Removal of Phenol using ZnO Nanoparticle in Three Phase Fluidization

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  • Authors : Mr. Dineshkumar M, Dr. A. Sivalingam, Dr. M. Thirumarimurugan
  • Paper ID : IJERTV4IS031155
  • Volume & Issue : Volume 04, Issue 03 (March 2015)
  • DOI : http://dx.doi.org/10.17577/IJERTV4IS031155
  • Published (First Online): 01-04-2015
  • ISSN (Online) : 2278-0181
  • Publisher Name : IJERT
  • License: Creative Commons License This work is licensed under a Creative Commons Attribution 4.0 International License

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Removal of Phenol using ZnO Nanoparticle in Three Phase Fluidization

Dineshkumar M1, Dr. A. Sivalingam1, Dr. M. Thirumarimurugan1

1Department of Chemical Engineering, Coimbatore Institute of Technology, Coimbatore 641014, India.

Abstract: Phenol is a wide spread persistent organic pollutant that is commonly present in industrial effluent streams such as those of the petrochemical industries, kraft pulp mills, olive oil production and other chemical industries. Since phenol is harmful to organisms even at low concentration and is difficult to be graded biologically, it has been classified as a priority pollutant in wastewater. There are many physical and chemical treatment methods available for the removal of phenol but all these methods have problem associated such as secondary effluent, hazardous and harmful end products, high energy consuming, non economic etc. These problems can be overcome by the use of nanotechnology which is simple when compared to the other methods and complete removal of the pollutants is possible. In this study, Zinc oxide nanoparticles was synthesized by the wet chemical process and used for the removal of phenol from pollutants. ZnO nanoparticles were characterized by X- Ray Diffraction (XRD) and Fourier Transform Infra Red spectroscopy (FTIR). Using fluidized bed reactor the characteristics of removal of phenol was studied. The parameter like pH was varied as 3, 7 and 11 phenol concentration was varied from 10 to 50 mg / l as case I, 20 to 100 mg / l as case II, 50 to 250 mg / l as case III and 100 to 500 mg / l as case IV in the difference of 10, 20, 50, 100 mg / l respectively and adsorbent dosage varied from 0.5 to

    1. g / l for case I and case II, 2 to 10 g / l for case III and 4 to 20 g / l for case IV. The optimized parameter for the removal of phenol from pollutant was predicted by using Design Expert software.

      Keywords: ZnO nanoparticles, Fourier Transform Infra Red spectroscopy, wet chemical process, fluidized bed reactor and Minitab software.

      1. INTRODUCTION

        Phenol one of the most common organic water pollutants is highly toxic even in low concentrations. Besides, phenol leads further to the generation of substituted compounds during disinfection and oxidation processes in natural water (1, 2, 3). Phenol is also relevant in the field of environmental research, because it has been chosen frequently as a model pollutant and many data are available on its removal and destruction in particular with respect to wastewater treatment. Phenol exists in all industrial wastewater, such as refineries (6-500 mg/l), coke making

        operations (28-3900 mg/l), coal processing (9-6800 mg/l) and manufacture of petrochemicals (2.8-1220 mg/l) (4). Phenol is also the main organic constituents exist in condensate streams of coal gasification and liquefaction (5). Phenol exists in waste water of pharmaceutical, plastics, wood products, paint, pulp and paper industries (0.1-1600 mg/l).

        Phenol containing wastewater must not drain into open water without treatment because of the toxicity of phenol. It also contributes to off-flavors in drinking and food processing water. Thus, lots of abatement technologies for phenol from wastewater were developed, whose primary mechanisms are separation and destruction. For separations, the solid-liquid-vapor state diagram for the water-phenol system to distillate the phenol contamination (6).

        The best abatement technologies for removal of phenol from wastewater to be applied strongly depend on single cases, in particular on the concentration of phenol in the stream, the co-presence of other contaminants, the nature of the plant where this problem is found. Now, a number of strategies such as oxidation with ozone-hydrogen peroxide, biological methods, membrane filtration, ion exchange, electrochemical oxidation, photo catalytic degradation and adsorption have been used for the removal of phenol (7, 8). Review on available technologies for phenol removal from fluid streams has been recently published providing comparison of the experimental conditions and the performances of different techniques (9).

        Nanotechnology involves the creation and manipulation of materials at the nanometre (nm) scale either by scaling up from single groups of atoms or by refining or reducing bulk materials. A nanometre is 1 x 10-9 m or one millionth of a millimetre. The properties of many conventional materials change when formed from nanoparticles. This is typically because nanoparticles have a greater surface area per weight than larger particles which causes them to be more reactive to some other molecules. Nanoparticles are used, or being evaluated for use, in many fields (10).

