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Modeling for predicting copper ion removal from aqueous solution by the fluidized adsorption based on dimensional analysis
Accepted Manuscript Modeling for predicting copper ion removal from aqueous solution by the fluidized adsorption based on dimensional analysis Zhaolin Du, Huizhe Cao, Guodong Liu, Tong Zheng, Peng Wang, Jin Quan PII: DOI: Reference:
S1383-5866(17)33778-4 https://doi.org/10.1016/j.seppur.2018.06.006 SEPPUR 14660
To appear in:
Separation and Purification Technology
Received Date: Revised Date: Accepted Date:
18 November 2017 22 May 2018 2 June 2018
Please cite this article as: Z. Du, H. Cao, G. Liu, T. Zheng, P. Wang, J. Quan, Modeling for predicting copper ion removal from aqueous solution by the fluidized adsorption based on dimensional analysis, Separation and Purification Technology (2018), doi: https://doi.org/10.1016/j.seppur.2018.06.006
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Modeling for predicting copper ion removal from aqueous solution by the fluidized adsorption based on dimensional analysis Zhaolin Dua,b, Huizhe Caoc, Guodong Liue, Tong Zhenga,*, Peng Wanga,d, Jin Quanf
a. School of Environment, Harbin Institute of Technology, Harbin 150090, People’s Republic of China b. Agro-Environmental Protection Institute, Ministry of Agriculture, Tianjin 300191, People’s Republic of China c. School of Architecture, Harbin Institute of Technology, Harbin 150006, People’s Republic of China d. State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology, Harbin 150090, People’s Republic of China e. School of Energy Science and Engineering, Harbin Institute of Technology, Harbin 150001, People’s Republic of China f. State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research (IWHR)
*Corresponding author, Tong Zheng: E-mail: [email protected];
Tel: +86 18904513531; Fax: 86-451-86266051
Abstract In this study, we developed a predictive model for quantitatively assessing the copper ion (Cu(II)) removal performance of 001*7 resin in a liquid–solid fluidized bed reactor. The model was developed using the dimensional analysis approach based on dynamic experimental data. Experiments were conducted to evaluate the effects of operational variables (adsorbent dosage, initial solution concentration, and superficial liquid velocity) on the Cu(II)removal performance. The model predictions were in good agreement with the experimental data, thereby indicating that the model could accurately describe the removal of Cu(II)by fluidized adsorption. A model that obtains better predictions could help to optimize the operational variables in implementations of the fluidized adsorption technique for wastewater treatment. The model showed that increasing the 001*7 resin dosage and decreasing the superficial liquid velocity could achieve a higher Cu(II)uptake capacity. In order to verify the theoretical rationality of the model, we clarified the effect of the superficial liquid velocity on the Cu(II)removal. A larger superficial liquid velocity yielded a higher Reynolds number which could enhanced the fluidized adsorption, but led to a higher void fraction which could weaken the adsorption. Thus, the effect of the superficial liquid velocity on the Cu(II) removal was modeled as a combination of the Reynolds number and void fraction.
Key words: Fluidized adsorption; Dimensional analysis; Mathematical model; Copper; Wastewater treatment.
1.
Introduction Copper ions are abundant in nature and they are applied widely in industries such
as mining, electroplating, and metal finishing [1-3]. If the copper pollutants derived from these industries are discharged into watersheds without timely and effective treatment, great environmental damage and economic losses may occur due to the high toxicity of copper[4]. Moreover, copper and its compounds are not readily degraded in water, and they also tend to accumulate in the lungs and other vital organs of humans through the food chain [5, 6]. Therefore, it is essential to remove the Cu(II) from industrial wastewater effectively. Among the common techniques for Cu(II) removal, the adsorption method has the obvious advantage of direct removal from water without secondary pollution and it is easy to operate with low costs [7-12]. The liquid–solid fluidized bed and fixed bed are the two main operational forms of the adsorption method used for industrial wastewater treatment [13, 14]. Compared with the fixed bed reactor, the fluidized bed can provide a larger liquid–solid contact area to give a higher mass transfer capability [15]; as well as removing contaminants from water with a lower head loss in practical applications [16, 17]. Therefore, previous research has focused on the fluidized adsorption technique for Cu(II) removal from water. In order to better guide the implementation of the fluidized adsorption technique for wastewater treatment, it is necessary to determine the quantitative relationships between the fluidized adsorption performance and different operational variables (e.g., superficial liquid velocity and adsorbent dosage). These relationships are particularly
important for obtaining better predictions of the fluidized adsorption performance in practical applications and for understanding the adsorption behavior in liquid–solid fluidized systems. However, these relationships have generally been analyzed and described qualitatively in previous studies [18, 19]. The current fluidized adsorption models usually describe the process partially from one of three perspectives and they fail to consider the effects of all the operational variables [20, 21], although the fluidized adsorption process depends on the adsorption characteristics, hydrodynamics, and mass transfer. Thus, Veeraraghavan et al. [22] developed a model of the adsorption of phenol onto granular activated carbon in a liquid–solid fluidized bed by considering the effects of axial mixing in the solid and liquid phases, and diffusion resistance within the particle. Wang et al. [23] proposed a model of the adsorption of phenol that considers the external mass transfer with film-surface diffusion, surface adsorption equilibrium, and internal mass transfer. Recently, Rahaman et al. [16] used a model to study the influence of the Reynolds number and void fraction on the average mass transfer coefficient, but there was no further quantitative analysis of the fluidized adsorption performance. As mentioned above, most of these models are based on isothermal equations (e.g., Langmuir and Freundlich), as well as some kinetic models, and they can only provide appropriate descriptions of the fluidized adsorption process under specified operating conditions (e.g., the specified superficial liquid velocity). Therefore, they cannot adequately reflect the relationships between the fluidized adsorption performance and different operational variables. It is not always possible to determine the relationships between the fluidized
adsorption performance and operational variables based on theoretical considerations and calculations for a complex liquid–solid fluidized system. In fact, experimental results may be employed directly and clearly using the dimensional analysis method [24-26], which can help to determine the quantitative relationships between the fluidized adsorption performance and different operational variables. The aim of the present study was to develop a mathematical model based on dimensional analysis to predict the Cu(II) removal efficiency by fluidized adsorption under different operating conditions. In order to develop and validate this model, we conducted a series of continuous dynamic experiments with the Cu(II)/001*7 resin system in a liquid–solid fluidized bed reactor (LSFBR).
2.
