Nickel Removal from Waters Using a Surfactant-Enhanced Hybrid

Dec 11, 2008 - UniVersity, Istanbul, 80626 Turkey ... research is to investigate the technical performance of the process under .... Istanbul Technica...
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Ind. Eng. Chem. Res. 2009, 48, 903–913

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Nickel Removal from Waters Using a Surfactant-Enhanced Hybrid Powdered Activated Carbon/Microfiltration Process. II. The Influence of Process Variables Coskun Aydiner,*,† Mahmut Bayramoglu,‡ Bulent Keskinler,† and Orhan Ince§ Department of EnVironmental Engineering and Department of Chemical Engineering, Gebze Institute of Technology, Gebze, 41400, Kocaeli, Turkey, and Department of EnVironmental Engineering, Istanbul Technical UniVersity, Istanbul, 80626 Turkey

This study focused on the removal of nickel ions from aqueous solution using a surfactant-enhanced (SE) powdered activated carbon (PAC)/microfiltration (MF) hybrid membrane process. The main objective of the research is to investigate the technical performance of the process under the influences of all relevant process variables namely process time, recycling time, pH, temperature, PAC concentration, surfactant concentration, nickel concentration, cross-flow velocity, and transmembrane pressure, thus to reveal its applicability to water problems involved in heavy metal pollution. The concentrations of PAC, surfactant, metal, and H+ ion were determined as significant process stream variables, while transmembrane pressure and temperature came into prominence as significant operating variables. ANOVA calculations indicated that the total influences of these six variables on process performance realized in a range of about 93-98%. Nickel concentration and PAC amount in the feed were established as the most influential variables for nickel rejection, and surfactant rejection and permeate flux, respectively. The increase of PAC from 0.1 to 4 g/L led to a flux decline of about 60%. The most critical parameter for process performance was the mass ratio of PAC to surfactant (PAC/surfactant) per unit mass of metal removed. The process which may be effectively operated in continuous cross-flow filtration mode within a short filtration time of 30 min has crucial advantages compared to high pressure driven membrane processes and offers a promising alternative to remove heavy metal pollutants from drinking waters and metal bearing wastewaters. 1. Introduction As of the beginning of this century, membrane technologies, which have been increasingly employing in water and wastewater treatment along the last 50 years, have made progress toward novel and diverse applications in terms of a scientific and technological viewpoint. The major reason for this advancement is the tendency to develop more economic and feasible processes in order to especially solve some special problems in water treatment, as well as the reinforcement of legal constraints and the raising of environmental consciousness of community. In the arrived situation for today, professional persons or research groups foresee as an alternative solution that combined or hybrid membrane separation processes will be used effectively in a wide variety in water and wastewater purification as a promising technique.1-4 Membrane-based hybrid processes, which have been developing depend on the separation needs, provide tremendous flexibility and effectiveness in removing of the selected components from water environment.1,4 The concept, which comprises coupling of two or more unit operations, makes it possible to achieve more effectiveness with them combined rather than each individual one, in practice.5 As a consequence of combining reaction and separation in the process, synergistic improvements could be obtained with regard to high throughput product, catalyst life lengthening, and selectivity enhancement.3 Hybrid membrane processes are widely used as an efficient way to overcome the limitations that are encountered in individual membrane processes.3-7 As compared with traditional * To whom correspondence should be addressed. E-mail: aydiner@ gyte.edu.tr. Tel.: +902626053220. Fax: +902626053205. † Department of Environmental Engineering, Gebze Institute of Technology. ‡ Department of Chemical Engineering, Gebze Institute of Technology. § Istanbul Technical University.

membrane processes, they have crucial advantages such as high quantity of treated wastewater, high removal efficiency, effective fouling control, low energy consumption, and lower backwashing frequency.3,5,7-10 In the literature, there is a lot of hybrid membrane processing studied for various water treatment demands in the past decade. The most striking uses are adsorption,1,11-13 flotation,9,10,14,15 coagulation,16-18 ozonation,19,20 photocatalysis,21-23 ultrasound,24-26 electrocoagulation,27,28 and bioreactor processes,29-33 as combined with the membrane process. An adsorption-membrane hybrid system among these processes was applied to various water treatment issues in a wide range. The most common uses of this hybrid system are the processes which are assembled with powdered activated carbon (PAC) and microfiltration (MF) or ultrafiltration (UF) as adsorbent and membrane, respectively. The PAC/membrane process is mainly utilized to remove synthetics or natural organics from waters. The principle goal in PAC use is to increase the specific surface of adsorbent and to enable higher uptakes of pollutants, in contrast to fixed bed techniques.34,35 Furthermore, the permeate flux could be enhanced by reducing membrane fouling in some cases in the process.36 However, high efficiency could not be obtained in the treatment of wastewaters including heavy metals. At this point, it can be said that the surfactant enhanced (SE) PAC/membrane technique attracts attention as a hybrid process, which exhibited good performance in the separation of heavy metals from waters.37,38 SE-PAC/MF is operated at lower pressures with a lower energy requirement as compared with other membrane separation processes. The process is essentially executed under the effect of mechanisms that simultaneously occur with direct and indirect adsorption of soluble metal ions. Despite the fact that the use of surfactant increases the metal adsorption capacity of PAC, it causes further complex behaviors in the process.38 Thus, the clarification of the process dynamics in light of input factors is

10.1021/ie8004308 CCC: $40.75  2009 American Chemical Society Published on Web 12/11/2008

