Modified Empty Bed Residence Time Model for Copper Removal

Article pubs.acs.org/IECR

Modified Empty Bed Residence Time Model for Copper Removal Anthony Ma,† Pejman Hadi,‡,§ John Barford,‡ Chi-Wai Hui,‡ and Gordon McKay*,‡,∥ †

Hong Kong Productivity Council, Tat Chee Avenue, Kowloon, Hong Kong SAR Chemical and Biomolecular Engineering Department, Hong Kong University of Science and Technology, Clear Water Bay Road, Hong Kong SAR § School of Energy and Environment, City University of Hong Kong, Tat Chee Avenue, Kowloon Tong, Hong Kong SAR ∥ College of Science, Engineering and Technology, Hamad bin Khalifa University, Doha, Qatar ‡

S Supporting Information *

ABSTRACT: The removal of copper ions from wastewater has been studied using ion exchange with an iminodiacetate resin. The effects of agitation time, pH value of the solution, and initial concentration of the copper solution have been evaluated. It has been found that the equilibrium is obtained after 72 h. Also, the capacity of the resin for the copper ions has been determined to be 2.2 mmol/g by measuring the equilibrium isotherm at a pH value of 5.0. When the pH value of the solution is lower than 3.2, the adsorption capacity of the resin is reduced drastically. It has been verified that the Langmuir-based isotherm models are the best-fit models for the equilibrium isotherm system. A series of fixed bed column runs have been performed to remove copper from solution and study the effects of solution flow rate, initial copper concentration, and resin particle size. The empty bed residence time model, EBRT, has been used to correlate the fixed bed pilot plant experimental results. The original form of the EBRT model was modified using a time dependent bed depth adsorption capacity to determine the bed exhaustion rate in order to improve the original model which utilized the equilibrium adsorption capacity. The modified model demonstrates that the EBRT can be used to more accurately model copper removal using the chelating ion exchange resin.

1. INTRODUCTION

Adsorption and ion exchange sorption are the most widely accepted treatment methods for metal ion removal at present.21−25 Efficient metal removal, ease of process, and cost effectiveness are several advantages of these techniques for wastewater treatment.9,26 Many different types of natural and synthetic ion exchangers, such as peat,27−30 bone char,31,32 chitosan,33−37 zeolites,38−41 clinoptilolite,42−45 dolomite,46−48 kaolinite,49−51 and hydroxyapatite,52−54 have been used for copper removal in equilibrium isotherms. Also, the kinetics of copper ion removal from wastewater have been extensively investigated.36,55−61 Although the removal of copper has been widely studied in batch systems, large scale continuous commercial systems using fixed bed sorbents/ion exchangers have only received limited attention. Moreover, in contrast to batch equilibrium and kinetic adsorption modeling, continuous fixed bed adsorption modeling has not been dealt with properly and requires focused attention. Furthermore, the empty bed residence time (EBRT) model is traditionally used to correlate the experimental breakthrough curve data. However, the full equilibrium assumption throughout the adsorption column leads to the incapability of this model to accurately correlate the whole range of data. Therefore, a modification is required where the fixed adsorption capacity term should be replaced by a rate dependent adsorption capacity.

Heavy metals are an inseparable part of almost every industrial activity. Copper is a common metal used in many metal industries and consumer products including mining, electroplating, batteries, hydrometallurgy, metal finishing, and microelectronics, and appears in metal industrial effluents.1−6 It is well-recognized that all heavy metals can accumulate in the human body after intake by different pathways, such as dermal contact, ingestion, or inhalation.6−8 Although a trace amount of copper, in the body, is essential to human biochemical processes and enzymatic reactions, the ingestion of excess quantities of this metal may cause morbid or mortal effects.9−11 Nowadays, numerous methods have been developed for treating metal ion waste solution. In chemical precipitation, metal ions react with hydroxide ions and form an insoluble metal precipitate at alkaline (high pH) condition;3,12−14 however, this process is sensitive to pH and composition changes and is incompatible with many soaps or surfactants.15 Evaporation may be used to produce a more concentrated solution which can sometimes be reused in various industries or in metal salt production. The evaporated water can be collected in an energy-intensive process and condensed for rinsing purposes.16,17 In membrane filtration processes, a cross-flow filtration method is used. Depending on the specific application, microfiltration, ultrafiltration, nanofiltration, reverse osmosis, or solvent extraction can be used.18−20 The filtration-based methods can provide a selective ion removal effect, which depends on the characteristics of the metal ion and the membrane used in the filtration. © 2014 American Chemical Society

Received: Revised: Accepted: Published: 13773

May 2, 2014 July 20, 2014 July 31, 2014 July 31, 2014 dx.doi.org/10.1021/ie501807c | Ind. Eng. Chem. Res. 2014, 53, 13773−13781

Industrial & Engineering Chemistry Research

Article

The present study investigates the ion exchange capacity and the rate of copper ion exchange of an iminodiacetate weak acid resin to remove copper ions from wastewater. The equilibrium isotherm has been determined, the effect of pH on the equilibrium sorption capacity has been determined, and a series of fixed bed sorption studies have been carried out to investigate the influence of process variables such as flow rate, resin particle size, and initial copper ion concentration. The empty bed residence time model has been modified and applied to fixed bed experimental data.

capacities were determined, and the time to reach equilibrium was obtained. 2.2.3. Equilibrium Sorption Isotherms. The methodology adopted was the same as for the contact time experiments except that different copper ion solution concentrations at a fixed pH value of 5 were used. The initial and final concentrations of the samples were determined by ICP-AES. Also, the final pH values were measured for all samples. The sorption capacity (qe) of the resin was calculated from the copper mass balance equation:

2. MATERIALS AND METHODOLOGY 2.1. Materials. 2.1.1. Chelating Ion Exchange Resin. The iminodiacetate chelating resin used was Suqing D401 produced by the Jiang Yin Organic Chemical Co., Beijing, China. The resins have the iminodiacetate functional group with disodium ionic form. The structure and physical characteristics of the chelating resins are shown in Figure S1 and Table S1, respectively, in the Supporting Information. The stability of metal−iminodiacetate complexes is characterized by their decomplexing pH (DpH) values. DpH indicates the concentration of hydrogen ions at which desorption of the metal ion from the resin is initiated. The maximum ion exchange capacity is generally reached when the pH of the metal solution is at least 2 units above the DpH, which is 1.0 for copper. 2.1.2. Metal Ion Solution. Copper(II) chloride (CuCl2· 2H2O, 99%) was supplied by Riedel-de Haën Chemicals. Stock copper ion solution with a concentration of 1000 ppm was prepared by dissolving a specific amount of the salt in ultrapure deionized water. Copper ion solutions with various concentrations were prepared by diluting the stock solution accordingly. 2.2. Methodology. 2.2.1. Pretreatment of Resins. In order to ensure consistent resin condition, the resins were treated as follows. To pretreat the resins, each resin sample was first immersed in 8% HCl (the amount of HCl would be based on a dosage of 180 g of HCl/L of wet resin) for 45 min with stirring. The resin was then rinsed with deionized water to remove the residual HCl. Subsequently, the resin was immersed in 4% NaOH for another 45 min. To condition the resin to the hydrogen−sodium form (the actual resin Na content of resin was later measured to be 3.60 mequiv/g), a dosage of 41.2 g of NaOH/L of wet resin was used, representing approximately 80% sodium form. Finally, the resin was rinsed with deionized water to remove the residual NaOH. After that, the resin was dried in an oven at 110 °C for more than 48 h. Finally, the resins were allowed to cool in a desiccator and sieved to within the range of 1000−450 μm. 2.2.2. Contact Time for Sorption Isotherm Studies. The equilibrium time for the sorption isotherm must be established before conducting any other experiments. The highest concentration of Cu ion solution, which was to be used in the equilibrium sorption isotherms, was used in these experiments. A fixed mass of 0.1000 g of resin was added to 50 mL of copper metal ion solution, transferred into a screw cap jar, and shaken at a speed of 200 rpm at a controlled temperature (25 ± 2 °C). Each jar was removed from the shaker at a specified time, and the concentration of the copper ion solution was measured using an inductively coupled plasma atomic emission spectrophotometer (ICP-AES), PerkinElmer Model 300XL. The removal percentages and adsorption

mq0 + VC0 = mqe + VCe

(1)

V (C0 − Ce) m

(2)

q0 − qe = −

where m is the mass of resin (g), V is the volume of metal ion solution (L), C0 is the initial concentration of metal ion solution (mM), Ce is the equilibrium concentration of metal ion solution (mM), q0 is the initial metal ion concentration on the resin (mmol/g resin), and qe is the equilibrium metal ion concentration on the resin (mmol/g resin). When fresh resin is being used, q0 = 0. Therefore, we obtain qe =

V (C0 − Ce) m

(3)

In order to investigate the pH effect on the ion exchange capacity, 50 mL of 6 mM CuCl2 solution with the initial pH adjusted to various values was added into each of the test bottles containing 0.1 g of the resin. After continuous agitation until equilibrium, the final metal concentration was measured and the final pH value of each test bottle was recorded. The amount of metal ion sorbed, qe, was calculated based on eq 3, and the exchange capacity, qe, was plotted against the equilibrium pH. 2.2.4. Fixed Bed Column Studies. 2.2.4.1. Pilot Plant Column Setup. A pilot-scale ion exchange system was built to conduct the column runs. The schematic diagram of the setup is shown in Figure S2 in the Supporting Information. The ion exchange columns were made of Perspex tubes with an internal diameter of 2.08 cm and a height of 150 cm. It was assumed that there was no variation of the axial liquid velocity and solute concentration in the radial direction inside the column. Laminar flow of the aqueous solution and negligible surface roughness were also considered as approximate assumptions. The column diameter to the particle diameter ratio for this experiment ranged between 21 and 46, which was considered to be adequate such that the effects of channeling at the wall and random variations in the interstitial velocity within the bed became negligible. The top and bottom of the column were connected to 0.5 cm Perspex tubes which were the inlet and outlet of the column, respectively. A retaining sieve of 65 mesh size was fixed at the bottom of the column using a special adhesive. Ballotini balls of 2 mm diameter were placed at the column bottom before the resin was put in. Five sample points were located at 0, 20, 40, 60, and 80 cm from the column bottom along the straight height of the column. Each point was sealed using Suba-seals. Five syringes were used to collect samples from these sampling points for analysis. The copper ion solutions were prepared by dissolving a specific amount of copper chloride salt in deionized water in the mixing tank where the copper ion solution was continuously circulated by a centrifugal pump. The pH of the copper solutions would also be adjusted slightly by adding HCl 13774

dx.doi.org/10.1021/ie501807c | Ind. Eng. Chem. Res. 2014, 53, 13773−13781


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or NaOH to maintain a constant pH of about 5.0. According to the speciation diagrams for copper, under pH 5.0, the predominant species is always Cu2+. The copper ion solution was delivered to the holding tank where the solution was pumped to each column via a rotameter. The rotameters would be used to monitor the flow rates which would be maintained constant throughout the experiment. 2.2.4.2. Experimental Pilot Plant Column Procedures. A specific amount of pretreated dry resin (i.e., 145 g) would be packed into each column. Before packing, the dry resin was first soaked in deionized water for 24 h so that all the air could be expelled from the resin. Then the wet resin, together with the deionized water, was poured into the columns. During the column filling procedure, the resin should be kept submerged in deionized water. It was important to ensure that all air was expelled from the resin bed. If air pockets existed in the packed column, the operation would become unstable as channeling and air locking would occur. After packing the column, the column was tapped well to allow the resin to settle down. After filling in the resin, the column would be covered by the top flange and the metal ion solution in the holding tank would be pumped at a constant flow rate (120−220 mL/min) to the ion exchange columns. Samples were taken along the column at 30 min time intervals until the metal ion concentration of the effluent coming out of the column reached the breakthrough point.

m=

= QC0ts/qe

(x − α)(y − β) = γ

(7)

where x is the EBRT (s), y is the resin exhaustion (g/dm ), α is the minimum EBRT (s), β is the minimum resin exhaustion (g/dm3), and γ is the system constant (s·g/dm3). After rearranging, we obtain γ +β y= (8) x−α 3

resin bed volume volumetric liquid flow rate

= VB (dm 3)/Q (dm 3/min)