Experimental section
2.1 Materials The 001*7 resin was purchased from Cangzhou Baoen Resin Company, China. Its cation exchange capacity was 4.5 mmol/g; the average particle diameter was about 0.90 mm; the average wet real density was about 1260 kg/m3. CuSO4·5H2O (analytical grade) was purchased from Tianjin Bodi Chemical Reagent Company. HCl solution (5 w%) was prepared to regenerate the 001*7 resin absorbed with Cu(II). 2.2 Experimental set-up A schematic of the LSFBR is illustrated in Fig. 1. The reactor was made of plexiglass with an internal diameter of 100 mm and a height of 1040 mm. The Cu(II)
solution (40 L) in the tank (1) was prepared by dissolving CuSO4·5H2O in running water. The 001*7 resin bed layer was fed through the upper opening in the reactor and then taken out through the material outlet (7) after adsorption saturation. The required liquid flow rate was determined by the dynamic volumetric method. Fig. 1 During operation of the submersible pump, water could flow at a determined rate from the tank (1) through a perforated plate distributor (5) to distribute water uniformly in the column. The water then flowed through the 001*7 resin bed layer (6), so the particles were fluidized and they could react with the copper ions in the solution. The water could reflux into the tank (1) and the copper ion concentration could be measured in the bed outlet at a fixed time. According to the calculation of Ergun equation [27], the minimum fluidization velocity (umf) was 0.97 cm/s. For the LSFBR, the terminal velocity (ut) was considered to be the free-fall velocity [28], and it was 5.60 cm/s by calculation. 2.3 Dynamic experiments To obtain experimental data for the dimensional analysis, the adsorption of the Cu(II) from aqueous solution on the 001*7 resin was performed in the LSFBR under different operating conditions of adsorption time (0~180 min), adsorbent dosage (0.1~0.5 g/L), initial solution concentration (10~150 mg/L) and superficial liquid velocity (1.18 cm/s~2.07 cm/s). Besides, the optimal pH of the solution to the 001*7 resin was about 6.0 [29]. To regenerate the 001*7 resin after adsorption saturation, it reacted with HCl
solution (5 w%) for 20 min. Then, it was filtered, rinsed with deionized water up to neutral pH, dried, and used in subsequent adsorption experiments. The regeneration experiment was repeated for four periods. 2.4 Analytical methods of the Cu(II) The concentration of the Cu(II) was measured by Inductively Coupled Plasma-Atomic Emission Spectroscopy (ICP-AES). The removal efficiency of the Cu(II) was calculated by:
R C0 Ct / C0 100%
(1)
The adsorption capacity of 001*7 resin was calculated by:
qt C0 Ct V / m
(2)
2.5 Dimensional analysis for fluidized adsorption The residual Cu(II) concentration in the solution at a specified time ( Ct ) was a function of t , u , m , C0 and V , which were considered for dimensional analysis based on the mass transfer and adsorption mechanism[30]. Therefore, their relationship can be expressed as the following equation.
Ct f t , u, C0 , m,V
(3)
The dimensions of t , u , C0 , m and V were [T]−1, [L][T]−1, [M][L]−3, [M] and [L]3, respectively. Length [L], mass [M], and time [T] were the three basic dimensions. Considering that the number of variables was five and the number of basic dimensions was three, all of the variables could be combined into three dimensionless variables based on the π theorem. Thus, we selected t , u , and C0 as
the basic variables, and the equations for the other two variables are as follows.
1 t1 u 1 C01 m
(4)
2 t2 u 2 C0 2V
(5)
According to the theory of similarity, the two equations given above can be changed into the following.
1
m C0 tu
2
(6)
3
V
tu
(7)
3
Then, Eq. (3) could be rewritten as follows. α
1
Ct 2
m C tu 3 0
V tu 3
(8)
Eq. (3) is a power function model for describing the quantitative relationships between the fluidized adsorption performance and different operational variables. The logarithmic form of Eq. (8) is:
V m ln Ct ln ln ln (9) C tu 3 tu 3 0 where the units of all the terms in Eq. (9) conform to the International System of Units.
3.
Results and discussion
3.1 Experimental analysis of Cu(II) removal in the LSFBR 3.1.1. Comparison of the Cu(II) removal efficiencies of some adsorbents In order to select highly efficient adsorbents of copper ions, we compared the copper removal efficiency of several adsorbents in batch adsorption experiments and based on previous research. The adsorption process parameters for the adsorbents are
shown in Table 1. Table 1 According to previous studies [31-35], 001*7 resin possesses a higher adsorption capacity for Cu(II) than other adsorbents under the same operating reaction conditions and its equilibrium time is relatively short. Thus, we selected 001*7 resin as the adsorbent for Cu(II) removal in the LSFBR. 3.1.2. Effect of the adsorption time The changes in the Cu(II) adsorption capacity during different time intervals under the specified operating conditions (001*7 resin dosage = 0.5 g/L, superficial liquid velocity = 1.78 cm/s, and initial Cu(II) concentration = 100 mg/L) are illustrated in Fig. 2. Fig. 2 Figure 2 shows that the adsorption capacity increased sharply during the original adsorption time of 60 min, and it then increased slowly and gradually reached the maximum (about 79.59 mg/g). The change in the removal efficiency exhibited a similar trend to the adsorption capacity, where it increased to about 40% at equilibrium. In fact, when the fluidization process started, the contact area between the resin and the copper ions increased gradually, which led to rapid reactions with the numerous active sites in the resin and the copper ions. At the same time, the concentration of copper ions was high and the difference in the concentration between the solid and liquid phases was large, thereby increasing the liquid–solid mass transfer rate and the reaction between the resin and the copper ions accelerated. Thus, the
adsorption capacity and removal efficiency increased rapidly. However, the number of active sites decreased and the difference in concentration became smaller with the adsorption time, and thus the copper ions absorbed in the resin tended to repel those in aqueous solution, which weakened the reaction until the adsorption equilibrium was reached. 3.1.3. Effect of the 001*7 resin dosage We also investigated the effect of the 001*7 resin dosage on the copper ion removal efficiency under the specified operating conditions (adsorption time = 120 min, superficial liquid velocity = 1.78 cm/s, and initial Cu(II) concentration = 100 mg/L) and the results are shown in Fig. 3. Fig. 3 Figure 3 shows that the Cu(II) removal efficiency increased sharply from 8.17% to 97.76% as the resin dosage increased from
, whereas it increased
only slightly to 99.08% when the resin dosage was 2.5 g/L. In addition, the adsorption capacity decreased from 80.90 mg/g to 39.68 mg/g because the solid mass flux was enhanced as the resin dosage increased, thereby providing a larger number of active sites for Cu(II) removal and a higher removal efficiency. However, considering the specific amount of copper ions in the solution, the amount of Cu(II) removed per unit mass decreased as the 001*7 resin dosage increased according to the equation for calculating the adsorption capacity (Eq. 2). These results demonstrate that further increasing the resin dosage could not significantly improve the rate of Cu(II) removal beyond a certain dosage. Using an excess resin dosage could increase the wastewater
treatment costs. Therefore, a balance should be reached between the adsorbent dosage and Cu(II) removal in practical wastewater treatment processes. 3.1.4. Effect of initial Cu(II) concentration We investigated the effect of the initial copper ion concentration on the adsorption capacity under specified operating conditions (adsorption time = 120 min, 001*7 resin dosage = 0.5 g/L, and superficial liquid velocity of 1.78 cm/s) and the results are shown in Fig. 4. Fig. 4 Figure 4 shows that the adsorption capacity increased rapidly as the initial copper ion concentration increased from 10 to 80 mg/L, but it remained constant (about 80 mg/g) above 100 mg/L. In addition, the removal efficiency decreased from 95.71% to 27.17%. The concentration gradient between the solution and the 001*7 resin surface became larger as the initial concentration increased, thereby accelerating the mass transfer coefficient and enhancing the adsorption process[36]. Hence, the adsorption capacity increased at the beginning. However, each specific resin dosage had a limited number of active sites. Thus, insufficient active sites were available to react with the copper ions as the initial concentration increased, so the residual copper concentration increased and the removal efficiency was lower. When the initial concentration exceeded 100 mg/L, it was difficult to increase the adsorption capacity of the 001*7 resin at a dosage of 0.5 g/L. Therefore, the fluidization adsorption method is more suitable for treating wastewater containing higher initial concentrations of copper ions.