904 Ind. Eng. Chem. Res., Vol. 48, No. 2, 2009

rather crucial to understanding the effects of all input factors and thus to operating the process more effectively. The SE-PAC/MF hybrid process involves inherently a great number of independent variables which may be classified in two main categories related to system components and process conditions. In a previous study, the influences of four systemcomponent variables namely membrane, adsorbent, and surfactant types as well as membrane pore size were investigated on the performance of the SE-PAC/MF hybrid process for nickel ion removal from aqueous solutions.38 This study deals with the investigation of the influences of nine process variables comprising process time, recycling time, pH, temperature, PAC concentration, surfactant concentration, nickel concentration, cross-flow velocity, and transmembrane pressure. Nickel and surfactant rejections and flux at the end of the process time were taken into account as performance responses which are critical for the technical evaluation of the process. Design of experiments (DoE) was carried out by the Taguchi method, and the experimental results were statistically analyzed using the analysis of variance (ANOVA) technique for detailed interpretations of the relative influences of process variables on performance responses. The effectiveness of the hybrid MF process in heavy metal removal from waters was consequently evaluated in terms of direct or advanced treatment of metal-contained waters. 2. Design of Experiments (DoE): A Brief Survey 2.1. Principles of DoE. In a traditional experimental approach assuming one-factor-at-a-time relationships among all input variables and responses can be established for only one variable as dependent to others. Furthermore, possible interactions between variables are not entirely detected. Besides, the accuracy of the results obtained is to be absolute under only fixed conditions and an uncertainty prevails for other conditions.39-41 DoE is a powerful tool to enable the most suitable design in terms of performance, quality, and cost for complex systems. It is able to apply in a structure including a series of tests in an organized manner with a small number of experiments. DoE can be used to establish and evaluate the reasons of purposeful changes in responses, in frame of a meaningful test plan consisting of a structured combination of numerous input variables having different levels. By the application of this method to complex processes, it can be made possible to decrease the error limit in predictions, to simplify the evaluation of the results using a suitable statistical analysis technique, and to enable the repeatability of the results under conditions at a sufficient confidence level, as well as the reduction of the number of experiments.39,40 2.2. Taguchi Methodology. The framework of the Taguchi methodology adopted in this study was schematically represented in Figure 1. The Taguchi method allows performing experiments in a rather small part of the whole as a highly fractional-factorial design. Principle tenets in this philosophy are to improve quality of a product or process accompanied by obtaining lower cost and to develop proper strategies for different implementation methods. This method consists of four main consecutive phases which are planning, conducting, analysis, and validation, respectively. Experimental error can be decreased in the applications designed by this approach, while the reproducibility and the efficiency of laboratory experiments are increased. Furthermore, the influences of all input variables on responses can be established as independent from each other, and the situations presenting the optimum conditions can be determined as well.41,42

Figure 1. Schematic representation of items in framework of the Taguchi DoE adopted in this study.

3. Experimental Section 3.1. Materials. The experiments were performed using 0.45 µm pore sized cellulose nitrate membrane, C9157 carbon adsorbent, and 1-hexadecane sulfonic acid sodium salt (HDSA) surfactant.43,44 C9157 was obtained from Sigma-Aldrich and has a BET surface area of 1000 m2/g and an average particle diameter of 55.1 µm in a particle size range of 2.5-479 µm. Its suspensions have a pH between 6 and 8.38 Cellulose nitrate membranes and HDSA (C16H33SO3Na) were supplied by Schleicher & Schuell and Alfa Aesar, respectively. The surfactant material has 99% purity. Analytical-grade Ni(NO3)2 · 6H20 was obtained from Riedel at 99% purity. The critical micelle concentration (CMC) of HDSA was determined by measuring the conductivity values of HDSA solutions at various concentrations, and the value was found to be 0.36 mM at 30 °C.38 3.2. Experimental Procedure. Experiments were carried out in two stages comprising recycling of feed solution to the feed tank without filtration (recycling) and thereafter filtration of the feed by the membrane (continuous filtration). Before the continuous membrane filtration, feed solution was circulated for 20 or 40 min to the feed tank. At the end of recycling time, the continuous filtration mode was directly operated along 30 or 120 min. Finally, the process performance was separately explored for total durations of 50, 70, 140, and 160 min. Experimental setup of a cross-flow microfiltration unit, analytical procedures, and calculation of flux and rejections were presented in our previous study.38 3.3. Taguchi Method Adopted in the Study. In the study, by taking into account that a full factorial DoE array with nine

Ind. Eng. Chem. Res., Vol. 48, No. 2, 2009 905 Table 1. Experimental Plan (L16 Orthogonal Array) Designed by the Taguchi Method and Experimental Results Obtained for Each Process Performance Parameter process variables

results

run

t (min) (X5)

t′ (min) (X6)

pH (-) (X7)

T (°C) (X8)

CA (g/L) (X9)

CS (CMC) (X10)

CM (mg/L) (X11)

ν (m/s) (X12)

∆P (kPa) (X13)

RM (%)

RS (%)

J*(m3/m2 · h)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

30 30 30 30 30 30 30 30 120 120 120 120 120 120 120 120

0 0 0 0 40 40 40 40 0 0 0 0 40 40 40 40

3 3 7 7 3 3 7 7 3 3 7 7 3 3 7 7

20 40 20 40 20 40 20 40 20 40 20 40 20 40 20 40

4 0.1 0.1 4 0.1 4 4 0.1 0.1 4 4 0.1 4 0.1 0.1 4

1 1 3 3 3 3 1 1 3 3 1 1 1 1 3 3

10 300 10 300 300 10 300 10 300 10 300 10 10 300 10 300

0.1 0.6 0.6 0.1 0.6 0.1 0.1 0.6 0.1 0.6 0.6 0.1 0.6 0.1 0.1 0.6

300 100 100 300 300 100 100 300 300 100 100 300 300 100 100 300

9.0 54.8 96.3 58.1 59.4 13.5 57.7 31.3 59.3 27.8 58.3 25.4 22.5 58.0 72.1 60.7

100.0 76.6 98.7 99.5 95.4 99.8 99.5 99.8 96.5 100.0 99.6 99.6 98.9 67.7 98.3 99.2

