Photocatalytic conversion of hexavalent chromium

Photocatalytic conversion of hexavalent chromium with low dosages of titania nanoparticles: RSM modeling, employing a new method in kinetic Study and energy consumption Amir Azizi Young Researchers and Elite Club, Arak Branch, Islamic Azad University, Arak, Iran Email: [email protected]

Javad Saien Department of Applied Chemistry, Bu-Ali Sina University, Hamedan, Iran Email: [email protected]

Abstract Photocatalytic reduction of hexavalent chromium Cr(VI) ions in aqueous media was performed with remarkably low dosages of commercial TiO2 nanoparticles. A direct imposed irradiation photo reactor, equipped with ultrasonic source was used. Four influencing parameters, including temperature within the conventional range of (15 to 45) °C, were considered. Design of experiments, modeling and process optimization were performed using central composite design (CCD) of response surface methodology. Accordingly, a reduced quadratic expression was developed to predict the reduction efficiency, and analysis of variance showed the model well consistency with experimental data. At the found optimized conditions of [TiO2]= 33.1 mg/L, pH= 2.50, T= 36 °C and t= 120 min, about 81% efficiency was achieved for reduction of 5 mg/L of initial Cr(VI). The process was revealed proceeding through parallel branches of photolysis and the photocatalysis simultaneously. In kinetic investigations, the effect of catalyst particles’ turbidity was taken into account and the rate of reduction of the Cr(VI), under mild conditions, was expressed as the sum of the rates of individual photolysis and photocatalysis process branches. Pseudo first order reactions are proceeding. Furthermore electrical energy consumption was evaluated for overall process. Keywords: Photocatalytic reduction, Hexavalent chromium, Modeling, Kinetic, Energy consumption

1

Introduction Chromium is widely detected in surface water and groundwater at sites associated with industrial activities. Industrial sources such as chrome plating, electronic, metallurgical, wood processing and leather tanning industries release hexavalent chromium, Cr(VI), in effluent streams with a high level of mobility and a notoriously toxicity of mutagenic and carcinogenic (Donmez and Aksu, 2002; Palmer and Wittbrodt, 1991). The approved tolerance limits for Cr(VI) existence in the surface and drinking waters are 0.1 and 0.05 mg/L respectively (WHO,2006). The methods generally used to Cr(VI) removal from wastewaters include adsorption (Bowers and Huang, 1980), ion exchange (Petruzzelli, et al, 1995) and electrochemical (Wu and Peng, 2011) methods. An alternative clean treating route that has received much attention is photocatalytic reduction in the presence of some semiconductor materials such as titania (TiO2), ZnO, CdS or WO3 under visible or UV irradiation (Sun and Reddy, 2005; Domenech and Munoz, 1987). Ultraviolet (UV) light, visible light, sunlight, or ultrasonic sources are commonly used for this purpose (Zhao and Yang, 2003). Titania is a common and suitable semiconductor photocatalyst and the corresponding photocatalytic steps are given as (Hoffmann, 1995): - charge carrier generation;

TiO2  h (λ 400nm)  e  h 

fast

(1)

h   Ti4OH  (Ti4OH ) 

(10 ns)

(2)

e   Ti 4 OH  (Ti 3 OH)

(100 ns)

(3)

Ti 4  e   Ti 3

(10 ns)

(4)

slow

(5)

fast

(6)

slow

(7)

- charge trapping in the particles;

- charge recombination;

e  (Ti4OH )  Ti4OH 

3

4

h  (Ti OH)  Ti OH - interfacial charge transfer:

(Ti4OH )  Red  Ti4OH  Red  3

4



(Ti OH)  OX  Ti OH  OX

very slow (8) Based on pH level, the photocatalytic reduction of chromium is well described by the capture of photo-excited conduction band electrons followed by reduction (Khalil et al, 1998; Liu et al, 2004): - In acidic media 2

Cr2O7  14H  6e  2Cr 3  7H2O -

(9)

In neutral media 

HCrO4  7H   3e   Cr 3  4H 2 O -

(10)

In alkaline media

CrO 4

2

 4H 2 O  3e   Cr(OH)3  5OH 

(11) In the absence of reducing agents, H2O molecules accept the valence band holes, producing hydrogen peroxide (Schrank et al, 2002):

2H 2 O  2h   H 2 O 2  2H 

(12) Also, in the presence of UV light, the reduction can proceed via photochemical process and photolysis of water molecules (Wang et al, 2009: Kyung et al, 2005) according to the following reactions:

2H 2O  h  O2 (g)  4H   4e 

(13)