3.1.5. Effect of superficial liquid velocity The changes in the Cu(II) adsorption capacity with time at different superficial liquid velocities (1.18, 1.78, and 2.07 cm/s) under specified operating conditions (adsorption time = 120 min, 001*7 resin dosage = 0.5 g/L, and initial Cu(II) concentration = 100 mg/L) are illustrated in Fig. 5. Fig. 5 Figure 5 shows that the adsorption capacity increased at the beginning, but then remained at the maximum with time at the specified superficial liquid velocity. The adsorption capacity decreased from 92.03 mg/g to 67.21 mg/g, and the removal efficiency decreased from 46.23% to 33.51% when the superficial liquid velocity increased from
The higher superficial liquid velocity could
lead to dramatic fluidization, thereby enhancing the collision frequency between the 001*7 resin and copper ions in the aqueous solution. Thus, the mass transfer coefficient was proportional to the superficial liquid velocity, which could enhance the adsorption performance [37]. However, it should be noted that increasing the superficial liquid velocity also increased the void fraction, which could reduce the contact area between the resin and the copper ions to further decrease the Cu(II) removal efficiency [20]. In addition, the higher Cu(II) removal at lower superficial liquid velocities was due to the increased contact time between the resin and copper ions [36, 38]. Hence, the superficial liquid velocity should be designed specifically for practical wastewater treatment processes. 3.1.6. Regeneration treatment of 001*7 resin
The experimental results showed that the Cu(II) adsorption capacity of the 001*7 resin decreased from 103.09 mg/g to 89.26 mg/g after four regeneration periods. This indicates that the recycled 001*7 resin still possessed an excellent capacity for Cu(II) uptake, which could reduce the consumption of the resin and economic costs. 3.2 Dimensional analysis results Based on multiple linear regression analysis, the parameters of Eq. (9) (Table 1) were obtained using the experimental data presented in Section 3.1. The final predictive model was as follows: 1.501
C Ct 2.102 0 M
tu
0.222
V 1.575
(10)
We conducted analysis of variance to verify the efficiency and accuracy of the proposed model, and the results are shown in Table 2. Table 2 Table 2 shows that the multiple correlation coefficient was larger than 0.9, which indicates that the term for Ct was highly correlated with the other terms for the independent variables. According to additional performance indicators (Multiple determination coefficient (R) and Standard error (SE)) in Table 2, the measured and predicted Cu(II) removal efficiency agreed very well. In addition, the regression results for the variables in Eq. (10) were significant. Thus, the proposed model described the quantitative relationships between the Cu(II) removal performance and different operational variables in the LSFBR fairly well.
Notably, the model could reveal the quantitative relationship between Cu(II) removal and operating parameters: the residual Cu(II) concentration was inversely proportional to the 001*7 resin dosage and superficial liquid velocity, while was proportional to the initial Cu(II) concentration, which agreed with the qualitative analysis results based on the experimental data presented in Section 3.1. Besides, the model, as a valuable tool, could be used to estimate the amount of 001*7 resin for Cu(II) removal from industrial wastewater by fluidized adsorption technique with minimal risk of product loss when other operating variables were determined. In particular, the model could describe the quantitative relationships between Cu(II) removal and the operating parameters, where the residual copper ion concentration was inversely proportional to the 001*7 resin dosage, and proportional to the initial Cu(II) concentration and superficial liquid velocity, which agreed with the qualitative analysis results based on the experimental data given in Section 3.1. In addition, the model is a valuable tool for estimating the amount of 001*7 resin required for removing Cu(II) from industrial wastewater using the fluidized adsorption technique with the minimal risk of product loss after the other operating variables have been determined. 3.3 Determination of the theoretical rationality of the predictive model It was also necessary to explore the effect of the superficial liquid velocity on the copper removal performance to determine the theoretical rationality of the predictive model. To simplify this study, plug flow of the liquid was assumed in the LSFBR. The whole adsorption process was divided into three phases: external diffusion,
internal diffusion, and reaction of the active sites on the inner surface. The third process was very rapid and it could not affect the adsorption rate [38]. Hence, the adsorption rate was controlled by the external or internal diffusion processes. The equation of external diffusion mass transfer rate was [38]: dq k f am Ct Ci dt
(11)
The equation of internal diffusion mass transfer rate was [38]: dq ks am qi qe dt
(12)
As the Ci and qi cannot be measured, the whole adsorption process was normally studied as the quasi-steady state [39]. The calculation formula of overall mass transfer rate was as follows： dq K F am C Ce K S am qe q dt m KS KF V
(13) (14)
The integral form of the equation (13) was as follows: m K F am t V q qe 1 e
(15)
The logarithmic form of the equation (15) was： ln qe q ln qe K F am
m t V
(16)
With the following initial condition: IC: t=0, q=0, C= C0. Where K F am
m was the adsorption rate constant K (s-1) V
The surface area per unit volume of 001*7 resin in the LSFBR was related to the 001*7 resin particle size and the void fraction. It can be obtained by the calculation of
the following equations [20]: aV
6 1 dp
(17)
am m V
(18)
aV
The void fraction can be calculated by solving Lewis-Bowerman equation with the following boundary condition [40]: u 2.00 2 gd p p l
0.215
(19)
BC: Re<2 u 0.29 0.43 1.95 0.71 1.14l 0.71 g d p ( p l )
0.337
(20)
BC: 2
0.43
(21)
BC: Re＞500 The calculation formula of Re was as follows: Re
d pu
(22)
The experimental results presented in Section 3.1.4 were fitted by the logarithmic form of Eq. (16) and the fitted curves are shown in Fig. 6. The hydraulic and mass transfer parameter values under different superficial liquid velocities shown in Table 3 were calculated using Eqs. (16)–(22). Fig. 6 Table 3 According to Fig. 6 and the performance indicator (R2 > 0.99) in Table 3, Eq. (16) provided good descriptions of the Cu(II) removal process in the LSFBR under
different superficial liquid velocities. Table 3 shows that the adsorption rate constant ( K ) decreased as the superficial liquid velocity increased, which was consistent with the experimental results in Section 3.1.4. However, the mass transfer coefficient ( K F ) was positively correlated with the superficial liquid velocity. This is because the bigger superficial liquid velocity led to a larger Re value, thereby indicating that the turbulence of the flow field in the LSFBR was more severe and the contact frequency of the 001*7 resin with the copper ions in the solution increased. Thus, the mass transfer coefficient became larger and the adsorption reaction was enhanced. However, at the same time, the increase in the superficial liquid velocity also caused an increase in the void fraction, which decreased the specific surface area of the 001*7 resin [37]; so the Cu(II) removal efficiency was reduced in the LSFBR, as also shown by Boskovic [41]. In fact, when the superficial liquid velocity increased from 1.18 cm/s to 2.07 cm/s, the mass transfer coefficient increased by 0.9 times, whereas the specific surface area decreased by 1.1 times according to the calculations. Therefore, the Cu(II) removal performance had a negative correlation with the superficial liquid velocity in the LSFBR in the present study.
4.
Conclusions In the present study, dimensional analysis was employed to determine the Cu(II)
removal performance as functions of the adsorbent dosage, initial solution concentration, and superficial liquid velocity based on dynamic adsorption experiments in an LSFBR. The dynamic adsorption capacity of 001*7 resin was 92.03
mg/g under the optimum reaction conditions comprising 001*7 resin dosage = 0.5 g/L, superficial liquid velocity = 1.18 cm/s, and initial Cu(II) concentration = 100 mg/L. The model predicted the fluidized adsorption performance under different operating conditions very well for the Cu(II)/001*7 resin system. The Cu(II) removal performance increased as the superficial liquid velocity decreased and as the 001*7 resin dosage increased. Furthermore, we explored the effect of the superficial liquid velocity on the Cu(II) removal performance based on the adsorption and mass transfer mechanism to verify the theoretical rationality of the model. We found that a larger superficial liquid velocity yielded a higher Reynolds number with enhanced fluidized adsorption, whereas a higher void fraction weakened the fluidized adsorption, and thus the effect of the superficial liquid velocity on Cu(II) removal was a combination of the Reynolds number and the void fraction. In conclusion, the model developed in this study could be a valuable tool for process development and optimization with minimal risk of product losses when removing Cu(II) from water by fluidized adsorption.