0.434 6.107 1.194 0.615 6.458 0.890 0.834 6.702 5.036 0.874 0.731 7.101 1.161 6.779 0.775 1.237

factors at two levels would have required an excessive number of experiments (512 ) 29), a fractional factorial array was preferred for variable screening and preliminary modeling purposes. Among various design alternatives, a standard Taguchi L16 orthogonal array was selected as the suitable one with sufficient number of experiments. Factor levels applied in this study were chosen based on preliminary experiments, a detailed literature survey and past experiences.37,42 The experimental design matrix is presented with real values of variables in Table 1. In light of the DoE array, SE-PAC/MF hybrid process was studied above the CMC of surfactant, in which nine process variables were taken into account as process time (t), recycling time (t′), pH, temperature (T), PAC concentration (CA), surfactant concentration (CS), nickel concentration (CM), cross-flow velocity (ν), and transmembrane pressure (∆P). The three control factors nickel rejection (RM), surfactant rejection (RS), and flux at the end of the process time (J*) were considered for the evaluation of the performance of the hybrid MF process in nickel removal from water. 3.4. ANOVA Calculations. DoE results obtained in the study were statistically analyzed by the ANOVA technique to assess the influence and relative contributions of input variables as well as their interactions on process responses.38,42,45,46 Calculations in the ANOVA methodology used in the analysis of the experimental results in frame of DoE were given in the Supporting Information. 4. Results and Discussion 4.1. Statistical Analyses of the Results. 4.1.1. Main Effects of Process Variables. Main effect plots, shown in Figure 2, were separately obtained from the arithmetic averages of response values in two sets of low and high level for each variable which include 8 data each for low and high levels and were used to evaluate the effects of process variables on process performance. 4.1.1.1. Process and Recycling Times. As the process time increased, nickel rejection and flux partly increased, but surfactant rejection decreased. The increase of recycling time increased the flux while decreased the rejections. In general, surfactant adsorption on different adsorbents generally occurs in four regions when a typical isotherm was plotted on a log-log scale. In region I, adsorption increases linearly with concentration, with a slope of approximately one, as a result of electrostatic adsorption of monomers on the adsorbent. Adsorp-

tion performs a sudden increase in region II, resulting in surface aggregation of the surfactants, while region III shows a slower rate of adsorption than region II. The beginning of the fourth region of the process is the point at which the adsorption process reaches equilibrium and adsorption isotherm curve shows a maximum. This behavior mainly occurs by adsorption of surfactant onto oppositely charged solid surface, and its reasons are not well understood. Further, surfactant adsorption performs at three various cases in the fourth region: equilibrium, fluctuation, or reduction.47 In addition to this, the main removal mechanism for surfactant in hybrid system was determined as the adsorption on PAC.38 From the aforementioned knowledge, the partial decrease in rejections with increasing recycling time put forward that, the adsorption of HDSA on PAC reached to the fourth region in the 40 min recycling stage. Otherwise, surfactant adsorption had to be increasing when recycling time increased. These results concluded that, the maximum adsorption capacity for surfactant in the presence of a metal ion could be obtained within a shorter time than 40 min. Because the results obtained for each main variable are in principle independent of those of others, the decrease of surfactant rejection with increasing process time was another piece of evidence of the fourth region adsorption of surfactant. This meant that the maximum adsorption of HDSA on PAC was reached earlier than 30 min due to a decrease in surfactant rejection from 30 to 120 min of filtration time. The surfactant adsorption process showed a behavior which takes place with partial translocation of surfactants adsorbed by PAC among the solid surface and the bulk solution and resulted in insignificant fluctuation in the adsorbed surfactant amount. However it is not obvious that the ones from free PAC particles in the feed or PAC particles participated into the gel layer are responsible for this circumstance. Consequently, the hybrid process may be operated within a short time of 30 min in the removal of nickel ions from water environment. Although a decrement in nickel rejection was anticipated because of the desorption of nickel bound surfactants to the solution, both direct adsorption of free nickel ions on PAC and their trapping by free surfactant aggregates blocked in the membrane pores and the secondary membrane layer38 partially increased the nickel rejection with an increase of the process time. In the case of a recycling application, the fouling effect on the membrane during the filtration period decreased because a lesser amount of surfactant and nickel remained in the feed. This revealed that a partial higher flux may be achieved

906 Ind. Eng. Chem. Res., Vol. 48, No. 2, 2009

Figure 2. Main effect plots for each process variables on nickel rejection (top), surfactant rejection (middle), and flux at the end of the process time (bottom).