(6)

For an ion exchange column with a fixed set of operating conditions, the service time of the resin column at a specified breakthrough percentage can be obtained from the breakthrough curve analysis and the resin saturation exchange capacity can be obtained from the equilibrium isotherm, qe. The EBRT and resin exhaustion at various resin bed depths can thus be calculated from eqs 4, 5, and 6, respectively. Then, the resin exhaustion can be plotted against the EBRT values to generate an operating line representing that of an ion exchange column. When the operating conditions, such as flow rate, initial feed concentration, and resin particle sizes, are varied, different operating lines can be generated. The operating line approaches asymptotes on both axes. The minimum EBRT and the minimum resin exhaustion can be determined from the asymptotes of the operating lines. The minimum EBRT enables the calculation of the minimum volume of resin required to achieve the desired effluent quality at infinitely high resin exhaustion. This represents the lowest capital investment due to the smaller column required but higher operating cost because of faster saturation of the resin. On the other hand, the minimum resin exhaustion is achieved when the EBRT is so high that the resin is in equilibrium with the influent. This represents the lowest operating cost due to slower saturation of the resin but higher capital investment because of the larger column required. The trade-off between capital investment and operating cost for a specific ion exchange system can be determined from these operating lines when designing the ion exchange system. We have defined the intersection of the two asymptotes as the EBRT pinch point. The inversely proportional relationship between the resin exhaustion rate and the EBRT of the operating line can be represented by the following mathematical form:

3. THEORY 3.1. EBRT Model. The empty bed residence time (EBRT), sometimes called the empty bed contact time (EBCT), is used to determine the optimum resin usage in a fixed-bed column. McKay and Bino62 proposed that the capital and operating costs for a fixed-bed ion exchange (or adsorption) system were dependent on the EBRT and the resin exhaustion rate. The EBRT is defined as the time required for the liquid to fill the volume of the resin bed and is a direct function of the liquid flow rate and volume of the resin bed. It enables system designers to determine the resin column size required. EBRT =

total copper exchanged resin exchange capacity

(4)

The resin exhaustion is the amount of resin in the column exhausted per unit volume of liquid treated when breakthrough occurs:

From the experiments, a pair of parameters for the EBRT and resin exhaustion can be obtained for each resin bed depth under the same operating conditions. By means of minimizing the sum of the squares of the errors (SSE) between the predicted and experimental values, a best-fitting curve with optimum values of α, β, and γ can be found which is the operating line for the specific set of operating conditions. 3.2. Modified EBRT Model. The service time of the bed can be used to calculate the total volume of liquid treated by the resin bed before breakthrough and, in turn, to obtain the “resin exhaustion rate”, now redefined as 10% copper breakthrough, based on eqs 4−6. When the breakthrough curves are vertical, the bed exhaustion rate and the bed breakthrough rate are the same; however, when the breakthrough curves are S-shaped and a low percentage breakpoint is selected, then the two values will differ. The corresponding EBRT for each resin bed depth at a specific volumetric flow rate

resin exhaustion mass of resin exhausted at breakthrough = volume of liquid treated at breakthrough = m (g)/Qts (dm/min ·min) or (g/dm 3) (5)

where ts is the time to saturation. However, the terms in eq 5 are still relative, because the actual metal ion concentration and resin capacity values are missing. Since they are dependent variables, one of these parameters needs to be specified. The term m, the mass of resin at ts, can be defined as 13775



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mmol/g resin. This value for divalent copper requires a total of 4.4 mmol/g of monovalent ion sites. This behavior can be described by the fact that, at low pH values, the concentration of hydrogen ions in the solution is relatively high, resulting in a competition between the metal and hydrogen ions to occupy the adsorption sites. As the pH level increases, the concentration of the hydrogen ions in the solution decreases and metal ions have a better opportunity to take up the active sites.64 The initial and final pH values were measured at several different initial copper solution concentrations. The results are plotted in Figure 2, and a distinct trend can be seen. There is an

can be found using eq 5. By plotting the resin exhaustion against the EBRT, the operating lines at various system conditions can be generated and the constants, α, β, and γ of eq 7 can be calculated. The modeling results using the original equations were satisfactory in some cases, but required some modification. The assumption that the resin capacity is qe throughout the exchange column is an oversimplification, because it assumes that equilibrium is instantaneous. Due to mass transfer resistances and uneven flow patterns, full equilibrium will not be achieved until after several hours, if at all. The reaction kinetics/mass transfer diffusion processes slow down the achievement of saturation equilibrium. Consequently, in order to lower the SSE, we have incorporated a rate dependent copper exchange capacity, qt, instead of qe based on the square root of time, the classical diffusion control time dependency. The equation proposed by Ko et al.63 has been used as qt = qe(1 − exp(−at 0.5))

(9)

where a is an empirical rate parameter depending on the mass transfer resistances and qe is the adsorption capacity from the equilibrium isotherm. In previous work,63 the authors tested several timedependent adsorption capacities, qt, and found the root t dependence to be the most successful. This result is also verified by solutions by Crank’s mathematics of diffusion. The modified EBRT model can then be compared by checking the sum of the squares of the errors (SSE) between the experimental results and the predicted data values of the resin exhaustion rates using qe. Figure 2. pH variation against time for copper solutions (initial pH 5.5).