5.
ACKNOWLEDGMENTS This research was supported by National Water Pollution Control and
Management Technology major projects (2012ZX07205-005) and the Central Public Research Institute Basic Fund for Research and Development (2018-jbkyywf-dzl). This research was also supported by Open Project of State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology (NO.QAK201604).
Nomenclature Cu(II) Copper ion LSFBR Liquid–Solid Fluidized Bed Reactor
am Surface area per unit mass of 001*7 resin in the LSFBR (m2/g) aV Surface area per unit volume of 001*7 resin in the LSFBR (m2/m3) C0 Initial concentration of the Cu(II) in the solution (mg/L) Ce Equilibrium concentration of the Cu(II) in the solution (mg/L)
Ci Cu(II) concentration in the outer surface at the specified time (mg/L) Ct Concentration of the Cu(II) in the solution at the specified time (mg/L)
d s Average particle size of the 001*7 resin (m) g Acceleration of gravity (m/s2) k f Mass transfer coefficient in the external process (m/s)
k s Mass transfer coefficient in the internal diffusion process (g/(s·m2)) K F Overall mass transfer coefficient (its impetus on concentration difference) (m/s)
K S Overall mass transfer coefficient (its impetus on adsorption capacity difference)
(g/(s·m2)).
m Dry weight of the 001*7 resin (g) qe Equilibrium adsorption capacity corresponding to the Ce (mg/g) qi Adsorption capacity of 001*7 resin corresponding to the Ci (mg/g) qt Adsorption capacity of 001*7 resin corresponding to the Ct (mg/g) R Removal efficiency of the Cu(II) from aqueous solution (%)
t Adsorption time (s)
u Superficial liquid velocity (cm/s) V Volume of the solution in the tank (L)
Greek letters
Void fraction (-) l Density of water (kg/m3) p Average wet real density of 001*7 resin (kg/m3)
Dynamic viscosity of water (N·s/m2) Kinematic viscosity of water (m2/s)
Subscripts l Liquid phase p Particle phase
REFERENCES [1] A. Labidi, A.M. Salaberria, S.C.M. Fernandes, J. Labidi, M. Abderrabba, Adsorption of copper on chitin-based materials: Kinetic and thermodynamic studies, Journal of the Taiwan Institute of Chemical Engineers, 65 (2016) 140-148. [2] K.H. Chu, M.A. Hashim, Adsorption of copper(II) and EDTA‐chelated copper(II) onto granular activated carbons, Journal of Chemical Technology & Biotechnology, 75 (2015) 1054-1060. [3] J.P. Chen, S. Wu, Study on EDTA‐chelated copper adsorption by granular activated carbon, Journal of Chemical Technology & Biotechnology, 75 (2015) 791-797. [4] A. Bogusz, P. Oleszczuk, R. Dobrowolski, Application of laboratory prepared and commercially available biochars to adsorption of cadmium, copper and zinc ions from water, Bioresour. Technol. , 196 (2015) 540. [5] M.K. Ahmadi, M. Ghafari, J.D. Atkinson, B.A. Pfeifer, A Copper Removal Process for Water based upon Biosynthesis of Yersiniabactin, a Metal-binding Natural Product, Chem. Eng. J. , 306 (2016) 772-776. [6] S.H. Park, H.J. Cho, C. Ryu, Y.-K. Park, Removal of copper(II) in aqueous solution using pyrolytic biochars derived from red macroalga Porphyra tenera, J. Ind. Eng. Chem. , 36 (2016) 314-319. [7] F. Ciesielczyk, P. Bartczak, Ł. Klapiszewski, T. Jesionowski, Treatment of model and galvanic waste solutions of copper(II) ions using a lignin/inorganic oxide hybrid as an effective sorbent, J. Hazard. Mater. , 328 (2017) 150-159. [8] C.A. Papadimitriou, G. Krey, N. Stamatis, A. Kallaniotis, The use of waste mussel shells for the adsorption of dyes and heavy metals, Journal of Chemical Technology & Biotechnology, (2017). [9] C. Jeon, Removal of cesium ions from aqueous solutions using immobilized nickel hexacyanoferrate-sericite beads in the batch and continuous processes, Journal of Industrial & Engineering Chemistry, 40 (2016) 93-98. [10] M. Sajid, M.K. Nazal, Ihsanullah, N. Baig, A.M. Osman, Removal of heavy metals and organic pollutants from water using dendritic polymers based adsorbents: A critical review, Separation & Purification Technology, (2017). [11] Ihsanullah, A. Abbas, A.M. Al-Amer, T. Laoui, M.J. Al-Marri, M.S. Nasser, M. Khraisheh, M.A. Atieh, Heavy metal removal from aqueous solution by advanced carbon nanotubes: Critical review of adsorption applications, Separation & Purification Technology, 157 (2016) 141-161. [12] S.S. Rath, S. Singh, D.S. Rao, B.B. Nayak, B.K. Mishra, Adsorption of Heavy Metals on a Complex Al-Si-O Bearing Mineral System: Insights from Theory and Experiments, Separation & Purification Technology, 186 (2017). [13] S. Shanmuganathan, T.V. Nguyen, W.G. Shim, J. Kandasamy, A. Listowski, S. Vigneswaran, Effluent organic matter removal from reverse osmosis feed by granular activated carbon and purolite A502PS fluidized beds, J. Ind. Eng. Chem. , 20 (2014) 4499-4508. [14] C. Lee, J. Jung, R.R. Pawar, M. Kim, Lalhmunsiama, S.-M. Lee, Arsenate and phosphate removal from water using Fe-sericite composite beads in batch and fixed-bed systems, J. Ind. Eng. Chem. , 47 (2017) 375-383. [15] A. Gaikwad, S. Kale, A. Lali, Modeling of counter-current adsorption in continuous liquid-solid circulating fluidized bed adsorption chromatography, Chem. Eng. Sci. , 63 (2008) 1062-1071. [16] M.S. Rahaman, D.S. Mavinic, A. Meikleham, N. Ellis, Modeling phosphorus removal and recovery from anaerobic digester supernatant through struvite crystallization in a fluidized bed reactor,