at the end of the 120 min process time compared to that without recycling. 4.1.1.2. pH. pH influences considerably the separation of nickel ions by the hybrid MF process. When pH increased from 3 to 7, both surfactant and nickel rejections increased, but flux decreased. It is well-known that the presence of electrolyte in a surfactant solution gives rise to the decrease of repulsive forces between charged headgroups of micelles, whereby the CMC of the surfactant decreases via the increase in aggregation number and the volume of micelles.48 The reason for the decrement in the rejections at acidic pH was the competition of H+ and nickel ions with each other. The increase of H+ ions in the solution led to a decrease of indirect adsorption of nickel ions by surfactant aggregates on PAC. The effect of direct adsorption of nickel ions in simultaneous mechanisms may be considered as attenuated by surface charge variation in PAC particles, as well. Even so, the process provided higher flux achievement at acidic pH as a result of decreasing membrane fouling, contrary to decreasing rejections. 4.1.1.3. Temperature. It is known that the adsorbed amount of metal ions on activated carbon increases with increase in temperature if the thermodynamic nature of the adsorption is endothermic.49 However, despite the fact that the rate of adsorption of ionic surfactant onto adsorbent material increases with increasing temperature, its adsorbed amount decreases.47 By increasing the temperature in a hybrid process, rejections decreased in contrast to the case of flux. It is also known that the adsorption of metal ions on PAC occurs predominantly under the effect of surfactant adsorption. In other words, indirect adsorption of metal ions attached by surfactant aggregates as a case occurring with surfactant adsorption onto PAC, prevails more intensely than the direct adsorption of free metal ions on PAC.37 Although the decrement in surfactant adsorption brought about more direct adsorption of free nickel ions in the process, an increase in nickel rejection due to attenuation in indirect mechanism was not observed. Accordingly, it was concluded that the course of competitive adsorption was affected by the variations in the interaction between nickel ions and surfactant aggregates under varying conditions. In that event, the decreasing surfactant adsorption led to an increase in flux depending

on the decrease of surfactant amount participated into the cake layer by means of PAC. In membrane processes, an exponential relationship exists between temperature and flux as follows:50 J(20°C) ) JTe-0.0239(T-20)

(1)

Permeate flux decreases depending on increasing water viscosity with decreasing temperature. In the process, flux at the end of the process time decreased from 3.788 to 2.078 m3/ m2 · h by decreasing the temperature from 40 to 20 °C, respectively, which implies a flux decline of 45.1%. According to eq 1, 37.9% of the flux decline is due to temperature decrement. It can be therefore said that the difference of 7.2% in flux decline is a result of the increase in membrane fouling via the increases in rejections. 4.1.1.4. PAC, Surfactant, and Nickel Concentrations. Figure 2 indicates that PAC and surfactant and nickel concentrations as major components of the feed solution showed mainly significant influences on hybrid process performance. Nickel rejection under the studied conditions increased with a increase of surfactant and nickel concentrations depending on the existence of nickel bound micelles in the medium. Besides, surfactant rejection also increased with an increase of PAC and surfactant amounts as a result of an increase of surfactant adsorbed on PAC. However, the decrement in nickel rejection inversely with the PAC amount has drawn attention as a behavior contrary to the expectation. Although the increase in the PAC amount anticipates increasing the removal of surfactant and metal ion, one by one, this was not observed because of the coexistence of surfactant and metal ion together with PAC in the feed solution. It was assumed that there were two reasons for this unexpected behavior in the process. The first reason was the adsorption of surfactant on PAC which takes up less time compared to direct adsorption of free metal ions in the feed.37 Accordingly, when the PAC amount increased, less free surfactant aggregates, relatively, remained in the feed, as a result of the surfactant adsorption being more predominant compared with that of free nickel ions. The indirect adsorption efficiency of free nickel ions on PAC decreased due to less trapping by free surfactant aggregates. The second reason was the decrease

Ind. Eng. Chem. Res., Vol. 48, No. 2, 2009 907 Table 2. ANOVA Results for Nickel Rejection source X5 X6 X7 X8 X9 X10 X11 X12 X13 X5X7 X5X8 X5X10 X5X11 X5X13 X7X10 exp error lack of fit total a

SS 0.898 11.645 1513.405 685.523 1389.612 1059.014 1778.942 210.613 794.535 209.019 37.180 22.161 4.698 16.261 609.967 0.063 -0.063 8343.471

DF a

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1a 15

MSS

F

PS

PC

P

0.898 11.645 1513.405 685.523 1389.612 1059.014 1778.942 210.613 794.535 209.019 37.180 22.161 4.698 16.261 609.967 0.032 -0.063

28.365 367.935 47816.888 21659.504 43905.593 33460.168 56206.683 6654.428 25103.796 6604.086 1174.708 700.176 148.438 513.777 19272.243 1.000 -2.000

0.87 11.61 1513.37 685.49 1389.58 1058.98 1778.91 210.58 794.50 208.99 37.15 22.13 4.67 16.23 609.93

0.01 0.14 18.14 8.22 16.65 12.69 21.32 2.52 9.52 2.50 0.45 0.27 0.06 0.19 7.31 0.01 -0.001 100.00

18.513b 98.503c

-0.09

This degree of freedom belonging to the lowest relative influential variable was appointed to that of lack of fit. b F0.05

(1-2).

c

F0.01

(1-2).

Table 3. ANOVA Results for Surfactant Rejection

a

source

SS

DF

MSS

F

PS

PC

P

X5 X6 X7 X8 X9 X10 X11 X12 X13 X5X7 X5X8 X5X10 X5X11 X5X13 X7X10 exp error lack of fit total

5.534 8.717 218.522 124.378 254.163 130.931 232.639 3.213 148.657 3.851 5.074 6.825 2.568 4.463 165.444 0.640 -0.640 1314.980

1 1 1 1 1 1 1 1 1 1 1 1 1a 1 1 2 1a 15

5.534 8.717 218.522 124.378 254.163 130.931 232.639 3.213 148.657 3.851 5.074 6.825 2.568 4.463 165.444 0.320 -0.640

17.295 27.241 682.882 388.682 794.260 409.159 726.996 10.041 464.553 12.0357 15.856 21.329 8.025 13.946 517.012 1.000 -2.000

5.21 8.40 218.20 124.06 253.84 130.61 232.32 2.89 148.34 3.53 4.75 6.51 2.25 4.14 165.12

0.40 0.64 16.59 9.43 19.30 9.93 17.67 0.22 11.28 0.27 0.36 0.49 0.17 0.32 12.56 0.37 -0.07 100.00