2e  O 2  2H  H 2 O 2

(14) 2



2HCrO4  3H 2 O 2  8H   2Cr 3  3O 2  8H 2 O

(15) For the experimental design and optimizing the photocatalytic processes, the approach of one factor at a time (OFAT) is frequently used, where the effect of one operational factor is studied after another. This approach requires varying only one variable while keeping the others constant. Although the approach is widely acceptable, results may be insignificant if the variables do not exhibit independent influence. When several variables and their interactions affect the response in a process, such defects can be excluded by the statistical experimental design approach such as response surface methodology (RSM). The RSM has pre-determined experimental points that are dispersed uniformly throughout the study field ranges (Fujishima et al, 2008; Chong et al, 2009). This method reduces the number of experiments and provides a model for process optimization as well as monitoring the influence of inter-variable interactions on the process outcome (Chong et al, 2009; Montgomery, 2001). The pervious researches on the chromium reduction have been related to the use of OFAT method in the presence of very high amounts of TiO2 particles (on the level 103 mg/L or so) (Khalil et al, 1998; Ku and Jung, 2001). In the present study, photo-reduction was performed with very low amounts of nano titania particles, and modeled via RSM. This and other important operational parameters were considered to follow the chromium reduction efficiency (RE). Accordingly, a mathematical expression was developed to reflect the process variations and to evaluate the optimum conditions. Under optimum conditions, the process kinetic and the cost-effective advantage were investigated. Finally, electrical energy consumption (EE) was proposed to compare the performance of different related processes.

Experimental Chemicals All the used chemicals were of analytical grade. Titania nano particles (P-25, purity >99.5%, 75% anatase, 25% rutile) was supplied by Plasma Chem. According to the manufacturer report, the specific surface area and particle size were about 50 m2/g and 21 nm, respectively. Potassium dichromate, 1,5diphenylcarbohydrazide (DPC), acetone, sulfuric acid and sodium hydroxide, potassium iodine, sodium thiosulfate, acetic acid and starch indicator all obtained from Merck Company. Deionized water (conductivity less than 0.08 μS/cm) was utilized for the solutions. Photo-reactor and experimental procedure A cylindrical photo-sono reactor made of glossy stainless steel with total capacity of 1.25 L and dimensions of 90 mm diameter and 200 mm height was used (Saien et al, 2010). The light source was a 250 W mercury lamp (165 mm body length and 80 mm arc length) with wavelength range of 280– 400 nm and the maximum emission of 365 nm (measured by a TOPCON UV-R-1 spectro-radiometer). The lamp was located centrally and directly irradiated the aqueous solution around. This geometry allowed a homogeneous irradiation and perfect reflection for the beams contacting the reactor wall. To propagate the ultrasonic waves into the reaction media, an ultrasound source (28 kHz, 60 W) was located at the outside bottom of the reactor. So, both fine mixing and the catalyst particle dispersion could be achieved before each experiment. The temperature during the reaction was kept constant through a stainless steel water-flow jacket from a thermostat bath. Initial solutions of Cr(VI) were prepared with 5.0 mg/L of potassium dichromate and after adjustment of pH with either dilute sulfuric acid or sodium hydroxide, one liter solution was transferred into the reactor. After addition of desired amounts of the TiO2 particles, sonication and temperature adjustment were established and the UV lamp was switched on. Samples of 2 mL were taken out at different times. The nano particles were separated with vigorous centrifuging and the residual concentration of Cr(VI) ions was analyzed by colorimetry with 1,5-diphenylcarbohydrazide at λmax=542 nm with the aid of appropriate calibration data (EEPC, 2002). Solutions with Cr(VI) concentration more than about 1.8 mg/L were accurately diluted to be within the linear absorbance region, and then their absorbance were measured. The maximum wavelength and the molar absorption coefficient of Cr(VI) are not much dependent on the solution pH within the range of 2.5-6.5. Using this method, the reduction efficiency percentage (RE) at any time was obtained from: 3

[Cr(VI)]  [Cr(VI)]t (16) 100 [Cr(VI)] where [Cr(VI)]  and [Cr(VI)] t are the initial and the appropriate time concentrations, respectively. RE 

It is noteworthy that during the process, the solution pH showed no change. To explore the extent of Cr(VI) ions adsorption by TiO2 nanoparticles, the amount of the ions were measured via iodometric titration (Kolthoff and Belcher, 1957) before and after separation of the particles from the solution. Negligible amount of Cr(VI) was removed from the samples, pointing out the stability of Cr (VI) and lack of any adsorption by titania nanoparticles, in agreement with previous studies (Wang et al, 1992; Chen and Ray, 2001). Design of experiments and RSM strategy The central composite design (CCD) is the most popular RSM. This design consists of three types of points: cubic, axial and central. The number of required experiments (N) can be determined by N  2k  2k  N0 where k is the number of factors and 2k, 2k and N0 are the cubic, axial, and the center point runs, respectively. The center point of CCD is used to calculate the experimental error. The distance of the axial points from the center points are dependent on the number of factors chosen for the experiments (Montgomery, 2001). The variable parameters were initial pH, the catalyst dosage, temperature and reaction time, having influence on the desired response of RE. Each of the variables was examined at five different levels (−α, −1, 0, 1, +α) and all of the variables were taken at a central coded value considered to be zero (Table. 1). The ranges of these variables were chosen considering a number of preliminarily experiments and the previous relevant results (Saien and Soleymani, 2009). The distance of the axial point from the center point is named α (alpha) (Sakkas et al, 2010). In this study, coded α value was fixed at 1.4141 and actual values of the variables can be determined by the multiplication of fixed coded α value by the step of factor level and then summation or subtraction of obtained value from the central point. For example, the center point value of TiO2 concentration was 25 mg/L and the step of factor level was 17 mg/L; so, −α will be 25−(17×1.4141) and +a will be 25+(17×1.4141). The corresponding designed CCD matrix for experimental runs is given in Table 2. Experimental data were analyzed using Design Expert software, V. 8.0.5 Trial. Table 1. Experimental ranges and levels of the independent variables used in terms of the real and coded factors. Levels and ranges −α