Water Res. , 51 (2014) 1-10. [17] D. Wang, E. McLaughlin, R. Pfeffer, Y.S. Lin, Aqueous phase adsorption of toluene in a packed and fluidized bed of hydrophobic aerogels, Chem. Eng. J. , 168 (2011) 1201-1208. [18] C.I. Lee, W.F. Yang, C.S. Chiou, Utilization of water clarifier sludge for copper removal in a liquid fluidized-bed reactor, J. Hazard. Mater. , 129 (2006) 58. [19] L. Li, C. Yu, J. Bai, Q. Wang, Z. Luo, Heavy metal characterization of circulating fluidized bed derived biomass ash, J. Hazard. Mater. , 233–234 (2012) 41-47. [20] D.V. Kalaga, A. Dhar, S.V. Dalvi, J.B. Joshi, Particle-liquid mass transfer in solid–liquid fluidized beds, Chem. Eng. J. , 245 (2014) 323-341. [21] J.J. Derksen, Simulations of solid–liquid mass transfer in fixed and fluidized beds, Chem. Eng. J. , 255 (2014) 233-244. [22] S. Veeraraghavan, L.T. Fan, A.P. Mathews, Modeling adsorption in liquid—solid fluidized beds, Chem. Eng. Sci. , 44 (1989) 2333-2345. [23] R.C. Wang, C.C. Kuo, C.C. Shyu, Adsorption of phenols onto granular activated carbon in a liquid-solid fluidized bed, J. Chem. Technol. Biotechnol. , 68 (1997) 187-194. [24] A. Izady, O. Abdalla, M. Sadeghi, M. Majidi, A. Karimi, M. Chen, A Novel Approach to Modeling Wastewater Evaporation Based on Dimensional Analysis, Water Resour. Manage 30 (2016) 1-14. [25] S. Gillot, S. Capela-Marsal, M. Roustan, A. Héduit, Predicting oxygen transfer of fine bubble diffused aeration systems--model issued from dimensional analysis, Water Res. , 39 (2005) 1379. [26] R. Hassan, K. Loubiere, J. Legrand, G. Delaplace, A consistent dimensional analysis of gas–liquid mass transfer in an aerated stirred tank containing purely viscous fluids with shear-thinning properties, Chem. Eng. J. , 184 (2012) 42-56. [27] V.J. Inglezakis∗, M. Loizidou‡, Hydrodynamic Studies on Zeolite Fluidized Beds, Int. J. Chem. Reactor Eng. , 8 (2010) 47-54. [28] R.C. Wang, S.C. Chang, Adsorption/desorption of phenols onto granular activated carbon in a liquid–solid fluidized bed, Journal of Chemical Technology & Biotechnology, 74 (2015) 647-654. [29] J. Jian-Hong, The treatment of heavy metal wastewater by ion-exchange resin, Tianjin Chemical Industry, 25 (2011) 60-62. [30] Q.D. Lan, J.X. Zhu, A.S. Bassi, A. Margaritis, Y. Zheng, G.E. Rowe, Continuous protein recovery using a liquid-solid circulating fluidized bed ion exchange system: Modelling and experimental studies, Can. J. Chem. Eng. , 78 (2000) 858-866. [31] E. Demirbas, N. Dizge, M.T. Sulak, Adsorption kinetics and equilibrium of copper from aqueous solutions using hazelnut shell activated carbon, Chemical Engineering Journal, 148 (2009) 480-487. [32] K.Z. Elwakeel, A.A. El-Bindary, E.Y. Kouta, Retention of copper, cadmium and lead from water by Na-Y-Zeolite confined in methyl methacrylate shell, Journal of Environmental Chemical Engineering, 5 (2017). [33] S.O. Yong, J.E. Yang, Y.S. Zhang, S.J. Kim, D.Y. Chung, Heavy metal adsorption by a formulated zeolite-Portland cement mixture, Journal of Hazardous Materials, 147 (2007) 91. [34] N.S. Rajurkar, A.N. Gokarn, K. Dimya, Adsorption of Chromium(III), Nickel(II), and Copper(II) from Aqueous Solution by Activated Alumina, Clean – Soil Air Water, 39 (2011) 767–773. [35] H. Merrikhpour, M. Jalali, Comparative and competitive adsorption of cadmium, copper, nickel, and lead ions by Iranian natural zeolite, Clean Technologies & Environmental Policy, 15 (2013) 303-316. [36] D.V. Kalaga, A. Dhar, S.V. Dalvi, J.B. Joshi, Particle-liquid mass transfer in solid–liquid
fluidized beds, Chem. Eng. J. , 245 (2014) 323–341. [37] S.L. Dashtban Kenari, B. Barbeau, Pyrolucite fluidized-bed reactor (PFBR): A robust and compact process for removing manganese from groundwater, Water Res. , 49 (2014) 475-483. [38] E. Rezaei, O. Mowla, D. Mowla, Aqueous phase adsorption of organic compounds on activated carbon in fluidized bed, Desalination and Water Treatment, 28 (2011) 35-41. [39] N. Boskovic-Vragolovic, R. Garic-Grulovic, Z. Grbavcic, R. Pjanovic, Mass transfer and fluid flow visualization in packed and fluidized beds by the adsorption method, Russ. J. Phys. Chem. A 83 (2009) 1550-1553. [40] W.K. Lewis, E.R. Gilliland, W.C. Bauer, Characteristics of Fluidized Particles, Ind. Eng. Chem. , 41 (1949) 1104-1117. [41] N. BoškovićVragolović, R. GarićGrulović, Ž. Grbavčić, R. Pjanović, Mass transfer and fluid flow visualization in packed and fluidized beds by the adsorption method, Russ. J. Phys. Chem. , 83 (2009) 1550-1553.
1-tank; 2-submersible pump; 3-a globe valve at the bed inlet; 4-water inlet; 5-water distributor; 6-001*7 resin bed layer; 7-001*7 resin outlet; 8- pressure hole; 9-overflow port; 10-water outlet. Fig.1. Schematic diagram of the LSFBR.
90
60
80
50
70
40
50
30
40
q(mg/g)
30
R(%)
20
R (%)
q (mg/g)
60
20 10
10 0
0 0
20
40
60
80 100 120 140 160 180 200 t (min)
Fig.2. Change curves of Cu(II) removal by the fluidized adsorption process under the specified operating conditions (001*7 resin dosage = 0.5 g/L, superficial liquid velocity = 1.78 cm/s, and initial Cu(II) concentration = 100 mg/L).
100 90 80 70 60 50 40 30 20 10 0
R (%)
q (mg/g)
100 90 80 70 60 50 40 30 20 10 0
q(mg/g) R(%)
0.0
0.5
1.0 1.5 2.0 001*7 resin dosage (g/L)
2.5
3.0
Fig.3. Effect of 001*7 resin dosage on the Cu(II) removal by the fluidized adsorption technique under the specified operating conditions (adsorption time = 120 min, superficial liquid velocity = 1.78 cm/s, and initial Cu(II) concentration = 100 mg/L).
80
q (mg/g)
70 60
q(mg/g)
50
R(%)
40 30 20 10 0 0
20
100 90 80 70 60 50 40 30 20 10 0
R (%)
90
40 60 80 100 120 140 160 initial concentration (mg/L)
Fig.4. Effect of initial concentration on the Cu(II) removal by the fluidized adsorption technique under specified operating conditions (adsorption time = 120 min, 001*7 resin dosage = 0.5 g/L, and superficial liquid velocity of 1.78 cm/s).
q (mg/g)
100 90 80 70 60 50 40 30 20 10 0
1.18cm/s 1.78cm/s 2.07cm/s
0
20
40
60
80 100 120 140 160 180 200 t (min)
Fig.5. Change curves of Cu(II) adsorption capacity by the fluidized adsorption process at different superficial liquid velocities under specified operating conditions (adsorption time = 120 min, 001*7 resin dosage = 0.5 g/L, and initial Cu(II) concentration = 100 mg/L).
5 1.18cm/s
ln(qe-q)
4
1.78cm/s 2.07cm/s
3 2 1 0 0
20
40
60 80 t (min)
100
120
140
Fig.6. Model fitting curves for the copper removal effect by the fluidized adsorption technique at different superficial liquid velocities under specified operating conditions (adsorption time = 120 min, 001*7 resin dosage = 0.5 g/L, and initial Cu(II) concentration = 100 mg/L).