18.513b 98.503c

-0.96

See footnote a in Table 2. b See footnote b in Table 2. c See footnote c in Table 2.

of the amount of free surfactant aggregates blocked in the membrane pores, which directly trap free nickel ions in the feed.38 On the other hand, when nickel concentration increased, surfactant rejection decreased depending on more direct adsorption of free nickel ions on PAC due to competitive adsorption. Although flux increased with an increase of the nickel concentration, it decreased by increasing the surfactant and PAC concentrations. It was also understood that the increase of surfactant and PAC amounts increased the membrane fouling due to their increase in the membrane and the cake layer. The increment in flux with nickel concentration originated from decreasing of membrane fouling depending on the decrement in surfactant rejection. 4.1.1.5. Cross-flow Velocity and Transmembrane Pressure. Transmembrane pressure had a remarkable influence on process performance which was not noticeably affected by altering the cross-flow velocity. Rejections and flux increased by increasing the cross-flow velocity, and the nickel rejection has had the most affected response by the variation of crossflow velocity. Despite the fact that nickel rejection varied inversely with increasing transmembrane pressure, surfactant rejection increased due to increasing adsorption of surfactant on PAC. On the other hand, the increment in flux with transmembrane pressure ascertained a proportional decrement in membrane fouling which took place by means of surfactant and PAC.

4.1.2. ANOVA Calculations and Statistical Modeling. The results concerning ANOVA calculations are given in Tables 2-4 for nickel rejection, surfactant rejection, and flux at the end of the process time, respectively. These results present important comparative knowledge as a focus of research in terms of process dynamics, by means of the detailed interpretations of influences of input variables on performance responses. According to Table 2, while all variables had significant effects on nickel rejection at a confidence level of 95%, the effect of process time only appeared to be insignificant at a confidence level of 99%. This implies that a short filtration time of 30 min is sufficient for operating the process in heavy metal purification. Noticeable variables in surfactant removal and flux decline were determined as pH, temperature, feed concentrations of PAC, surfactant, and nickel, transmembrane pressure, and pH-surfactant interaction from Tables 3 and 4, respectively. These results mean that flux decline is directly related with surfactant rejection in the process. It was previously established that the main removal mechanism for surfactant in this hybrid process is adsorption on PAC. Besides, as surfactant rejection increases, more surfactant aggregates mainly cause to more decline in flux by the ways of blocking in the membrane pores and participating into the secondary membrane (cake) layer.38 Therefore, it can be consequently said that, if desirable removal can be achieved for the selected heavy metal, higher flux would

908 Ind. Eng. Chem. Res., Vol. 48, No. 2, 2009 Table 4. ANOVA Results for Flux at the End of the Process Time source

SS

X5 X6 X7 X8 X9 X10 X11 X12 X13 X5X7 X5X8 X5X10 X5X11 X5X13 X7X10 exp error lack of fit total

DF

0.013 0.471 4.569 11.700 69.622 10.192 4.694 0.250 6.970 0.018 0.524 0.537 0.053 0.002 6.662 0.055 -0.055 116.2754

1 1 1 1 1 1 1 1 1 1 1 1 1 1a 1 2 1a 15a

MSS 0.0132 0.4706 4.5689 11.6998 69.6223 10.1921 4.6937 0.2500 6.9696 0.0181 0.5235 0.5366 0.0531 0.0023 6.6616 0.0274 -0.0548

a

F

PS

PC

0.482 17.168 166.680 426.827 2539.926 371.821 171.234 9.120 254.261 0.660 19.096 19.574 1.938 0.084 243.024 1.000 -2.000

-0.01 0.44 4.54 11.67 69.59 10.16 4.67 0.22 6.94 -0.01 0.50 0.51 0.03 -0.03 6.63

-0.01 0.38 3.91 10.04 59.85 8.74 4.01 0.19 5.97 -0.01d 0.43 0.44 0.02 -0.02d 5.71 0.42 -0.07 100.00

See footnote a in Table 2. b See footnote b in Table 2. c See footnote c in Table 2. addition of them to experimental error value. Table 5. Regression Coefficients Estimated for Each Process Performance Parameter coefficients terms

symbols

RM

RS

J*

constant X5 X6 X7 X8 X9 X10 X11 X12 X13 X5X7 X5X8 X5X10 X5X11 X5X13 X7X10

z0 t t′ pH T CA CS CM ν ∆P t/pH t/T t/CS t/CM t/∆P pH/CS

47.756900 0.236875 -0.853125 9.725630 -6.545630 -9.319380 8.135620 10.544400 3.628120 -7.046880 -3.614380 1.524370 -1.176870 0.541875 1.008120 6.174380

95.556900 -0.588125 -0.738125 3.695620 -2.788130 3.985630 2.860630 -3.813120 0.448125 3.048130 0.490625 -0.563125 0.653125 -0.400625 0.528125 -3.215620

2.933000 0.028750 0.171500 -0.534375 0.855125 -2.086000 -0.798125 0.541625 0.125000 0.660000 0.033625 0.180875 -0.183125 -0.057625 0.012000 -0.645250

be obtained in the process by abating surfactant amount which brings about less use of PAC. The Taguchi DoE approach gives also an opportunity to create an empirical model from the experimental results in frame of the selected design matrix. In this sense, multiple linear regression models were developed for each process performance parameter, separately. Coefficients related to model equations were presented in Table 5. Regression models including both process and interactive variables presented sovereign agreement between the experimental data and model-predicted values with a correlation (R2) value of 1 and a standard deviation (S) of 0 (zero) under the studied experimental conditions. If process variables were only taken into account in regression models, consistencies of nickel rejection, surfactant rejection, and flux were obtained with 0.892, 0.857, and 0.933 and 12.240, 5.601, and 1.140 for R2 and S, respectively. 4.1.3. Comparison of Influences of Process Variables. Relative influences of each process variable were shown in Figure 3, as well as their results in decreasing order for each performance response presented in Table 6. Statistical results in Table 6 were obtained for a confidence level of 99% and were discussed together with the results shown in Figure 2. Table 6 indicates that the influence of seven variables, pH, T, CA, CS, CM, ∆P, and pH/CS, on each performance parameter