        Up to now, ZnO has been synthesized by various techniques including sol-gel method, hydrothermal synthesis method, chemical vapor deposition, precipitation method, laser vaporization condensation method and spray-pyrolysis method (11). Among these techniques, spray-pyrolysis process has been successfully applied to synthesize a wide variety of inorganic and organic materials (12). It has many advantages, which were high purity of synthesized powders, regular shape

        of particles, a better control of stoichiometry and continuous working.

        Process design is typically complex due to varying customer demands and technology advances. Several responses must generally be considered in complex product/process designs. Therefore, simultaneously optimizing multiple responses is of priority concern. Design of experiments (DOE) is extensively adopted in industry to improve processes, product design or obtain an optimal parameter setting for process parameters. When utilizing DOE, Response Surface Methodology (RSM) is frequently employed to obtain the optimal parameter setting following analysis of variance (ANOVA) for identifying significant factors (13). Through RSM, an equation representing the approximate relationship between a single response and control factors can be obtained based on experimental data. A contour plot is used to characterize the response surface graphically and determine the optimal parameter-setting. When multiple responses are considered, the optimal parameter setting is obtained by observing overlay contour plots (14). Because RSM constructs response surfaces for each

          1. Characterization of ZnO Nanoparticle

            Size of the nanoparticles were analyzed by XRD and confirmed by using FTIR spectroscopy.

          2. Preparation Of Synthetic Effluent

            To get a 1000 ppm of synthetic effluent , 0.93 ml of phenol was dissolved in 1000 ml water. Effluent was prepared by varying the concentration 10 ppm to 500 ppm. The solution was prepared and stored in a brown color glass container.

            response, the overlay contour plots are complex when many responses are considered. In this case, determining the optimal parameter-setting is difficult. A conventional means of

            Fig 1. Glass beads and ceramic rings before coating

          3. Analysis of Phenol Solution

            Fig 2. Glass beads and ceramic rings

            after coating

            optimizing multiple responses in DOE is to formulate a multi- response problem as a constrained optimization problem (15). The response surface of a concerned response is selected as the objective function, and the remaining response surfaces are selected as constraint functions. Mathematical programming is then utilized to acquire the problem solution.

      2. EXPERIMENTAL SETUP AND PROCEDURE

          1. Precipitation of ZnO from Aqueous Solution of Zinc Salts

            A one-step wet-chemical process was employed for synthesis of ZnO nanoarticles as shown in equation (1).

            ZnSO4 + 2NaOH ZnO(s) + Na2SO4 + H2O (1)

            Around 250 ml of 0.05 M zinc sulphate was prepared and 150 ml of 1N NaOH was added slowly to the zinc sulpahte solution. A white precipitate obtained was stirred for 2hr to get a milky white solution. Then the solution was placed in an oven at 100 0C. After removal of the moisture content, the solid was crushed using sterilized mortar to get a powder. The solid was washed in double distilled water, then dried and crushed, followed by calcination at 800 0C.

            The concentration of the phenol in the synthetic effluent was determined by spectrophotometry method. The absorbance of colored complex of phenol with 4- aminoantipyrene was read at wavelength 700nm. 4- aminoantipyrene solution was added to the effluent at a ratio of 15 ml in 1000 ml (17).

              1. Experimental Procedure

            Removal of phenol from the effluent was carried out using two methods, batch reactor and batch fluidized bed reactor. In the batch reactor, the effect of pH, effluent concentration and adsorbent dosage were studied. In batch fluidized bed reactor, the optimized pH value in batch stirring method was used to prepare synthetic effluent (18). In this method, the driving forces for the removal of phenol are gas flow rate, liquid flow rate, concentration of the effluent and adsorbent dosage, which are optimized using the software MINITAB17. The parameters varied are concentration (100-

            500 mg/l), adsorbent dosage (2.0-10.0 g/l), gas flow rate (0.025-0.125 cm3/sec) and liquid flow rate (50-250 l/hr).

            Disengagement

            Section

          2. Coating of Nanoparticle

        Coating of nanoparticles on glass beads and ceramic rings were done by the following methods. 50 g of ZnO nanoparticles and 9 g of polyethylene glycol are mixed with 50 g of isopropanol and stirred at room temperature for 1hr. Then 4 ml of deionized water and 2 g of nitric acid was added slowly under vigorous stirring conditions. The glass beads

        AIR FLOW

        WATER FLOW

        BYPASS

        Collection Tank

        Mercury Manometer

        Test Section

        were added to the solution and placed in an oven to remove the moisture content (16).

