Article pubs.acs.org/IECR
Comprehensive Study on Wastewater Treatment Using Photo-Impinging Streams Reactor: Residence Time Distribution and Reactor Modeling Sayed Javid Royaee*,† and Morteza Sohrabi*,‡,§ †
Refining Technology Development Division, Research Institute of Petroleum Industry, Tehran, Iran Chemical Engineering Department, Amirkabir University of Technology, Tehran, Iran § Iran Academy of Sciences, Tehran, Iran ‡
ABSTRACT: A photo impinging streams reactor was employed to study the degradation of phenol in aqueous solutions applying titanium dioxide nanoparticles as the reaction catalyst. The central composite experimental design method was applied to determine the pertinent operating parameters of such a process. These were phenol concentration, catalyst loading, pH of the slurry, and the feed flow rate. Residence time distribution (RTD) of the slurry phase within the reactor was measured using the impulse tracer method. A compartment model consisting of a number of mixed and plug flow regions was assigned to describe the flow pattern in the reactor. On the basis of such an arrangement and applying the Markov chains discrete time formulation, a three parameters model was derived for the RTD. The parameters of the theoretical RTD model were evaluated by comparing the latter with those determined experimentally. The RTD expression was applied in conjunction with the phenol degradation kinetic model to predict the apparent rate coefficient for such a reaction. The higher values observed for the apparent rate coefficient in impinging streams reactor than those available in the literature may be explained by the mass transfer limitations affecting the conventional reactors performance.
1. INTRODUCTION The textile industry needs large quantities of water of good quality in its fundamental processing steps, which generally imply the utilization of a large number of chemicals including phenolic components, organic dyestuffs, surfactants, chelating agents, pH regulators. These processes generate extremely toxic wastewater, the treatment of which is often difficult due to the presence of some nonbiodegradable species with complex structures. Therefore, development of some alternative and more cost-effective treatment methods is of primary concern. In recent years, a number of experimental investigations have been conducted on degradation of pollutants using photocatalytic processes by which a complete destruction of major classes of organic pollutants at ambient conditions is achieved.1−9 However, some of the crucial issues in photocatalytic processes are mass and photon transfer limitations.9 To eliminate photon transfer limitations, certain apparatus such as various photoreactors, including spinning disk reactors, monolith reactors, fluidized bed reactors, bubble column reactors, rotating disk reactors, and microreactors have been successfully applied.5,9,10 As it was stated previously, mass transfer resistances are also a critical issue which must be carefully considered and investigated within this reaction. An ideally intensified reactor, however, should be able to integrate both maximized light efficiency and minimized mass transfer resistance within a single piece of equipment. Photo-impinging stream reactors (PISR), which utilize a unique flow behavior to intensify transfer processes in photocatalytic degradation, were first used by Royaee and Sohrabi.11,12 The key phenomenon in these systems is the penetration of the particles into the opposite streams, creating a longer mean residence time and higher relative velocity. Consequently, the particles’ flow pattern, mixing intensity, photon adsorption, and transport characteristics may be significantly improved within such reactors. © 2012 American Chemical Society
Owing to the large deviation of the PISR behavior from the ideal, determination of the residence time distribution (RTD) of the particles in the reactor is a key information required for the design and modeling of the latter devices. On the basis of the RTD data, it may be possible to simulate the nonideality of the particle behavior by a configuration of ideal systems, including ideal perfect mixed tanks and/or ideal plug flow reactors. Modeling of the random behavior of the particles in the impingement streams by Markov chains discrete formulation is a typical mathematical technique that has been applied by previous investigators.13−16 In the present study, the central composite experimental design (CCD) was used to identify optimum levels of the significant variables for enhancement of phenol degradation using a photo-impinging streams reactor. I9n the next step, a model for the residence time distribution of the particles within the reactor was developed. Finally, the photoreactor performance capability for photocatalytic degradation of phenol was determined, applying the RTD information and the rate expression for such a reaction.
2. EXPERIMENTAL SECTION 2.1. Materials. Titanium dioxide nanoparticle (P25) was supplied by Degussa, Germany. TiO2 P25 consists of anatase 80% and rutile 20% with the mean particle size of 20 nm and a BET surface area of 50 m2/g. Phenol with a purity of above 99.5%, laboratory grade phenol, sodium hydroxide, and sulfuric acid were all obtained from Merck Co. C.I. Reactive Black Received: Revised: Accepted: Published: 4152
June 28, 2011 January 28, 2012 February 24, 2012 March 8, 2012 dx.doi.org/10.1021/ie201384s | Ind. Eng. Chem. Res. 2012, 51, 4152−4160
Industrial & Engineering Chemistry Research
Article
Figure 1. Schematic diagram of the photoreactor (a) in the case of phenol degradation: (1) reaction vessel, (2) impingement zone, (3) UV lamps, (4) feed nozzle, (5) feed reservoir, (6) gear pump, (7) magnetic mixer, (8) cooling system, (9) air pump, (10) pH sensor. (b) In the case of RTD study: (11) dye injection port, (12) sampling point, (13) rotating disk for sampling.