d

-0.08

P d

18.513b 98.503c

These negative values were assumed to be zero (0) with the

has been important. Total influences of these variables on RM, RS, and J* were determined as 93.6, 96.8, and 98.2%, respectively. The influences of process time, recycling time, and cross-flow velocity at the studied values are so low as to be able to be eliminated. 4.1.3.1. Influences on Nickel Rejection. The influential variables on nickel rejection were ordered as CM, pH, CA, CS, ∆P, T, and pH/CS with decreasing influences of 21.3, 18.1, 16.7, 12.7, 9.5, 8.2, and 7.3%, respectively. The nickel rejection was at most affected by nickel concentration in the feed. The increase of nickel amount decreased direct and indirect adsorption of soluble nickel ions on PAC. The increase of H+ ions in the solution decreased the nickel adsorption based on competitive adsorption. At acidic pH compared to neutral pH value, H+ ions decreased CMC of HDSA by decreasing the energy of repulsion between head groups, so that more micelle structures formed in the solution. As H+ concentration in the medium increased, the decrease of indirect adsorption of nickel ions accompanied by trapping of hydrogen ions with surfactant aggregates which resulted in less binding of nickel ions in spite of the increment in size and volume of micelles. In that case, direct adsorption of nickel ions may be also attenuated by surface charge variation in PAC. In other words, the decrease in H+ concentration increased the rejection because of more trapping of nickel ions by surfactant aggregates. The decrease of PAC and surfactant amounts in the feed concluded the decrease of direct and indirect adsorption of free nickel ions and of their trapped ones with surfactant aggregates, respectively. The rejection, which decreased with increase of transmembrane pressure and temperature increased with surfactant amount simultaneously with the increase of pH to a neutral value. As a consequence, it was understood that the increase of nickel amount in the feed necessitates the increase of PAC and surfactant amounts required per unit nickel amount to be removed, in the application of the hybrid process to real wastewaters. 4.1.3.2. Influences on Surfactant Rejection. The influential variables on surfactant rejection are in order of CA, CM, pH, pH/CS, ∆P, CS, and T with the relative influences of 19.3, 17.7, 16.6, 12.6, 11.3, 9.9, and 9.4%, respectively. The most influential variable on surfactant rejection has been PAC concentration because adsorption was the effective removal process.38 The presence of nickel ions in the solution competitively decreased the surfactant adsorption on PAC. The decrease of surfactant adsorption on PAC with increase of H+ ions in the solution

Ind. Eng. Chem. Res., Vol. 48, No. 2, 2009 909

Figure 3. Relative influences of process variables on process performance. Table 6. Influence Ordering of Process Variables on Process Performance performance parameter

influence ordering of variables

CM > pH > CA > CS > ∆P > T > pH/CS > ν > t/pH . t/T > t/CS > t/∆P > t′ > t/CM > t surfactant rejection CA > CM > pH > pH/CS > ∆P > CS > T . t′ > t/CS > t > t/T > t/∆P > t/pH > ν > t/CM flux CA . T > CS > ∆P > pH/CS > CM > pH . t/CS > t/T > t′ > ν > t/CM > t/pH > t > t/∆P nickel rejection

was assumed to be due to the variations of sizes of micelles and/or surface charge of PAC. The increase of transmembrane pressure increased the adsorption efficiency of surfactant. The influence of surfactant concentration on the rejection was found to be smaller than being expected, because its adsorption occurred quickly and effectively. Temperature as the smallest influential important variable had an effect in terms of decreasing the surfactant rejection. 4.1.3.3. Influences on Flux. The order of influential variables on the flux is CA > T > CS > ∆P > pH/CS > CM > pH with the relative influences of 59.9, 10.0, 8.7, 6.0, 5.7, 4.0, and 3.9%, respectively. PAC concentration notably affected the flux and has been the most distinctive variable for the flux decline. Accordingly, the secondary cake layer is essentially PAC, and its physicochemical characteristics such as porosity, density, and thickness played the most important role in the membrane fouling. Temperature induced the increase of the flux by decreasing the viscosity and partially increasing the membrane fouling. Surfactant aggregates blocked on the surface and within the membrane pores decreased the flux in addition to the secondary layer comprising PAC particles. In this sense, if desired rejections are able to be achieved simultaneously, operation of the process with the absence of surfactant aggregates in the feed solution during the filtration leads to higher flux or flux enhancement. Another piece of evidence for this fact was the increase of the flux together with the increasing transmembrane pressure, so that surfactant adsorption increased and less surfactant remained in the solution. On the other hand, the presence of H+ ions caused an increase of the flux. The main reason for this behavior is the decrease of surfactant adsorption. In this situation, in spite of the availability of more micellar structures in the solution, the amount of surfactant incorporated into the membrane and the cake decreased, and less fouling occurred on the membrane. The same influential