4. RESULTS AND DISCUSSION 4.1. Contact Time for Sorption Isotherm Studies. Based on the contact time studies, the equilibrium capacity was reached after 72 h. To ensure all isotherms achieved equilibrium, the agitation time was specified as 96 h in all further isotherm experiments. 4.2. Effect of Equilibrium pH on Ion Exchange Capacity. The effect of pH value of the metal solution on the metal uptake capacity is shown in Figure 1. It can be seen that the adsorption capacity is low at low equilibrium pH values, but as the pH value increases to 3.2 the copper exchange capacity rises to a maximum relatively constant value of 2.2

almost constant change in pH for each initial copper concentration of 5.5 pH units. In all the experiments, the final pH was below 6, thus ensuring no precipitation of copper hydroxide occurs. The continuous decrease in pH value for an initial copper solution concentration of up to 4.5 mmol/dm3 was due to the complete removal of all the copper ions in the solution and continually rising qe values. The steadily falling pH values are due to the liberation of hydrogen ions from the resin into the solution. For initial copper concentrations of 5.0 mmol/dm3 and above, the amount of copper adsorbed onto the resin has become constant at the maximum adsorption/ exchange capacity and therefore all the sodium and all the hydrogen ions in the resin have been released and the pH of the solution remains constant at a value of 3.8. 4.3. Equilibrium Isotherms. The metal adsorption process of the iminodiacetate chelating resin is actually an equilibrium system among Na+, H+, and metal ions. Hydrolysis and protonation will also take place during the metal ion exchange process. Thus, the solution pH will shift when the metal ions are exchanged by the resin. When the solution pH shifts up, metal hydroxide precipitation will occur, whereas, if the solution pH shifts downward, the protonation effect starts to become dominant. Both metal precipitation and protonation will interfere with the determination of the true ion exchange capacity of the resin, and in turn distort the equilibrium isotherm. In this regard, when batch tests are used to determine equilibrium isotherms, care should be taken to make sure that the equilibrium isotherms are not distorted by the pH shift. The equilibrium isotherm has been determined experimentally,

Figure 1. Effect of equilibrium pH on sorption of copper. 13776



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Table 1. Summary of Equilibrium Isotherm Constants of Cu2+ Ions for Different Isotherms isotherm

model

Langmuir

KLCe qe = 1 + aLCe

Freundlich

qe = KFCe nF

correlated constants

fitting error

KL = 2110, aL = 980

0.0178

KF = 2.270, nF = 0.0111

0.0305

nLF

Sips

qe =

KLFCe 1 + (aLFCe)nLF

KLF = 16 130, aLF = 70 920, nLF = 2.018

0.0142

Redlich−Peterson

qe =

KR Ce 1 + aR Ce nR

KR = 1315, aR = 5792, nR = 0.989

0.0152

Dubinin−Radushkevich

2⎞ ⎛ ⎡ ⎛ 1 ⎞⎤ ⎟ ⎥ qe = KDR exp⎜− nDR ⎢RT ln⎜1 + ⎟ ⎜ ⎢⎣ Ce ⎠⎥⎦ ⎟⎠ ⎝ ⎝

KDR = 2.2831, nDR = 0.0006

0.0229

Temkin

qe =

KT = 35.6, nt = 2.76 × 102

0.1151

Toth

qe =

Kt = 2.280, at = 0.0010, nt = 1.00

0.0178

RT ln(ntCe) KT

K tCe (at + Ce)1/ nt

and the data have been analyzed using several isotherm equations, namely, Langmuir,65 Freundlich,66 Langmuir− Freundlich or Sips,67 Redlich−Peterson,68 Dubinin−Radushkevich,69 Temkin,70 and Toth.71 The equilibrium isotherm constants obtained for the seven different models are presented in Table 1, and the equilibrium isotherm model fits using the two most commonly used models are shown in Figure 3.

Figure 4. Breakthrough curves for copper ion exchange at five sampling points (C0 = 1.5 mM, mean dp = 725 μm, flow rate = 180 mL/min).

heights in the fixed bed adsorption column for a flow rate of 180 mL/min and an initial copper concentration of 1.5 mmol/ dm3. The bed capacities at different percentage breakthrough values can be obtained by integrating the area under the curves at various breakthrough points. Figure 5 shows a series of breakthrough curves at different flow rates, and it can be seen that the breakthrough curve capacity is affected by the flow rate. Examination of the areas under the curves in Figure 5 demonstrates that the column loading, adsorbed copper capacity, decreases as the solution flow rate increases. To attain the full equilibrium saturation capacity, qe,m, of 2.2 mmol of Cu/g of resin under the isotherm conditions took 72 h in the batch equilibrium studies. On this basis, the capacity for “slow” adsorption systems will be affected by the kinetic rate parameters and/or the rate of diffusion across the boundary layer which will in turn influence the y-intercept. On one hand, an increase in fluid velocity can help reduce the external film reaction/diffusion resistance. On the other hand, a high flow rate reduces the residence time, which hinders the attainment of equilibrium. The original EBRT model was based on the saturation on the particle being extremely rapid or “instantaneous”, but from Figure 5, this is apparently not the case and there is a need to introduce a

Figure 3. Equilibrium isotherms for copper using the two most commonly used isotherm models.

The SSE values indicate that the two best fit isotherms are the Langmuir−Freundlich and the Langmuir models. Since there are only two different but distinct sites, namely, the sodium sites and the hydrogen sites, then this result is expected. For only one site, the Langmuir model should give the best fit, but there are two sites and the copper, being divalent, must find two active sites for its attachment, so a small deviation from the Langmuir model, as can readily be accommodated by the Langmuir−Freundlich model, provides an excellent best fit to the experimental data. The reaction mechanisms for the two reactions are shown in Figure S3 in the Supporting Information. 4.4. Fixed Bed Column Breakthrough Curves. Figure 4 shows a series of fixed bed breakthrough curves at five different 13777



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solution flow rate of 220 mL/min, showing that the modified model facilitates a better EBRT analysis. At the lowest solution flow rate of 120 mL/min, the original EBRT model fit is improved by only 4%. The rate parameter term, a, ranges from 0.70 to 1.39 h−1 with an average value of 1.0 h−1. However, there is a steadily increasing trend in the improvement of model fit with flow rate which suggests some small dependence on the Reynolds number. The particle Reynolds number ranges from 16.8 to 29.0 for flow rates of 120−220 mL/min, which is all in the laminar region. Therefore, the increasing change in the bed adsorption capacity with decreasing velocity implies a much longer contact time in the bed resulting in a higher adsorption capacity. Theoretically, the minimum resin exhaustion, β, will be independent of the volumetric flow rate as it reflects the maximum bed capacity achieved when the resin is in equilibrium with the metal solution at infinitely high EBRT. However, in practice, this does not happen with S-shaped breakthrough curves and when a breakpoint concentration, for example 0.10C0, has been selected. The value of the minimum EBRT, α, shows an increasing trend with the increase in volumetric flow rates. It is contradictory to the general concept that a higher flow rate shall have a lower minimum EBRT as predicted from eq 5. It accounts for the fact that the minimum amount of resin bed required to maintain the effluent solute concentration below the breakthrough limit is not the same for different flow rates. Table 2 shows that the constant, γ, decreases with increasing flow rate. Using the EBRT plot of 180 mL/min as the reference case, the change of γ is inversely proportional to the flow rate as follows:

Figure 5. Change of bed capacity for Cu ion exchange at different flow rates (C0 = 1.5 mM, mean dp = 725 μm, 10% breakthrough).

kinetic or mass transfer parameter to account for this “slow equilibrium” effect. The bed depth is a function of the saturation capacity of the adsorbent. As we demonstrated in eq 9, by replacing the equilibrium adsorption capacity, qe, by the time-dependent adsorption capacity, qt, the modified EBRT model analysis provides a much better fit in terms of lower SSE values as shown in Table 2. Table 2 shows that the qt values decrease with increasing flow rate or velocity. Therefore, the modified adsorption capacity, qt, decreases more significantly for large flow rates, because the residence time available for this ion exchange process to take place is less and, thus, a greater minimum of adsorbent is required to keep the effluent concentration below the 10% breakthrough limit. 4.5. EBRT Analysis with Different System Variables. 4.5.1. Change of Flow Rate. The values of the EBRT constants based on the modified exchange capacity term in eq 8 for the exchange of the copper ions at different flow rates are given in Table 2. The SSE values of the EBRT analysis using the modified qt equation are compared with those using the original qe values. It reveals that there will be a reduction of SSE up to 40% when the modified EBRT model is used at the highest

γ1 γ2

=

u2 u1

(10)

As indicated in eq 10, theoretically, EBRT plots with smaller γ values for higher flow rates would imply less retention time but lower resin exhaustion, or both. Therefore, for equal resin adsorption capacities at all flow rates, it will be the most costeffective when an ion exchange system is designed and operated at the highest possible flow rate provided that the desirable

Table 2. EBRT Analysis Based on Modified Model for Different Process Variables α (s)

β (g/dm3)

init concn (mmol/dm3) 1.0 1.5 2.2

12.3 14.7 14.6

0.035 0.052 0.070

90 1.05 2.2 125 1.20 2.2 182 1.42 2.2 fixed parameters: flow rate, 180

soln flow rate (mL/min) 120 140 180 220

13.0 14.7 14.7 19.1

0.050 0.050 0.050 0.050

204 0.70 2.2 2.11 113 173 0.85 2.2 1.83 18.2 125 1.30 2.2 1.58 31.5 106 1.39 2.2 1.32 11.7 fixed parameters: initial concentration, 1.5 mM; particle size, 725 μm

mean diam (μm) 850 650 525

22.5 18.4 10.0

0.03 0.03 0.03

variable param

γ (s g/dm3)

a (h−1)

qe,m (mmol/g)

145 0.76 135 1.00 130 1.25 fixed parameters: initial

qt (mmol/g)

SSE based on orig qe,m model

1.56 48.3 1.61 31.5 1.72 39.6 mL/min; particle size, 725 μm

2.2 1.54 2.2 1.80 2.2 1.98 concentration, 1.5 mM; flow rate, 13778

27.3 16.2 5.1 180 mL/min

SSE based on mod qt model

new SSE/old SSE

34.3 22.9 30.9

0.71 0.73 0.78

108 15.1 22.8 7.0

0.96 0.83 0.72 0.60

19.1 13.4 4.59

0.70 0.82 0.90



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effluent quality is still maintained. Figure 6 shows the EBRT plot for the copper ion exchange at various flow rates.

Figure 7. EBRT plot for copper ion exchange with different feed concentrations (flow rate = 180 mL/min, mean dp = 725 μm, 10% breakthrough). Figure 6. EBRT plot for Cu ion exchange with different volumetric flow rates (C0 = 1.5 mM, mean dp = 725 μm, 10% breakthrough).

γ1 γ2

β2

=

C0,2

(12)

Figure 8. EBRT plot for Cu ion exchange with different particle sizes (C0 = 1.5 mM, flow rate = 180 mL/min, 10% breakthrough).

While the minimum resin exhaustion, β, is constant regardless of change in particle size, the minimum EBRT, α, is found to increase with larger resin size. This may be due to the fact that large resin particles may have greater diffusion resistance due to the steric effect, so that some functional sites cannot be effectively used for ion exchange. Therefore, for larger resin particles, the minimum amount of resin bed required to maintain the effluent solute concentration below the breakthrough limit is higher, leading to a higher value of α. The smaller the particle diameter, the larger the specific surface area available for adsorption and therefore the rate parameter

C0,1 C0,2

C0,1

4.5.3. Change of Particle Size. EBRT analysis of the copper ion exchange for different particle sizes is shown in Table 2. The SSE when the modified EBRT model is used is 10−40% smaller than the SSE when the original EBRT model is used. Figure 8 illustrates the effect of particle size on the EBRT plot for copper ion exchange.

The ratio of the SSE values in Table 2 shows that the changes between the original EBRT and the modified EBRT are greatest at high flow rates as the SSE ratios change from 0.96 to 0.60 for the low flow rate of 120 mL/min to the highest flow rate of 220 mL/min. The ratio is almost entirely due to the changes in eq 9 and the difference between the theoretical “isotherm” equilibrium capacity and the integrated practical capacity adsorbed in the columns. The rate parameter, a, is found by minimizing the SSE for eq 9. 4.5.2. Change of Feed Concentration. Table 2 shows the EBRT analysis of the metal ion exchange for different feed concentrations. Comparing the SSE values resulted from using the original and modified EBRT models, significant improvements in the SSE values are observed when the modified EBRT model is used. The SSE ratios increase as the initial copper concentration increases, implying the copper loading on the resin is higher with a higher C0. This is to be expected. The rate parameter values average 1.22 h−1. The effect of feed concentration on the EBRT plot for copper ion exchange is shown in Figure 7. The minimum EBRT, α, shows an increasing trend with the increase in feed concentration which can be explained by the fact that the critical bed depth, and therefore qt, increases with the increase in feed concentration as shown in Table 2. For a fixed amount of copper solution, a higher copper ion concentration would require more resin to treat the effluent, representing a greater resin exhaustion rate as indicated in eq 6. Similarly, a higher feed concentration would result in a higher minimum resin exhaustion when the resin is in equilibrium with the metal solution at infinitely high EBRT. Therefore, the EBRT constant, β, is found to be directly proportional to the feed concentration:

β1

=

(11)

Another EBRT constant, γ, is also found to be proportional to the feed concentration: 13779



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(5) Hadi, P.; Barford, J.; McKay, G. Selective Toxic Metal Uptake Using an E-Waste-Based Novel sorbentSingle, Binary and Ternary Systems. J. Environ. Chem. Eng. 2014, 2, 332. (6) Xu, M.; Hadi, P.; Chen, G.; McKay, G. Removal of Cadmium Ions from Wastewater Using Innovative Electronic Waste-Derived Material. J. Hazard. Mater. 2014, 273, 118. (7) Tella, M.; Doelsch, E.; Letourmy, P.; Chataing, S.; Cuoq, F.; Bravin, M. N.; Saint Macary, H. Investigation of Potentially Toxic Heavy Metals in Different Organic Wastes Used to Fertilize Market Garden Crops. Waste Manage. 2013, 33, 184. (8) Marinussen, M. P.; van der Zee, S. E.; de Haan, F. A. Cu Accumulation in the Earthworm Dendrobaena Veneta in a Heavy Metal (Cu, Pb, Zn) Contaminated Site Compared to Cu Accumulation in Laboratory Experiments. Environ. Pollut. 1997, 96, 227. (9) Hadi, P.; Barford, J.; McKay, G. Toxic Heavy Metal Capture Using a Novel Electronic Waste-Based MaterialMechanism, Modeling and Comparison. Environ. Sci. Technol. 2013, 47, 8248. (10) Gong, J.-L.; Wang, X.-Y.; Zeng, G.-M.; Chen, L.; Deng, J.-H.; Zhang, X.-R.; Niu, Q.-Y. Copper (II) Removal by Pectin−iron Oxide Magnetic Nanocomposite Adsorbent. Chem. Eng. J. 2012, 185−186, 100. (11) Jellouli Ennigrou, D.; Ben Sik Ali, M.; Dhahbi, M. Copper and Zinc Removal from Aqueous Solutions by Polyacrylic Acid AssistedUltrafiltration. Desalination 2014, 343, 82. (12) Kurniawan, T. A.; Chan, G. Y. S.; Lo, W.-H.; Babel, S. Physicochemical Treatment Techniques for Wastewater Laden with Heavy Metals. Chem. Eng. J. 2006, 118, 83. (13) Chen, Q.; Luo, Z.; Hills, C.; Xue, G.; Tyrer, M. Precipitation of Heavy Metals from Wastewater Using Simulated Flue Gas: Sequent Additions of Fly Ash, Lime and Carbon Dioxide. Water Res. 2009, 43, 2605. (14) Mauchauffée, S.; Meux, E. Use of Sodium Decanoate for Selective Precipitation of Metals Contained in Industrial Wastewater. Chemosphere 2007, 69, 763. (15) Lazaridis, N. K.; Peleka, E. N.; Karapantsios, T. D.; Matis, K. A. Copper Removal from Effluents by Various Separation Techniques. Hydrometallurgy 2004, 74, 149. (16) Patterson, J. W.; Minear, R. A. Physical-Chemical Methods of Heavy Metal Removal. In Heavy Metals in the Aquatic Environment; Krenkel, P. A., Ed.; Pergamon Press: Oxford, England, 1975; pp 261− 276. (17) Singh, U.; Kaushal, R. K. Treatment of Wastewater with Low Cost AdsorbentA Review. VSRD Int. J. Technol. Non-Tech. Res. 2013, 4, 33. (18) Lazaridis, N.; Blöcher, C.; Dorda, J.; Matis, K. A Hybrid MF Process Based on Flotation. J. Membr. Sci. 2004, 228, 83. (19) Chaudhari, L. B.; Murthy, Z. V. P. Separation of Cd and Ni from Multicomponent Aqueous Solutions by Nanofiltration and Characterization of Membrane Using IT Model. J. Hazard. Mater. 2010, 180, 309. (20) Al-Rashdi, B. A. M.; Johnson, D. J.; Hilal, N. Removal of Heavy Metal Ions by Nanofiltration. Desalination 2013, 315, 2. (21) Hadi, P.; Barford, J.; McKay, G. Synergistic Effect in the Simultaneous Removal of Binary Cobalt−nickel Heavy Metals from Effluents by a Novel E-Waste-Derived Material. Chem. Eng. J. 2013, 228, 140. (22) Hadi, P.; Barford, J.; McKay, G. Electronic Waste as a New Precursor for Adsorbent Production. SIJ Trans. Ind., Financ. Bus. Manage. 2013, 1, 128. (23) Pehlivan, E.; Altun, T. The Study of Various Parameters Affecting the Ion Exchange of Cu2+, Zn2+, Ni2+, Cd2+, and Pb2+ from Aqueous Solution on Dowex 50W Synthetic Resin. J. Hazard. Mater. 2006, 134, 149. (24) Curkovic, L.; Stefanovic, S.; Filipan, T. Metal Ion Exchange by Natural and Modified Zeolites. Water Res. 1997, 31, 1379. (25) Sud, D.; Mahajan, G.; Kaur, M. P. Agricultural Waste Material as Potential Adsorbent for Sequestering Heavy Metal Ions from Aqueous Solutionsa Review. Bioresour. Technol. 2008, 99, 6017.

values increase with decreasing particle size. By a similar argument, the bed loading, qt, increases with decreasing resin particle diameter. Also, smaller resin particle size shows a reduced γ which implies less retention time (i.e., smaller system) or lower resin exhaustion rate (i.e., lower operating cost). Therefore, it is more cost-effective to use finer resin. Nevertheless, finer resin will create greater flow resistance across the resin bed, resulting in a greater pumping pressure or even undesirable resin breakage under excessive pressurean optimization is required to determine the most economical operating conditions.