Table 1 Comparison of the uptake capacity toward Cu(II) by adsorbents Experimental conditions Adsorbents 001*7 resin Strong acidic cation exchange fiber hazelnut shell activated carbon MMA-Na-YZeolite zeolite-Portland cement mixture Activated alumina powder polyacrylonitrile fiber Iranian natural zeolite
Adsorption capacity (mg/g)
References
30
103.09
Present study
293
30
89.44
Present study
6.0
293
90
48.64
[32]
4.5
298
90
37.97
[33]
6.0
298
200
23.25
[34]
3.0
318
40
15.50
[3]
6.0
293
60
5.02
Present study
6.0
298
400
4.70
[36]
pH
Temperature (K)
Equilibrium time (min)
6.0
293
6.0
Table 2 The parameters and ANOVA results of multiple linear regression analysis
α
β
τ
Multiple correlation coefficient (R)
Multiple determination coefficient (R2)
Standard error (SE)
significance of F-test
1.501
1.575
2.102
0.907
0.823
0.488
＜0.05
Table 3 Hydraulic and mass transfer parameters under different superficial liquid velocities u (10-2 m/s) 1.18 1.78 2.07
Hydraulic parameters
Mass transfer parameters -1
Re
ε
K (min )
av (m2/m3)
KF (10-7 m/s)
R2
10.56 15.92 18.52
0.71 0.81 0.86
0.0348 0.0344 0.0329
1910.40 1203.64 918.58
3.03 4.76 5.97
0.9938 0.9970 0.9978
Adsorption time t 001*7 resin dosage m/V Initial concentration C0 Superficial liquid velocity u
Dynamic experiments in the LSFBR
Process parameters
Dimensional analysis
Predictive model 1.501
C Ct 2.102 0 M
tu
0.222
V 1.575
Mechanism analysis 5
ln(qe-q)
Effect of superficial liquid velocity was the combination of Re and bed voidage.
1.18cm/s
4
1.78cm/s 2.07cm/s
3 2 1
LSFBR
0 0
20
40
60 80 t (min)
100
120
140
Highlights
A predictive model for fluidized adsorption was developed by dimensional analysis The model predicted adsorption performance well under different operating conditions Effect of superficial liquid velocity was the combination of Re and void fraction
S1383-5866(17)33778-4 https://doi.org/10.1016/j.seppur.2018.06.006 SEPPUR 14660
To appear in:
Separation and Purification Technology
Received Date: Revised Date: Accepted Date:
18 November 2017 22 May 2018 2 June 2018
Please cite this article as: Z. Du, H. Cao, G. Liu, T. Zheng, P. Wang, J. Quan, Modeling for predicting copper ion removal from aqueous solution by the fluidized adsorption based on dimensional analysis, Separation and Purification Technology (2018), doi: https://doi.org/10.1016/j.seppur.2018.06.006
This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Modeling for predicting copper ion removal from aqueous solution by the fluidized adsorption based on dimensional analysis Zhaolin Dua,b, Huizhe Caoc, Guodong Liue, Tong Zhenga,*, Peng Wanga,d, Jin Quanf
a. School of Environment, Harbin Institute of Technology, Harbin 150090, People’s Republic of China b. Agro-Environmental Protection Institute, Ministry of Agriculture, Tianjin 300191, People’s Republic of China c. School of Architecture, Harbin Institute of Technology, Harbin 150006, People’s Republic of China d. State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology, Harbin 150090, People’s Republic of China e. School of Energy Science and Engineering, Harbin Institute of Technology, Harbin 150001, People’s Republic of China f. State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research (IWHR)
*Corresponding author, Tong Zheng: E-mail: [email protected];
Tel: +86 18904513531; Fax: 86-451-86266051
Abstract In this study, we developed a predictive model for quantitatively assessing the copper ion (Cu(II)) removal performance of 001*7 resin in a liquid–solid fluidized bed reactor. The model was developed using the dimensional analysis approach based on dynamic experimental data. Experiments were conducted to evaluate the effects of operational variables (adsorbent dosage, initial solution concentration, and superficial liquid velocity) on the Cu(II)removal performance. The model predictions were in good agreement with the experimental data, thereby indicating that the model could accurately describe the removal of Cu(II)by fluidized adsorption. A model that obtains better predictions could help to optimize the operational variables in implementations of the fluidized adsorption technique for wastewater treatment. The model showed that increasing the 001*7 resin dosage and decreasing the superficial liquid velocity could achieve a higher Cu(II)uptake capacity. In order to verify the theoretical rationality of the model, we clarified the effect of the superficial liquid velocity on the Cu(II)removal. A larger superficial liquid velocity yielded a higher Reynolds number which could enhanced the fluidized adsorption, but led to a higher void fraction which could weaken the adsorption. Thus, the effect of the superficial liquid velocity on the Cu(II) removal was modeled as a combination of the Reynolds number and void fraction.
Key words: Fluidized adsorption; Dimensional analysis; Mathematical model; Copper; Wastewater treatment.
1.
Introduction Copper ions are abundant in nature and they are applied widely in industries such
as mining, electroplating, and metal finishing [1-3]. If the copper pollutants derived from these industries are discharged into watersheds without timely and effective treatment, great environmental damage and economic losses may occur due to the high toxicity of copper[4]. Moreover, copper and its compounds are not readily degraded in water, and they also tend to accumulate in the lungs and other vital organs of humans through the food chain [5, 6]. Therefore, it is essential to remove the Cu(II) from industrial wastewater effectively. Among the common techniques for Cu(II) removal, the adsorption method has the obvious advantage of direct removal from water without secondary pollution and it is easy to operate with low costs [7-12]. The liquid–solid fluidized bed and fixed bed are the two main operational forms of the adsorption method used for industrial wastewater treatment [13, 14]. Compared with the fixed bed reactor, the fluidized bed can provide a larger liquid–solid contact area to give a higher mass transfer capability [15]; as well as removing contaminants from water with a lower head loss in practical applications [16, 17]. Therefore, previous research has focused on the fluidized adsorption technique for Cu(II) removal from water. In order to better guide the implementation of the fluidized adsorption technique for wastewater treatment, it is necessary to determine the quantitative relationships between the fluidized adsorption performance and different operational variables (e.g., superficial liquid velocity and adsorbent dosage). These relationships are particularly
important for obtaining better predictions of the fluidized adsorption performance in practical applications and for understanding the adsorption behavior in liquid–solid fluidized systems. However, these relationships have generally been analyzed and described qualitatively in previous studies [18, 19]. The current fluidized adsorption models usually describe the process partially from one of three perspectives and they fail to consider the effects of all the operational variables [20, 21], although the fluidized adsorption process depends on the adsorption characteristics, hydrodynamics, and mass transfer. Thus, Veeraraghavan et al. [22] developed a model of the adsorption of phenol onto granular activated carbon in a liquid–solid fluidized bed by considering the effects of axial mixing in the solid and liquid phases, and diffusion resistance within the particle. Wang et al. [23] proposed a model of the adsorption of phenol that considers the external mass transfer with film-surface diffusion, surface adsorption equilibrium, and internal mass transfer. Recently, Rahaman et al. [16] used a model to study the influence of the Reynolds number and void fraction on the average mass transfer coefficient, but there was no further quantitative analysis of the fluidized adsorption performance. As mentioned above, most of these models are based on isothermal equations (e.g., Langmuir and Freundlich), as well as some kinetic models, and they can only provide appropriate descriptions of the fluidized adsorption process under specified operating conditions (e.g., the specified superficial liquid velocity). Therefore, they cannot adequately reflect the relationships between the fluidized adsorption performance and different operational variables. It is not always possible to determine the relationships between the fluidized
adsorption performance and operational variables based on theoretical considerations and calculations for a complex liquid–solid fluidized system. In fact, experimental results may be employed directly and clearly using the dimensional analysis method [24-26], which can help to determine the quantitative relationships between the fluidized adsorption performance and different operational variables. The aim of the present study was to develop a mathematical model based on dimensional analysis to predict the Cu(II) removal efficiency by fluidized adsorption under different operating conditions. In order to develop and validate this model, we conducted a series of continuous dynamic experiments with the Cu(II)/001*7 resin system in a liquid–solid fluidized bed reactor (LSFBR).
2.