(−1)

(0)

(+1)

+α

1.78

2.50

4.0

5.50

6.12

0.96

8.0

25.0

42.0

49.04

xT

16.69

20.0

28.0

36.0

39.31

xt

47.57

60.0

90.0

120.0

132.42

Variables solution pH,

xpH

[TiO2] dosage (mg/L), Temperature

(°C

),

Reaction time (min),

xTiO 2

Table 2. The 4-factor central composite design matrix and the values of the response function. Design factor Runs 1 2 3 4 5 6

pH 0 +1 0 0 +1 −1

[TiO2] (mg/L) 0 −1 0 0 −1 +1

T (˚C) 0 −1 0 0 −1 −1

RE t (min)

Exp.

Pred.

−α +1 0 0 −1 −1

20.8 12.6 30.2 29.7 8.9 27.1

21.7 11.1 28.4 28.4 10.2 26.9

4

7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

+1 0 −1 0 0 0 0 +1 +1 −1 0 0 0 +α −α +1 −1 −1 0 +1 −1 −1 −1 +1

−1 0 +1 0 0 0 0 +1 +1 +1 0 0 −α 0 0 −1 +1 −1 +α +1 −1 −1 −1 +1

−1 0 +1 0 +α 0 0 +1 −1 +1 0 0 0 0 0 +1 −1 +1 0 −1 −1 −1 +1 +1

+1 −α −1 0 0 0 0 +1 0 +1 +α 0 0 0 0 +1 +1 −1 0 +1 −1 +1 +1 −1

16.8 16.9 40.5 27.7 44.2 28.1 27.2 39.1 19.7 82.2 52.1 27.1 14.3 19.3 56.2 29.5 49.2 33.0 23.6 26.2 20.8 40.2 70.2 21.3

16.0 18.9 41.8 28.4 43.3 28.4 28.1 40.0 18.9 80.2 50.0 28.4 13.0 20.7 54.8 31.3 50.5 33.5 25.3 24.7 18.2 41.9 71.6 19.8

Results The mathematical modeling The results show that RE ranges from 8.9 to 82.2% under different conditions (Table 2). The variations can be analyzed with the aid of a mathematical model in agreement with the obtained data. The step-wise model fitting was employed to determine the best mathematical expression to describe the obtained reduction efficiencies. Considering the appropriate “lack-of-fits” (the variation between the model prediction and experimental responses) and other statistical items, a quadratic mathematical reduced expression for modeling reduction efficiency in terms of given variables was obtained as

RE  28.36  12.06 xpH  4.34 xTiO 2  10.96 xT  7.63xt  4.45 xpH xT  3.61xpH xt  3.61xt xT

2 2  4.68 xpH  4.61xTiO  3.07 xT2  2.07 xt2 2

(17) where xi denotes the values of the variables and t is process time. The significant of the model terms was evaluated based on the F probability “prob > F’ at the 95% confidence limit. Parameters with “prob > F” less than 0.05 are statistically significant. In the present work, all factors in terms of the linear, interaction and quadratic, were significant except the interactions of TiO2 with pH, T and t terms, which were omitted. The analysis of variance (ANOVA) was performed (Table 3). The statistical criteria of the reduced model showed a value of 0.993 for the coefficient of determination (R2, goodness of model fitting with data) which implies that 99.3% of the data variations can be explained. Adj-R2 (modified R2 that adjusts for the number of explanatory terms in the model) value of 0.988 is very close to the corresponding R2 value. Also, pred-R2 (indicates how well the model predicts responses for new observations) of 0.9758 is in reasonable agreement with the Adj-R2. The values of prob > F less than 0.0001 and the F-value of 219.07 imply that this model is satisfactory. The recent criterion is calculated by model mean square divided by residual mean square (Sakkas et al, 2010) Also, the lack-of-fit F-value (test for comparing lack-of-fit variance with pure error variance) of 2.35 implies that the lack-of-fit is not significant relative to the pure error. Table 3. ANOVA for the response quadratic model.a Source Sum of squares Degrees of freedom

Mean square

F-Value

Prob>F

Model

8115.96

11

737.81

219.07