meter. The temperature of the photo reactor was maintained unchanged, using a water jacket constructed around the reservoir. The suspension was kept uniform in composition by agitation with a magnetic stirrer. 2.4. Experimental Design Methods. To determine the optimum conditions for degradation of pollutants, the crucial operating factors were screened and the central composite design (CCD) was applied in order to establish the optimal levels of the significant factors and the interactions of such variables on the process. In this study, a four-factor, five-level CCD with 30 runs was employed. Tested variables (phenol concentration, catalyst loading, pH, and flow rate) were denoted as X1, X2, X3, and X4, respectively. Each of these variables was assessed at five different levels, combining factorial points (−1, +1), axial points (−2, +2), and central point (0), as shown in Table 1.
8 was supplied by Youhao Co. (China) and used for RTD determination. 2.2. Analytical Procedure. The concentration of phenol was measured by a UV−visible spectrophotometer (Dr 2800 Hach Co.) at the wavelength of 495 nm using the 4aminoantipyrine method.17 Owing to the presence of TiO2 powders in the system, prior to the measurement, the samples were filtered using a 0.22 μm syringe filter (Millipore) to separate particles.18 The concentration of C.I. Reactive Black 8 was also measured by the latter UV−visible spectrophotometer at the wavelength of 578 nm. 2.3. Photoreactor. A schematic diagram of the photoimpinging streams reactor used in this study is shown in Figure 1a. The apparatus consisted of a cylindrical vessel made of quartz equipped with 16 low-pressure mercury vapor lamps, with a dominant emission line at 253.7 nm (TUV 8W from Philips Co.), used as the irradiation sources. Two pressure nozzles were mounted on the same axis in front of each other spraying two jets of slurry. A stream of air with a specified flow rate was continuously supplied to the slurry solution. To prevent hole/electron formation, prior to turning on the illumination, the catalyst was placed in the feed reservoir at dark. The phenol solution was then added and the suspension was saturated with air and stirred with a magnetic stirrer in darkness for 30 min to establish the adsorption−desorption equilibrium. The gear pump was then switched on and the suspension, after passing thorough the two nozzles, was irradiated with UV light and collided in the impingement zone. Samples were regularly withdrawn from the reactor and filtered to remove all the suspended solid particles prior to analysis. The pH of the solution was adjusted by adding 0.1 mol L−1 NaOH or H2SO4 and monitored with a digital pH
Table 1. Levels of the Variables in the CCD coded levels variable
−2
−1
0
+1
+2
X1
25
50
75
100
125
X2 X3 X4
0.05 1 300
0.7 3 400
1.35 5 500
2 7 600
2.65 9 700
symbol
PheOH concentration (mg L−1) catalyst loading (g L−1) pH slurry flow rate (mL min−1)
The principle of response surface methodology (RSM) has been described by Khuri and Cornell,19 and its objective is to optimize the response based on factors investigated.20 4153
dx.doi.org/10.1021/ie201384s | Ind. Eng. Chem. Res. 2012, 51, 4152−4160
Industrial & Engineering Chemistry Research
Article
The simplest and most direct way of finding the RTD curve is a physical or nonreactive tracer with pulse injection. In the present study, bulk material was first fed into the reactor until a steady state was established. The tracer (1 mL of 400 mg L−1 Reactive Black 8 dye for each nozzle) was then injected instantaneously into the inflow stream. Samples were collected at the outlet of the reactor at regular time intervals. The disturbance to the bulk flow caused by the injection of the tracer may be assumed to be negligible. The collected samples were analyzed by a UV−visible spectrophotometer to determine the concentration of dye. The residence time distribution [E(t)] was calculated from the latter data [dye concentration with time, C(t)] using the following equation:
An empirical second-order polynomial model for independent variables was considered as follows, k
Y = b0 +
k−1 k
k
∑ biXi + ∑ ∑ bijXiXj + ∑ biiXi 2 + e i=1
i=1 j=2
i=1 (1)
where Y is the predicted response (phenol degradation), Xi and Xj are input variables that influence the response Y, b0 is the offset term, bi is the linear effect, bij is the squared effect, bii is the interaction effect, and k is the number of variables. Analysis of variance (ANOVA) was conducted to determine the significance of the model. The quality of polynomial equation was judged by the determination of the coefficient (R2), and its statistical significance was checked by Fischer’s F-test. The response surface and contour plots of the model-predicted responses were utilized to assess the interactive relationships between the significant variables. Design-Expert, version 7.1.3 (Stat-Ease Inc., Minneapolis, MN) was used for designing the experiments and for regression and graphical analysis of the experimental data obtained. 2.5. Experimental RTD Determination. The RTD is one of the major informative characteristics of the flow pattern in a chemical reactor. It provides information on the duration of stay of various elements within the reactor and allows for thorough comparison between systems having different configuration of the reactor. For reactor design and scale-up purposes, it is essential to have information on the RTD.