Experimental section
2.1 Materials The 001*7 resin was purchased from Cangzhou Baoen Resin Company, China. Its cation exchange capacity was 4.5 mmol/g; the average particle diameter was about 0.90 mm; the average wet real density was about 1260 kg/m3. CuSO4·5H2O (analytical grade) was purchased from Tianjin Bodi Chemical Reagent Company. HCl solution (5 w%) was prepared to regenerate the 001*7 resin absorbed with Cu(II). 2.2 Experimental set-up A schematic of the LSFBR is illustrated in Fig. 1. The reactor was made of plexiglass with an internal diameter of 100 mm and a height of 1040 mm. The Cu(II)
solution (40 L) in the tank (1) was prepared by dissolving CuSO4·5H2O in running water. The 001*7 resin bed layer was fed through the upper opening in the reactor and then taken out through the material outlet (7) after adsorption saturation. The required liquid flow rate was determined by the dynamic volumetric method. Fig. 1 During operation of the submersible pump, water could flow at a determined rate from the tank (1) through a perforated plate distributor (5) to distribute water uniformly in the column. The water then flowed through the 001*7 resin bed layer (6), so the particles were fluidized and they could react with the copper ions in the solution. The water could reflux into the tank (1) and the copper ion concentration could be measured in the bed outlet at a fixed time. According to the calculation of Ergun equation [27], the minimum fluidization velocity (umf) was 0.97 cm/s. For the LSFBR, the terminal velocity (ut) was considered to be the free-fall velocity [28], and it was 5.60 cm/s by calculation. 2.3 Dynamic experiments To obtain experimental data for the dimensional analysis, the adsorption of the Cu(II) from aqueous solution on the 001*7 resin was performed in the LSFBR under different operating conditions of adsorption time (0~180 min), adsorbent dosage (0.1~0.5 g/L), initial solution concentration (10~150 mg/L) and superficial liquid velocity (1.18 cm/s~2.07 cm/s). Besides, the optimal pH of the solution to the 001*7 resin was about 6.0 [29]. To regenerate the 001*7 resin after adsorption saturation, it reacted with HCl
solution (5 w%) for 20 min. Then, it was filtered, rinsed with deionized water up to neutral pH, dried, and used in subsequent adsorption experiments. The regeneration experiment was repeated for four periods. 2.4 Analytical methods of the Cu(II) The concentration of the Cu(II) was measured by Inductively Coupled Plasma-Atomic Emission Spectroscopy (ICP-AES). The removal efficiency of the Cu(II) was calculated by:
R C0 Ct / C0 100%
(1)
The adsorption capacity of 001*7 resin was calculated by:
qt C0 Ct V / m
(2)
2.5 Dimensional analysis for fluidized adsorption The residual Cu(II) concentration in the solution at a specified time ( Ct ) was a function of t , u , m , C0 and V , which were considered for dimensional analysis based on the mass transfer and adsorption mechanism[30]. Therefore, their relationship can be expressed as the following equation.
Ct f t , u, C0 , m,V
(3)
The dimensions of t , u , C0 , m and V were [T]−1, [L][T]−1, [M][L]−3, [M] and [L]3, respectively. Length [L], mass [M], and time [T] were the three basic dimensions. Considering that the number of variables was five and the number of basic dimensions was three, all of the variables could be combined into three dimensionless variables based on the π theorem. Thus, we selected t , u , and C0 as
the basic variables, and the equations for the other two variables are as follows.
1 t1 u 1 C01 m
(4)
2 t2 u 2 C0 2V
(5)
According to the theory of similarity, the two equations given above can be changed into the following.
1
m C0 tu
2
(6)
3
V
tu
(7)
3
Then, Eq. (3) could be rewritten as follows. α
1
Ct 2
m C tu 3 0
V tu 3
(8)
Eq. (3) is a power function model for describing the quantitative relationships between the fluidized adsorption performance and different operational variables. The logarithmic form of Eq. (8) is:
V m ln Ct ln ln ln (9) C tu 3 tu 3 0 where the units of all the terms in Eq. (9) conform to the International System of Units.
3.
Results and discussion
3.1 Experimental analysis of Cu(II) removal in the LSFBR 3.1.1. Comparison of the Cu(II) removal efficiencies of some adsorbents In order to select highly efficient adsorbents of copper ions, we compared the copper removal efficiency of several adsorbents in batch adsorption experiments and based on previous research. The adsorption process parameters for the adsorbents are
shown in Table 1. Table 1 According to previous studies [31-35], 001*7 resin possesses a higher adsorption capacity for Cu(II) than other adsorbents under the same operating reaction conditions and its equilibrium time is relatively short. Thus, we selected 001*7 resin as the adsorbent for Cu(II) removal in the LSFBR. 3.1.2. Effect of the adsorption time The changes in the Cu(II) adsorption capacity during different time intervals under the specified operating conditions (001*7 resin dosage = 0.5 g/L, superficial liquid velocity = 1.78 cm/s, and initial Cu(II) concentration = 100 mg/L) are illustrated in Fig. 2. Fig. 2 Figure 2 shows that the adsorption capacity increased sharply during the original adsorption time of 60 min, and it then increased slowly and gradually reached the maximum (about 79.59 mg/g). The change in the removal efficiency exhibited a similar trend to the adsorption capacity, where it increased to about 40% at equilibrium. In fact, when the fluidization process started, the contact area between the resin and the copper ions increased gradually, which led to rapid reactions with the numerous active sites in the resin and the copper ions. At the same time, the concentration of copper ions was high and the difference in the concentration between the solid and liquid phases was large, thereby increasing the liquid–solid mass transfer rate and the reaction between the resin and the copper ions accelerated. Thus, the
adsorption capacity and removal efficiency increased rapidly. However, the number of active sites decreased and the difference in concentration became smaller with the adsorption time, and thus the copper ions absorbed in the resin tended to repel those in aqueous solution, which weakened the reaction until the adsorption equilibrium was reached. 3.1.3. Effect of the 001*7 resin dosage We also investigated the effect of the 001*7 resin dosage on the copper ion removal efficiency under the specified operating conditions (adsorption time = 120 min, superficial liquid velocity = 1.78 cm/s, and initial Cu(II) concentration = 100 mg/L) and the results are shown in Fig. 3. Fig. 3 Figure 3 shows that the Cu(II) removal efficiency increased sharply from 8.17% to 97.76% as the resin dosage increased from
, whereas it increased
only slightly to 99.08% when the resin dosage was 2.5 g/L. In addition, the adsorption capacity decreased from 80.90 mg/g to 39.68 mg/g because the solid mass flux was enhanced as the resin dosage increased, thereby providing a larger number of active sites for Cu(II) removal and a higher removal efficiency. However, considering the specific amount of copper ions in the solution, the amount of Cu(II) removed per unit mass decreased as the 001*7 resin dosage increased according to the equation for calculating the adsorption capacity (Eq. 2). These results demonstrate that further increasing the resin dosage could not significantly improve the rate of Cu(II) removal beyond a certain dosage. Using an excess resin dosage could increase the wastewater
treatment costs. Therefore, a balance should be reached between the adsorbent dosage and Cu(II) removal in practical wastewater treatment processes. 3.1.4. Effect of initial Cu(II) concentration We investigated the effect of the initial copper ion concentration on the adsorption capacity under specified operating conditions (adsorption time = 120 min, 001*7 resin dosage = 0.5 g/L, and superficial liquid velocity of 1.78 cm/s) and the results are shown in Fig. 4. Fig. 4 Figure 4 shows that the adsorption capacity increased rapidly as the initial copper ion concentration increased from 10 to 80 mg/L, but it remained constant (about 80 mg/g) above 100 mg/L. In addition, the removal efficiency decreased from 95.71% to 27.17%. The concentration gradient between the solution and the 001*7 resin surface became larger as the initial concentration increased, thereby accelerating the mass transfer coefficient and enhancing the adsorption process[36]. Hence, the adsorption capacity increased at the beginning. However, each specific resin dosage had a limited number of active sites. Thus, insufficient active sites were available to react with the copper ions as the initial concentration increased, so the residual copper concentration increased and the removal efficiency was lower. When the initial concentration exceeded 100 mg/L, it was difficult to increase the adsorption capacity of the 001*7 resin at a dosage of 0.5 g/L. Therefore, the fluidization adsorption method is more suitable for treating wastewater containing higher initial concentrations of copper ions.