process also prevailed for the case of the increase of nickel ions in the solution. 4.1.4. Validation. Two validation experiments were conducted with process times of 30 and 120 min. In the experiments, it was aimed to evaluate the direct applicability of the process to real wastewater including heavy metal pollution in addition to the model validation. Hence, feed solutions with a high nickel content of 300 mg/L were studied. The hybrid MF system was operated without recycling at the conditions of 20 °C temperature, 0.6 m/s cross-flow rate, and 300 kPa transmembrane pressure. The concentrations of PAC and surfactant were 0.1 g/L and 1.08 mM (3 CMC), respectively, and the pH of the feed solution was kept constant at 7 throughout both experiments. Experimental and regression model results belonging to the validation tests which were carried out for 30 and 120 min process times were indicated in Figure 4. As seen from Figure 4, regression model presented good agreement for surfactant rejection but fair fits for the flux and nickel rejections. If higher nickel rejection could be obtained at same values for surfactant rejection, some decline in the flux could be seen. It can be therefore said that the weakness of predictions resulted from nickel rejection being smaller than anticipated. This result puts forward the idea that the required amounts of PAC and surfactant per unit nickel to be removed were not available for high nickel concentrations of 300 mg/L in the feed. In other words, the amounts of PAC and surfactant as 0.1 g/L and 3 CMC were not sufficient for the treatability of wastewaters including high metal concentration. On the other hand, a nickel rejection could be obtained as high as 96.3% using 0.1 g PAC/L and 3 CMC, by run 3 from the designed experiments. This reveals that the hybrid process would be applied for the treatment of real wastewaters. But, it is taken into account that the operation of the process using higher PAC amounts would not be economical and feasible. Because direct adsorption of metal ions on PAC is too slow, and a considerable increase was not observed from 30 to 120 min in the validation experiments while surfactant rejection did not change. 4.2. Evaluation of Hybrid Process Performance for Nickel Separation. The results obtained for nickel removal were deeply compared to those obtained for CrO42- and Cu2+ in terms of evaluating the technological applicability of the process to real wastewaters.37,51 The literature lacks comparative evaluations about this subject, apart from that presented in Table 7. As seen from Table 7, SE-PAC/MF hybrid process could be more effectively operated in the removal of metal ions from

910 Ind. Eng. Chem. Res., Vol. 48, No. 2, 2009

Figure 4. Experimental and regression model results obtained for validation experiments. Table 7. Performance Evaluation of the SE-PAC/MF Hybrid Process in Heavy Metal Removal metal content type 2-

[CrO4 ]a

conc (mg/L) 23.2 46.4 92.8

2+

[Cu ]b

28.7 57.4 114.8

2+

[Ni ]c

10.0

experimental conditions system components

process

surfactant: cetyl trimethyl ammonium bromide (CTAB), adsorbent: PAC (BET surface area of 465 m2/g), membrane: cellulose acetate of 0.2 µm (effective surface area of 28 cm2)

2 g PAC/L and 5 mM (∼5.4 CMC) CTAB at temperature of 30 °C, cross-flow velocity of 1.18 m/s and transmembrane pressure of 150 kPa for 60 min process time and 60 min recycling time 2 g PAC/L and 5 mM (2.5 CMC) LABS at temperature of 30 °C, cross-flow velocity of 1.18 m/s and transmembrane pressure of 150 kPa for 60 min process time and 60 min recycling time 0.1 g PAC/L and 1.08 mM (3 CMC) HDSA at temperature of 20 °C, cross-flow velocity of 0.6 m/s and transmembrane pressure of 100 kPa for 30 min process time without recycling 0.1 g PAC/L and 1.08 mM (3 CMC) HDSA at temperature of 20 °C, cross-flow velocity of 0.6 m/s and transmembrane pressure of 300 kPa for 30 min process time without recycling 0.1 g PAC/L and 1.08 mM (3 CMC) HDSA at temperature of 20 °C, cross-flow velocity of 0.6 m/s and transmembrane pressure of 300 kPa for 120 min process time without recycling

surfactant: linear alkyl benzene sulfonate (LABS), adsorbent: PAC (BET surface area of 465 m2/g), membrane: cellulose acetate of 0.2 µm (effective surface area of 28 cm2) surfactant: 1-hexadecane sulfonic acid sodium salt (HDSA), adsorbent: PAC as C9157 (BET surface area of 1000 m2/g), membrane: cellulose nitrate of 0.45 µm (effective surface area of 33.17 cm2)

300.0

300.0

a

performance results surfactant/metal (mM/mM)

PAC/surfactant/metal (w/w/w)

RM (%)

RS (%)

J* (m3/m2 · h)

25.00

42.74/38.88/1.00

90.2

78.5

0.237

12.50

21.37/19.44/1.00

89.9

86.0

0.229

6.25

10.68/9.72/1.00

88.0

92.5

0.193

25.00

69.68/56.44/1.00

98.2

76.7

0.082

12.50

34.84/28.22/1.00

96.8

75.5

0.062

6.25

17.42/14.11/1.00

92.4

73.3

0.050

31.40

10.00/35.48/1.00

96.3

98.7

1.194

1.05

0.33/1.18/1.00

61.7

99.9

3.690

1.05

0.33/1.18/1.00

64.7

99.9

2.996

Basar et al.37 b Basar.51 c This study.

water, in the case of nickel compared to CrO42- and Cu2+. Rather high performance for both rejections and the flux was obtained for the feed metal concentration of 10 mg/L in water.