5. CONCLUSION Adsorption tests were performed on a series of batch and fixed bed ion exchange systems to remove copper from effluent. The batch adsorption studies revealed that the equilibrium was reached in 72 h and the exchange capacity of the resin for copper at pH 5 was reported to be 2.2 mmol/g. The effects of effluent flow rate, initial copper concentration, and resin particle size in fixed bed systems were also investigated, and the results were analyzed using a new modified model. The modification of the empty bed residence time, EBRT, model was carried out by replacing the fixed adsorption capacity throughout the column with a rate dependent adsorption capacity term. This modification resulted in significant improvements in correlation between the experimental and predicted data.

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ASSOCIATED CONTENT

S Supporting Information *

Figure S1, structure of iminodiacetate chelating resin; Figure S2, schematic diagram of the experimental system; Figure S3, proposed reaction mechanisms for the removal of copper ions using ion exchange resin; Table S1, physical properties of Suqing D401. This material is available free of charge via the Internet at http://pubs.acs.org.

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AUTHOR INFORMATION

Corresponding Author

*Tel.: +852 23588412. Fax: +852 23580054. E-mail: [email protected]. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS The authors would like to thank the Hong Kong Research Grant Council for their support of this research.

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REFERENCES

(1) González-Muñoz, M. J.; Rodríguez, M. A.; Luque, S.; Á lvarez, J. R. Recovery of Heavy Metals from Metal Industry Waste Waters by Chemical Precipitation and Nanofiltration. Desalination 2006, 200, 742. (2) Kadirvelu, K.; Thamaraiselvi, K.; Namasivayam, C. Removal of Heavy Metals from Industrial Wastewaters by Adsorption onto Activated Carbon Prepared from an Agricultural Solid Waste. Bioresour. Technol. 2001, 76, 63. (3) Fu, F.; Wang, Q. Removal of Heavy Metal Ions from Wastewaters: A Review. J. Environ. Manage. 2011, 92, 407. (4) Bailey, S. E.; Olin, T. J.; Bricka, R. M.; Adrian, D. A Review of Potentially Low-Cost Sorbents for Heavy Metals. Water Res. 1999, 33, 2469. 13780



Article

(26) Hadi, P.; Barford, J.; McKay, G. Production of Novel Adsorbents From Waste Electric and Electronic Equipments. Presented at the AICHE Annual Meeting, 2012. (27) Ho, Y. S.; Wase, D. A. J.; Forster, C. F. The Adsorption of Divalent Copper Ions from Aqueous Solution by Sphagnum Moss Peat.pdf. Trans. Inst. Chem. Eng. 1994, 72, 185. (28) McKay, G.; Vong, B.; Porter, J. F. Isotherm Studies for the Sorption of Metal Ions on to Peat. Adsorpt. Sci. Technol. 1998, 16, 51. (29) Ho, Y. S.; McKay, G. Sorption of Copper(II) from Aqueous Solution by Peat. Water, Air, Soil Pollut. 2004, 158, 77. (30) Balasubramanian, R.; Perumal, S. V.; Vijayaraghavan, K. Equilibrium Isotherm Studies for the Multicomponent Adsorption of Lead, Zinc, and Cadmium onto Indonesian Peat. Ind. Eng. Chem. Res. 2009, 48, 2093. (31) Cheung, C. W.; Chan, C. K.; Porter, J. F.; McKay, G. Combined Diffusion Model for the Sorption of Cadmium, Copper, and Zinc Ions onto Bone Char. Environ. Sci. Technol. 2001, 35, 1511. (32) Cheung, C. W.; Porter, J. F.; McKay, G. Removal of Cu(II) and Zn(II) Ions by Sorption onto Bone Char Using Batch Agitation. Langmuir 2002, 18, 650. (33) Li, N.; Bai, R. A Novel Amine-Shielded Surface Cross-Linking of Chitosan Hydrogel Beads for Enhanced Metal Adsorption Performance. Ind. Eng. Chem. Res. 2005, 44, 6692. (34) Wu, F.-C.; Tseng, R.-L.; Juang, R.-S. Role of pH in Metal Adsorption from Aqueous Solutions Containing Chelating Agents on Chitosan. Ind. Eng. Chem. Res. 1999, 38, 270. (35) Findon, A.; McKay, G.; Blair, H. S. Transport Studies for the Sorption of Copper Ions by Chitosan. J. Environ. Sci. Health, Part A: Environ. Sci. Eng. 1993, 28, 173. (36) Chu, K. H. Removal of Copper from Aqueous Solution by Chitosan in Prawn Shell: Adsorption Equilibrium and Kinetics. J. Hazard. Mater. 2002, 90, 77. (37) Ng, J. C.; Cheung, W.; McKay, G. Equilibrium Studies of the Sorption of Cu(II) Ions onto Chitosan. J. Colloid Interface Sci. 2002, 255, 64. (38) Lam, K. F.; Chen, X.; McKay, G.; Yeung, K. L. Anion Effect on Cu2+ Adsorption on NH2-MCM-41. Ind. Eng. Chem. Res. 2008, 47, 9376. (39) Parida, K.; Mishra, K. G.; Dash, S. K. Adsorption of Copper(II) on NH2-MCM-41 and Its Application for Epoxidation of Styrene. Ind. Eng. Chem. Res. 2012, 51, 2235. (40) Keane, M. A. The Removal of Copper and Nickel from Aqueous Solution Using Y Zeolite Ion Exchangers. Colloids Surf., A 1998, 138, 11. (41) Qiu, W.; Zheng, Y. Removal of Lead, Copper, Nickel, Cobalt, and Zinc from Water by a Cancrinite-Type Zeolite Synthesized from Fly Ash. Chem. Eng. J. 2009, 145, 483. (42) Langella, A.; Pansini, M.; Cappelletti, P.; de Gennaro, B.; de Gennaro, M.; Colella, C. NH4+, Cu2+, Zn2+, Cd2+ and Pb2+ Exchange for Na+ in a Sedimentary Clinoptilolite, North Sardinia, Italy. Microporous Mesoporous Mater. 2000, 37, 337. (43) Doula, M.; Ioannou, A.; Dimirkou, A. Copper Adsorption and Si, Al, Ca, Mg, and Na Release from Clinoptilolite. J. Colloid Interface Sci. 2002, 245, 237. (44) Doula, M. K. Synthesis of a Clinoptilolite-Fe System with High Cu Sorption Capacity. Chemosphere 2007, 67, 731. (45) Doula, M. K. Simultaneous Removal of Cu, Mn and Zn from Drinking Water with the Use of Clinoptilolite and Its Fe-Modified Form. Water Res. 2009, 43, 3659. (46) Pehlivan, E.; Ozkan, A. M.; Dinç, S.; Parlayici, S. Adsorption of Cu2+ and Pb2+ Ion on Dolomite Powder. J. Hazard. Mater. 2009, 167, 1044. (47) Walker, G. M.; Connor, G.; Allen, S. J. Copper (II) Removal onto Dolomitic Sorbents. Chem. Eng. Res. Des. 2004, 82, 961. (48) Ivanets, A. I.; Kitikova, N. V.; Shashkova, I. L.; Oleksiienko, O. V.; Levchuk, I.; Sillanpäa,̈ M. Removal of Zn2+, Fe2+, Cu2+, Pb2+, Cd2+, Ni2+ and Co2+ Ions from Aqueous Solutions Using Modified Phosphate Dolomite. J. Environ. Chem. Eng. 2014, 2, 981.