E (t ) =
C(t ) ∞ ∫0 C(t ) dt
(2)
3. RESULTS AND DISCUSSION In the present study, operating parameters for photocatalytic phenol degradation in a photo-impinging streams reactor have been optimized. In addition, the residence time distribution has been determined and the reactor modeling presented. 3.1. Optimization of Operating Parameters. CCD was employed to study the interactions between the significant factors and to determine their optimal levels. The design matrix of tested variables and the experimental data are given in Table 2. Multiple regression method was used to analyze the data.
Table 2. Design Matrix of Experiments X1
X2
X3
X4
phenol conversion
std
run
type
phenol concentration
catalyst loading
pH
slurry flow rate
after 30 min
3 8 9 13 18 2 15 20 27 23 4 6 7 28 16 5 10 14 21 19 12 30 25 24 17 1 11 22 29 26
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
fact fact fact fact axial fact fact axial center axial fact fact fact center fact fact fact fact axial axial fact center center axial axial fact fact axial center center
−1 +1 −1 −1 +2 +1 −1 0 0 0 +1 +1 −1 0 +1 −1 +1 +1 0 0 +1 0 0 0 −2 −1 −1 0 0 0
+1 +1 −1 −1 0 −1 +1 +2 0 0 +1 −1 +1 0 +1 −1 −1 −1 0 −2 +1 0 0 0 0 −1 +1 0 0 0
−1 +1 −1 +1 0 −1 +1 0 0 0 −1 +1 +1 0 +1 +1 −1 +1 −2 0 −1 0 0 0 0 −1 −1 +2 0 0
−1 −1 +1 +1 0 −1 +1 0 0 −2 −1 −1 −1 0 +1 −1 +1 +1 0 0 +1 0 0 +2 0 −1 +1 0 0 0
0.8251 0.7501 0.8599 0.8152 0.6225 0.6128 0.9794 0.8452 0.8041 0.7804 0.6878 0.5152 0.9764 0.8123 0.7626 0.8021 0.6649 0.5287 0.4138 0.3263 0.6931 0.7815 0.8090 0.7601 0.9991 0.8393 0.8641 0.7342 0.8082 0.8098
4154
dx.doi.org/10.1021/ie201384s | Ind. Eng. Chem. Res. 2012, 51, 4152−4160
Industrial & Engineering Chemistry Research
Article
that useful information is lost. By correlating the successive experimental results (using various Δt) with the theoretical model, the true RTD curve may be determined.21 In order to perform RTD experiments, a circular plate divided into 27 equal cells was constructed. The plate was placed under the discharge port of the reactor and was being driven at the selected speed by means of an electric motor (Figure 1b). Different sampling intervals were thus obtained by changing the speed of rotation. Since UV light has no effect on the hydrodynamics of the reactor, RTD studies were performed in the absence of UV light in order to hinder any interaction between UV light and the tracer. Ten series of RTD experiments were performed by varying Δt from 0.6 to 1.1 s in order to determine the best value for Δt. RTD studies were first carried out in the absence of photocatalyst. In the next step, after determination of Δt, the experiments were conducted in the presence of photocatalyst, using phenol as the tracer. By measuring the concentration of tracer in each cell, the age distribution [E(t)], the variance (σ2), and the mean residence time (tm̅ ) were calculated using the following relations and plots of E(t) versus nΔt as the theoretical RTD curve has been presented in Figure 2.
The adequacy of the model was checked using ANOVA, as shown in Table 3. The “F-value” and the value of “Prob > F” for Table 3. ANOVA for Response Surface Quadratic Model source
sum of squares
df
mean square
F-value
p-value Prob > F
model X1 X2 X3 X4 X1 × 2 X1 × 3 X1 × 4 X2 × 3 X2 × 4 X3 × 4 X12 X22 X32 X42 residual
0.59 0.26 0.16 0.022 5.860 × 10−4 3.706 × 10−3 5.134 × 10−3 3.700 × 10−6 0.032 9.872 × 10−5 3.510 × 10−4 5.612 × 10−3 0.048 0.055 4.762 × 10−4 0.093
14 1 1 1 1 1 1 1 1 1 1 1 1 1 1 15
0.042 0.26 0.16 0.022 5.860 × 10−4 3.706 × 10−3 5.134 × 10−3 3.700 × 10−6 0.032 9.872 × 10−5 3.510 × 10−4 5.612 × 10−3 0.048 0.055 4.762 × 10−4 6.204 × 10−3
6.83 41.95 25.23 3.52 0.094 0.60 0.83 5.963 × 10−4 5.14 0.016 0.057 0.90 7.78 8.91 0.077
0.0003