The applied experimental approach provided to reach to this result by means of more detailed investigation of the influences of all variables on process performance. The process was more

Ind. Eng. Chem. Res., Vol. 48, No. 2, 2009 911

economically executed with less consumption of PAC and surfactant under the lower velocity and transmembrane pressure. As concluding remarks, it was understood that the process can be successfully employed for advanced treatment of metalcontaminated waters. However, its direct implementation necessitates rather large PAC and surfactant consumptions due to high metal content in these wastewaters. The recovery of metal and surfactant adsorbed on PAC will promote more economical and feasible application of the SE-PAC/MF hybrid process to the treatment of wastewaters including heavy metal pollution. Besides, when compared with nanofiltration or reverse osmosis, widely used in heavy metal removal from waters, it can be said that the hybrid process might be most probably operated in a rather economical continuity due to the lower energy requirements and lower flux deteriorations. However, more research needs to be done to provide a complete economic evaluation by cost-benefit analysis of this subject. 5. Conclusions This study deals with the removal of nickel ions from aqueous solution using the SE-PAC/MF hybrid process, and it is focused on the investigation of the influences of process variables on process performance. The results may be summarized as follows: 1. While surfactant rejection of about 100% could be obtained for feed concentrations of both 10 and 300 mg Ni/L in the hybrid process, nickel removal could be achieved with a high efficiency of 96.3% only for a feed concentration of 10 mg Ni/L, at the end of 30 min of filtration time without recycling the feed solution before the continuous filtration in the range of the selected levels of the process variables. 2. The performance was more affected by the feed solution properties than operational parameters of the hybrid system. The nickel concentration and PAC amount in the feed were established as the most influential variables for nickel rejection, surfactant rejection, and permeate flux, respectively. The feed concentrations of PAC, surfactant, metal, and H+ were established as the parameters significantly influencing the process performance in addition to transmembrane pressure and temperature as operational parameters. At the end of the ANOVA analyses, the total influences of these parameters on process performance were determined to be approximately 93-98%. 3. The most critical parameter for process performance was established as the mass ratio of PAC to surfactant (PAC/ surfactant) per unit mass of metal removed. Because increasing the PAC amount caused serious losses in the flux, less consumption of surfactant should be intended that enables possibly less PAC use for a desirable metal removal. Moreover, PAC could be replaced with low cost adsorbents which lead to the operation of the process more economically. 4. By means of the application of a design of experiments approach in the study, high nickel removal could be obtained with less PAC and surfactant consumptions and lower energy requirements in shorter operation time, when compared to the results corresponding to CrO42- and Cu2+ studies. 5. It can be anticipated that the hybrid process will be more advantageous than high pressure driven membrane processes with respect to the removal of heavy metals from waters, due to lower energy requirements and lower flux deteriorations in the operation. However, it should be noted that direct implementation of the hybrid process to wastewaters having high metal content will require rather large PAC and surfactant consumptions for the purpose of removal of metal and surfactant at high levels as an important factor affecting the economical applicability of the process. Hence, in order to render the process

rather applicable or economical, the process development task must be further focused on both desorption of surfactant and metal ions into the solution from the treated PAC and recovery of metal and surfactant from the solution using chemical precipitation or other appropriate techniques. Acknowledgment This study was financially supported by the Gebze Institute of Technology Research Fund (Project no. 02-B-03-03-02) and the Istanbul Technical University-Institute of Science & Technology (Project no. 30313). C.A. thanks The Scientific & Technological Research Council of Turkey for the Ph.D. scholarship. Nomenclature ANOVA ) analysis of variance CMC ) critical micelle concentration CA ) PAC concentration in feed solution (g/L) CM ) metal concentration in feed solution (mg/L) CS ) surfactant concentration in feed solution (mM) DF ) degree of freedom DFXj ) degree of freedom of the process variable with number j DoE ) Design of experiments ∆P ) transmembrane pressure (kPa) e ) experimental error F ) F statistics HDSA ) 1-hexadecane sulfonic acid sodium salt J* ) permeate flux at the end of the process time (m3/m2 · h) LF ) lack of fit L16 ) orthogonal array layout (29) MSS ) mean of sum of squares MSSe ) mean of sum of squares of the experimental error MSSXj ) mean of sum of squares of the process variable with number j n ) total experiment number at level i ni(Xj)i(Xj′) ) total experiment number for the selected combination of i(Xj)i(Xj′) N ) total experiment number in DoE matrix p ) total number of intrinsic error experiments P ) the probability value PC ) the percentage contribution PCXj ) the percentage contribution of the process variable with number j PS) pure sum PSXj ) pure sum of the process variable with number j RM ) metal rejection (%) RS ) surfactant rejection (%) SE-PAC/MF ) surfactant-enhanced powdered activated carbon/ microfiltration hybrid process SS ) sum of squares SSe ) sum of squares of the experimental error SSTA ) the total sum of squares of all variables SSXj ) sum of squares of the process variable with number j t ) process time (min) t′ ) recycling time (min) T ) temperature (°C) X ) process variable Xj ) process variable with number j XjXj′ ) interactive variable Y ) response parameter jYj ) arithmetic mean of all results Yp ) response value for experiment p Yr ) response value for experiment r

912 Ind. Eng. Chem. Res., Vol. 48, No. 2, 2009 (Yr)i(Xj)i(Xj′) ) response value for experiment r at the selected combination of i(Xj)i(Xj′) z0 ) regression model constant zj ) regression model coefficient for process variable zjj′ ) regression model coefficient for interactive variable Greek Symbols ∆ ) delta ν ) cross-flow velocity (m/s) Superscripts * ) steady state ) arithmetic mean Subscripts A ) all results F ) fit i ) variable levels in DoE matrix (i ) -1 or +1) i(Xj) ) different levels for the process variable with number j i(Xj′) ) different levels for the process variable with number j′ i(Xj)i(Xj′) ) various combinations of levels belonging to variables Xj and Xj′ j ) number of the process variable in DoE matrix (j ) 5, 6, 7, . . ., 13) j′ ) number of the second process variable in interactive variable M ) metal p ) total number of intrinsic error experiments r ) experimental run number depending on level i (r ) 1, 2, 3, . . ., n) S ) surfactant TA ) total of all results 16 ) design layout value

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ReceiVed for reView March 17, 2008 ReVised manuscript receiVed October 10, 2008 Accepted October 27, 2008 IE8004308