(49) Yavuz, O.; Altunkaynak, Y.; Güzel, F. Removal of Copper, Nickel, Cobalt and Manganese from Aqueous Solution by Kaolinite. Water Res. 2003, 37, 948. (50) Doula, M.; Ioannou, A.; Dimirkou, A. Thermodynamics of Copper Adsorption-Desorption by Ca-Kaolinite. Adsorption 2000, 6, 325. (51) Alkan, M.; Kalay, B.; Doğan, M.; Demirbaş, O. Removal of Copper Ions from Aqueous Solutions by Kaolinite and Batch Design. J. Hazard. Mater. 2008, 153, 867. (52) Šljivić Ivanović, M.; Smičiklas, I.; Pejanović, S. Analysis and Comparison of Mass Transfer Phenomena Related to Cu2+ Sorption by Hydroxyapatite and Zeolite. Chem. Eng. J. 2013, 223, 833. (53) Corami, A.; Mignardi, S.; Ferrini, V. Copper and Zinc Decontamination from Single- and Binary-Metal Solutions Using Hydroxyapatite. J. Hazard. Mater. 2007, 146, 164. (54) Smiciklas, I.; Onjia, A.; Raicević, S.; Janaćković, D.; Mitrić, M. Factors Influencing the Removal of Divalent Cations by Hydroxyapatite. J. Hazard. Mater. 2008, 152, 876. (55) Cheung, W.; Ng, J.; McKay, G. Kinetic Analysis of the Sorption of Copper(II) Ions on Chitosan. J. Chem. Technol. Biotechnol. 2003, 78, 562. (56) Ho, Y. S.; McKay, G. Pseudo-Second Order Model for Sorption Processes. Process Biochem. 1999, 34, 451. (57) Ho, Y. S.; McKay, G. Application of Kinetic Models to the Sorption of Copper (II) on to Peat. Adsorpt. Sci. Technol. 2002, 20, 797. (58) Cheung, C. W.; Porter, J. F.; McKay, G. Sorption Kinetics for the Removal of Copper and Zinc from Effluents Using Bone Char. Sep. Purif. Technol. 2000, 19, 55. (59) Biškup, B.; Subotić, B. Kinetic Analysis of the Exchange Processes between Sodium Ions from Zeolite A and Cadmium, Copper and Nickel Ions from Solutions. Sep. Purif. Technol. 2004, 37, 17. (60) Bhattacharyya, K. G.; Sen Gupta, S. Influence of Acid Activation on Adsorption of Ni(II) and Cu(II) on Kaolinite and Montmorillonite: Kinetic and Thermodynamic Study. Chem. Eng. J. 2008, 136, 1. (61) Boyd, G. E.; Adamson, A. W.; Myers, L. S. The Exchange Adsorption of Ions from Aqueous Solutions by Organic Zeolites. II. Kinetics. J. Am. Chem. Soc. 1947, 69, 2836. (62) McKay, G.; Bino, M. J. Fixed Bed Adsorption for the Removal of Pollutants from Water. Environ. Pollut. 1990, 66, 33. (63) Ko, D. C.; Porter, J. F.; McKay, G. Film-Pore Diffusion Model for the Fixed-Bed Sorption of Copper and Cadmium Ions onto Bone Char. Water Res. 2001, 35, 3876. (64) Hadi, P.; Gao, P.; Barford, J. P.; McKay, G. Novel Application of the Nonmetallic Fraction of the Recycled Printed Circuit Boards as a Toxic Heavy Metal Adsorbent. J. Hazard. Mater. 2013, 252−253, 166. (65) Langmuir, I. The Constitution and Fundamental Properties of Solids and Liquids. Part I. Solids. J. Am. Chem. Soc. 1916, 38, 2221. (66) Freundlich, H. Over the Adsorption in Solution. Z. Phys. Chem. A 1906, 57, 385. (67) Sips, R. On the Structure of a Catalyst Surface. J. Chem. Phys. 1948, 16, 490. (68) Redlich, O.; Peterson, D. L. A Useful Adsorption Isotherm. J. Phys. Chem. 1959, 63, 1024. (69) Dubinin, M. M.; Radushkevich, L. V. Equation of the Characteristic Curve of Activated Charcoal. Proc. Acad. Sci., Phys. Chem. Sect., USSR 1947, 331−333. (70) Temkin, M. I.; Pyzhev, V. Kinetics of Ammonia Synthesis on Promoted Iron Catalyst. Acta Physicochim. URSS 1940, 12, 327. (71) Toth, J. State Equations of the Solid Gas Interface Layer. Acta Chim. Acad. Sci. Hung. 1971, 69, 311.

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Modified Empty Bed Residence Time Model for Copper Removal

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