However, to perform a comprehensive optimization, either economic perspectives of a process or the maximizing its conversion should be considered. In this work, only the optimization of the degradation ratio has done for a lab-scale reactor. In order to check the accuracy of this prediction, repeating experiments were carried out under such optimized conditions. It is found that the experiment and model results are in excellent agreement without any error which confirms the validity of the model for simulating the photocatalytic degradation of phenol in this study. 3.6. Degradation kinetics model The Kinetic tests with an initial concentration in the range of 30-70mg/L and setting of rotational speed, flow rate and disc diameter on their optimum conditions based on Design Expert results were carried out. Generally, the photocatalytic degradation rate of aquatic organics


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can be expressed formulation42:

S=

by

Langmuir-Hinshelwood

!H W (13) 1 + W

Where !H is the true rate coefficient (mg /min/ L), is the adsorption equilibrium constant (L/ mg) and W is contaminant concentration in the liquid phase (mg/L).

Evaluation of phenol concentration and degradation from the spinning disc photoreactor, replacing with N= 4 CSTRs, is described by the set of N mass balance expressions29:

W7 = W78' −

./ !H W7 X Y (14) 1 + W7

W.G = W[.9 , W\.G = W[.] (15)

Where, W78' and W7 demonstrate the inlet and outlet contaminant concentrations (i.e. phenol) from the Nth reactor, respectively. Subsequent relation describes a balance around the feed reservoir which can be seen in Figure 6:

PW[.] − PW[.9 =

^_`Wa bc ^ (16)

Where P is volumetric flow rate, ` is the solution volume in feed reservoir, W[.9 and W[.] are the inlet and outlet phenol concentrations from the reactor, respectively, and Ea is concentration of phenol reservoir. The finite difference form of latter Equation can be expressed by this formulation:

Wa d' − Wa P _W[.] − W[.9 b = ` (17) ./

In above relation,./ is mean residence time and e = V . ./ By combination of Equation (15) with Equation (17)8, 23:

−

Wd' = a

P P ./ W. + W. O1 − . U (19) a ` ` /

Wd' a W. a

W. b

Wa d' − Wa =` (18) ./

P _W\.

P W. P = ./ . + O1 − ./ U (20) ` ` Wa

/fC = 1 −

Wd' a (21) W. a

For identification of the kinetic coefficients, the predicted degradation ratios which are calculated from

Equation (20), were compared with experimental values. The parameters are optimized by using the least square method as below: ijk

gg6 = h 6SS" =

1

eFlm

ijk

"

h_Flm − /fC b (22)

Table 4 shows the experimental and predicted conversions and estimated rate coefficients. According to this table, SSEs for experimental and predicted values from tanks-in-series model approach. This means that there is a good adjustment between the model prediction and experimental results. As it can be seen, the adsorption coefficients are different for different concentrations of phenol. It can be due to the effect of concentration on adsorption. At higher phenol concentrations, more and more molecules adsorb on the TiO2 surface. This leads to more competition between phenol molecules and different reaction intermediates for adsorption on the catalyst active sites43, 44. Briefly, increasing the initial concentration leads to increase in the amount of adsorbed molecules that decreases the direct contact between them and active sites and hence causes a change in adsorption equilibrium and its relative coefficient. Table 4 4. Conclusion In this study, a spinning disc photoreactor for degradation of phenol is designed and constructed. Process optimization is performed by focusing on the effect of operating parameters (i.e. rotational speed, flow rate and disc diameter) and the interaction between each parameter is examined using BBD in RSM package. The ANOVA results show that by increasing all three major parameters, photocatalytic decomposition of phenol increase and in the examined range, disc diameter has the highest significance. Complete degradation of phenol is obtained within 4hours reaction time under optimum conditions in the reactor. RTD of spinning disc photoreactor under optimum conditions is obtained experimentally by impulse tracer method. On the basis of RTD data, the estimated number of tanks-in-series as four is fitted well with the experimental data. The kinetic coefficients were predicted by a combination of tanks-in-series model and phenol photocatalytic decomposition rate equation.