        Storage Tank

        with Heater

        Centrifugal

        pump

        Ball

        Valve

        Rotometer

        Orificemeter

        Gas – Liquid Distribution Section

        Compressor

        CCl4 Manometer Ball Valve

        Fig 3. Fluidized COLUMN

      3. RESULTS AND DISCUSSION

        3.1 Characterization of Zinc-oxide Nanoparticle

        constant. The adsorbent dosage of ZnO nanoparticles of 1.5 g/l and initial concentration of phenol of 30 mg/l was maintained at the beginning of the process. The percentage of phenol removal was good at pH 7, whereas pH 3 and 11 shows less removal compared to pH 7. From the below graph the optimized pH 7 effluent was used. This is due to high alkalinity and high acidity of the effluent (19).

        3.3 Response Surface Method

        RSM defines the effect of the independent variables, alone or in combination, on the processes. In addition to analyzing the effects of the independent variables, this experimental methodology also generates a mathematical model.

        Fig 4. XRD analysis on Zinc oxide Nanoparticle

        In the present study, the BoxBehnken experimental design was chosen for finding out the relationship between the response function variables. BoxBehnken design is rotatable second-order design based on three-level incomplete factorial designs [20]. The special arrangement of the BoxBehnken design levels allow the number of design points to increase at the same rate as the number of polynomial coefficients. For three factors, for example, the design can be constructed as three blocks of four experiments consisting of a full two-factor factorial design with the level of the third factor set at zero.

        Design

        Table 1. Parameters provided in Design Expert software

        Factor

        Name

        Type

        Low

        High

        A

        Gas flow rate

        Numeric

        0.025

        0.125

        B

        Liquid flow rate

        Numeric

        50

        250

        C

        Concentration

        Numeric

        100

        500

        D

        Adsorbent Dosage

        Numeric

        20

        100

        Std

        Run

        Factor

        1

        Factor

        2

        Factor

        3

        Factor

        4

        Response

        1

        A

        Gas flow rate

        B

        Liquid flow rate

        (l/hr)

        C

        Conc. (mg/l)

        D

        Adsorbent dosage (g/l)

        Removal efficiency %

        4

        1

        0.125

        250

        300

        60

        77.38

        6

        2

        0.075

        150

        500

        20

        65.84

        24

        3

        0.075

        250

        300

        100

        76.53

        23

        4

        0.075

        50

        300

        100

        94.28

        29

        5

        0.075

        150

        300

        60

        85.67

        20

        6

        0.125

        150

        500

        60

        76.45

        14

        7

        0.075

        250

        100

        60

        81.23

        22

        8

        0.075

        250

        300

        20

        69.85

        25

        9

        0.075

        150

        300

        60

        79.81

        13

        10

        0.075

        50

        100

        60

        89

        18

        11

        0.125

        150

        100

        60

        82.71

        21

        12

        0.075

        50

        300

        20

        74.05

        7

        13

        0.075

        150

        100

        100

        96.09

        8

        14

        0.075

        150

        500

        100

        81.57

        26

        15

        0.075

        150

        300

        60

        84.63

        16

        16

        0.075

        250

        500

        60

        67.81

        5

        17

        0.075

        150

        100

        20

        71.49

        Table 2. Actual design of experiments and responses

        Fig 5. FTIR analysis of Zinc oxide Nanoparticle

        The analyzed ZnO Nanoparticle obtained a size of 61.2nm calculated from the graph

        3.2. Optimization of pH

        Fig 6. Removal efficiency of adsorbent at various pH values

        To remove phenol from effluent, pH of the effluent was optimized. The pH of the solution was varied as pH 3, 7 and 11. To optimize the removal efficiency of phenol, the adsorbent dosage and initial concentration of phenl was kept

        9

        18

        0.025

        150

        300

        20

        72.07

        2

        19

        0.125

        50

        300

        60

        74.92

        28

        20

        0.075

        150

        300

        60

        80.67

        27

        21

        0.075

        150

        300

        60

        83.95

        3

        22

        0.025

        250

        300

        60

        70.26

        15

        23

        0.075

        50

        500

        60

        79.36

        10

        24

        0.125

        150

        300

        20

        60.17

        12

        25

        0.125

        150

        300

        100

        74.41

        17

        26

        0.025

        150

        100

        60

        79.92

        11

        27

        0.025

        150

        300

        100

        79.93

        1

        28

        0.025

        50

        300

        60

        84.75

        19

        29

        0.025

        150

        500

        60

        74.32

        Table 3. ANNOVA table

        Source

        Squares

        Df

        Mean Square

        F

        Value

        p-value

        Model

        1539.96

        8

        192.49

        15.38

        < 0.0001

        A-Gas flow rate

        19.28

        1

        19.28

        1.54

        0.2289

        B-Liquid flow rate

        236.74

        1

        236.74

        18.92

        0.0003

        C-Concentration

        252.91

        1

        252.91

        20.21

        0.0002

        D-Adsorbent dosage

        665.14

        1

        665.14

        53.14

        < 0.0001

        AB

        71.83

        1

        71.83

        5.74

        0.0265

        BD

        45.9

        1

        45.9

        3.67

        0.0699

        A2

        175.13

        1

        175.13

        13.99

        0.0013

        D2

        105.71

        1

        105.71

        8.45

        0.0087

        Residual

        250.31

        20

        12.52

        Lack of Fit

        224.03

        16

        14

        2.13

        0.2424

        Pure Error

        26.28

        4

        6.57

        C of Total

        1790.27

        28

        Fig 7. Combined effects of 100 mg/l of concentration and 20g/l adsorbent dosage on removal efficiency

        Fig 8. Combined effects of 100 mg/l of concentration and 60g/l adsorbent dosage on removal efficiency

        Fig 9. Combined effects of 100 mg/l of concentration and 100 g/l adsorbent dosage on removal efficiency

        Fig 10. Combined effects of 300 mg/l of concentration and 100 g/l adsorbent dosage on removal efficiency

        Fig 11. Combined effects of 300 mg/l of concentration and 60g/l adsorbent dosage on removal efficiency

        Fig 12. Combined effects of 300 mg/l of concentration and 20g/l adsorbent dosage on removal efficiency

        Fig 13. Combined effects of 500 mg/l of concentration and 20g/l adsorbent dosage on removal efficiency

        Fig 14. Combined effects of 500 mg/l of concentration and 60g/l adsorbent dosage on removal efficiency

        Fig 15. Combined effects of 500 mg / l of concentration and 100 g/l adsorbent dosage on removal efficiency

        Final Equation in Terms of Actual Factors

        Removal efficiency = 68.00490057 + 149.7630682 X Gas flow rate – 0.057166667 X Liquid flow rate – 0.022954167 X Concentration + 0.606672585 X Adsorbent dosage + 0.8475 X Gas flow rate X Liquid flow rate – 0.000846875 X Gas flow rate X Liquid flow rate – 2014.920455 X Gas flow rate2 – 0.002445969 X Adsorbent dosage2

        Final Equation in Terms of Coded Factors

        Removal efficiency = 81.94931818 – 1.2675 X A – 4.441666667 X B – 4.590833333 X C + 7.445 X D + 4.2375 X AB – 3.3875 X BD – 5.037301136 X A2 – 3.913551136 X D2

        Fig 16. Linear plot for actual values versus predicted values

      4. CONCLUSION

        The effect of phenol removal by using ZnO nanoparticles were studied. Synthesized nanoparticles were analyzed by using XRD and FTIR. From XRD image the size of ZnO nanoparticles was calculated as 61.2 nm. FTIR image shows that ZnO nanoparticles were found in the region between 400-600 cm-1. The high adsorbent dosage of 2.5 g/l has the maximum removal efficiency for 40 mg/l and 80 mg/l respectively. The maximum adsorbent dosage of 8 g/l and 16g/l removes the minimum effluent concentration as 50 mg/l and 100 mg/l respectively. By using fluidized column, the effect of removal of phenol was optimized by using Design Expert software and Minitab software. Parameters such as gas flow rate, liquid flow rate, concentration of effluent and adsorbent dosage play a vital role in treatment of effluent. By using the software it was analyzed that the effluent concentration was affected with respect to the gas flow rate, liquid flow rate and adsorbent dosage in order. At low gas flow rate and liquid flow rate the removal efficiency of phenol was high. Adsorbent dosage and effluent concentration does affect the removal efficiency but not greater than the gas flow rate and liquid flow rate. ANNOVA table, fit summary table, removal efficiency equation, linear regression equation, interaction plot and actual value versus predicted value has been provided and plotted for BoxBehnken experimental design and factorial design. It was evident that the removal efficiency of phenol was good in fluidized bed column than the batch stirring method.

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  3. Al-SultaniKadhim F et al., (2012) Characterization the Removal of Phenol from Aqueous Solution in Fluidized Bed Column by Rice Husk Adsorbent, Research Journal of Recent Sciences, Vol. 1, pp. 145-151.

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