AUTHOR INFORMATION Corresponding Author * E-mail address: [email protected] (B. Dabir). Department of Chemical Engineering, Amirkabir University of Technology, Tehran 15914, Iran.

Notes The authors declare no competing financial interest.

Acknowledgments



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

The authors would like to Evonik GmbH for kindly supplying Nano-sized TiO2.

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Figure 9. Effect of (a) rotational speed (b) flow rate (c) disc diameter on RTD Figure 10. Surface mutual interaction between the significant factors (a) conversion vs. rotational speed and flow rate (b) conversion vs. rotational speed and disc diameter (c) conversion vs. flowrate and disc diameter.

Tables Title Table 1. Experimental independent variables

ranges

and

levels

of

Table 2. Design matrix of experiments Table 3. model

ANOVA for response surface quadratic

Table 4. Intrinsic kinetic coefficients degradation ratio for tanks in series model

Figures Caption Figure 1. Schematic diagram of the photoreactor: (1) feed reservoir, (2) pump, (3) valve, (4) flow meter, (5) strip of UV lamps, (6) spinning disc, (7) temperature sensor, (8) compressor, (9) power supplier, (10) circulating water bath. Figure 2. Schematic diagram of the photoreactor in the case of RTD study: (1) reservoir, (2) pump, (3) valve, (4) flowmeter, (5) dye injection port, (6) strip of UV lamps, (7) spinning disc, (8) rotating disc for sampling, (9) power supplier for spinning disc, (10) power supplier for sample collector disc. Figure 3. Effect of (a) rotational speed, (b) flow rate and (c) disc diameter on phenol degradation in pre-tests. Figure 4. X-ray diffraction spectra of TiO2 coating; A: anatase, R: rutile, SS: stainless steel Figure 5. (a) cross section SEM image of TiO2 coatings on stainless steel, (b,c,d) FESEM images of TiO2 coating on stainless steel with different magnification. Figure 6. Cascade of CSTRs in series model for the reaction zone. Figure 7. Comparison between the residence time distribution E(t) of the spinning disc photoreactor and that predicted from tank in a series model with N=4. Figure 8. (a) predicted versus actual for degradation of phenol, (b,c,d) effect of individual factors on the degradation of phenol.


and


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Table 1. Experimental independent variables Factor

Unit

ranges

Symbol

and

levels

Page 10 of 24

of

Coded levels (-1)

(0)

(+1)

Rotational speed

rpm

A

10

150

290

Flow Rate

ml/min

B

500

1500

2500

Disc diameter

cm

C

18

20

22


10

Page 11 of 24

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Industrial & Engineering Chemistry Research Table 2. Design matrix of experiments Run

A

B

C

Phenol degradation ratio after 4h

Rotational speed (rpm)

Flow rate -1 (mL.min )

Disc diameter (cm)

1

10

1500

22

0.91

2

150

1500

20

0.84

3

150

500

18

0.80

4

10

1500

18

0.78

5

150

500

22

0.88

6

290

1500

22

1

7

290

1500

18

0.81

8

10

2500

20

0.85

9

150

1500

20

0.84

10

150

2500

18

0.83

11

290

2500

20

0.90

12

290

500

20

0.87

13

10

500

20

0.76

14

150

2500

22

0.99

15

150

1500

20

0.83


11


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Page 12 of 24

Table 3. ANOVA for response surface quadratic model source

Sum of squares

Degree of freedom

Mean squares

F-value

P-value

Model

656.31

9

72.92

33.79

0.0006

A-Rotational speed

106.73

1

106.73

49.45

0.0009

B-Flow rate

79.76

1

79.76

36.96

0.0017

C-Disc diameter

396.49

1

396.49

183.73

Photocatalysis of Phenol Using a Spinning Disc ... - ACS Publications

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