3.1.5. Effect of superficial liquid velocity The changes in the Cu(II) adsorption capacity with time at different superficial liquid velocities (1.18, 1.78, and 2.07 cm/s) under specified operating conditions (adsorption time = 120 min, 001*7 resin dosage = 0.5 g/L, and initial Cu(II) concentration = 100 mg/L) are illustrated in Fig. 5. Fig. 5 Figure 5 shows that the adsorption capacity increased at the beginning, but then remained at the maximum with time at the specified superficial liquid velocity. The adsorption capacity decreased from 92.03 mg/g to 67.21 mg/g, and the removal efficiency decreased from 46.23% to 33.51% when the superficial liquid velocity increased from
The higher superficial liquid velocity could
lead to dramatic fluidization, thereby enhancing the collision frequency between the 001*7 resin and copper ions in the aqueous solution. Thus, the mass transfer coefficient was proportional to the superficial liquid velocity, which could enhance the adsorption performance [37]. However, it should be noted that increasing the superficial liquid velocity also increased the void fraction, which could reduce the contact area between the resin and the copper ions to further decrease the Cu(II) removal efficiency [20]. In addition, the higher Cu(II) removal at lower superficial liquid velocities was due to the increased contact time between the resin and copper ions [36, 38]. Hence, the superficial liquid velocity should be designed specifically for practical wastewater treatment processes. 3.1.6. Regeneration treatment of 001*7 resin
The experimental results showed that the Cu(II) adsorption capacity of the 001*7 resin decreased from 103.09 mg/g to 89.26 mg/g after four regeneration periods. This indicates that the recycled 001*7 resin still possessed an excellent capacity for Cu(II) uptake, which could reduce the consumption of the resin and economic costs. 3.2 Dimensional analysis results Based on multiple linear regression analysis, the parameters of Eq. (9) (Table 1) were obtained using the experimental data presented in Section 3.1. The final predictive model was as follows: 1.501
C Ct 2.102 0 M
tu
0.222
V 1.575
(10)
We conducted analysis of variance to verify the efficiency and accuracy of the proposed model, and the results are shown in Table 2. Table 2 Table 2 shows that the multiple correlation coefficient was larger than 0.9, which indicates that the term for Ct was highly correlated with the other terms for the independent variables. According to additional performance indicators (Multiple determination coefficient (R) and Standard error (SE)) in Table 2, the measured and predicted Cu(II) removal efficiency agreed very well. In addition, the regression results for the variables in Eq. (10) were significant. Thus, the proposed model described the quantitative relationships between the Cu(II) removal performance and different operational variables in the LSFBR fairly well.
Notably, the model could reveal the quantitative relationship between Cu(II) removal and operating parameters: the residual Cu(II) concentration was inversely proportional to the 001*7 resin dosage and superficial liquid velocity, while was proportional to the initial Cu(II) concentration, which agreed with the qualitative analysis results based on the experimental data presented in Section 3.1. Besides, the model, as a valuable tool, could be used to estimate the amount of 001*7 resin for Cu(II) removal from industrial wastewater by fluidized adsorption technique with minimal risk of product loss when other operating variables were determined. In particular, the model could describe the quantitative relationships between Cu(II) removal and the operating parameters, where the residual copper ion concentration was inversely proportional to the 001*7 resin dosage, and proportional to the initial Cu(II) concentration and superficial liquid velocity, which agreed with the qualitative analysis results based on the experimental data given in Section 3.1. In addition, the model is a valuable tool for estimating the amount of 001*7 resin required for removing Cu(II) from industrial wastewater using the fluidized adsorption technique with the minimal risk of product loss after the other operating variables have been determined. 3.3 Determination of the theoretical rationality of the predictive model It was also necessary to explore the effect of the superficial liquid velocity on the copper removal performance to determine the theoretical rationality of the predictive model. To simplify this study, plug flow of the liquid was assumed in the LSFBR. The whole adsorption process was divided into three phases: external diffusion,
internal diffusion, and reaction of the active sites on the inner surface. The third process was very rapid and it could not affect the adsorption rate [38]. Hence, the adsorption rate was controlled by the external or internal diffusion processes. The equation of external diffusion mass transfer rate was [38]: dq k f am Ct Ci dt
(11)
The equation of internal diffusion mass transfer rate was [38]: dq ks am qi qe dt
(12)
As the Ci and qi cannot be measured, the whole adsorption process was normally studied as the quasi-steady state [39]. The calculation formula of overall mass transfer rate was as follows： dq K F am C Ce K S am qe q dt m KS KF V
(13) (14)
The integral form of the equation (13) was as follows: m K F am t V q qe 1 e
(15)
The logarithmic form of the equation (15) was： ln qe q ln qe K F am
m t V
(16)
With the following initial condition: IC: t=0, q=0, C= C0. Where K F am
m was the adsorption rate constant K (s-1) V
The surface area per unit volume of 001*7 resin in the LSFBR was related to the 001*7 resin particle size and the void fraction. It can be obtained by the calculation of
the following equations [20]: aV
6 1 dp
(17)
am m V
(18)
aV
The void fraction can be calculated by solving Lewis-Bowerman equation with the following boundary condition [40]: u 2.00 2 gd p p l
0.215
(19)
BC: Re<2 u 0.29 0.43 1.95 0.71 1.14l 0.71 g d p ( p l )
0.337
(20)
BC: 2
0.43
(21)
BC: Re＞500 The calculation formula of Re was as follows: Re
d pu
(22)
The experimental results presented in Section 3.1.4 were fitted by the logarithmic form of Eq. (16) and the fitted curves are shown in Fig. 6. The hydraulic and mass transfer parameter values under different superficial liquid velocities shown in Table 3 were calculated using Eqs. (16)–(22). Fig. 6 Table 3 According to Fig. 6 and the performance indicator (R2 > 0.99) in Table 3, Eq. (16) provided good descriptions of the Cu(II) removal process in the LSFBR under
different superficial liquid velocities. Table 3 shows that the adsorption rate constant ( K ) decreased as the superficial liquid velocity increased, which was consistent with the experimental results in Section 3.1.4. However, the mass transfer coefficient ( K F ) was positively correlated with the superficial liquid velocity. This is because the bigger superficial liquid velocity led to a larger Re value, thereby indicating that the turbulence of the flow field in the LSFBR was more severe and the contact frequency of the 001*7 resin with the copper ions in the solution increased. Thus, the mass transfer coefficient became larger and the adsorption reaction was enhanced. However, at the same time, the increase in the superficial liquid velocity also caused an increase in the void fraction, which decreased the specific surface area of the 001*7 resin [37]; so the Cu(II) removal efficiency was reduced in the LSFBR, as also shown by Boskovic [41]. In fact, when the superficial liquid velocity increased from 1.18 cm/s to 2.07 cm/s, the mass transfer coefficient increased by 0.9 times, whereas the specific surface area decreased by 1.1 times according to the calculations. Therefore, the Cu(II) removal performance had a negative correlation with the superficial liquid velocity in the LSFBR in the present study.
4.
Conclusions In the present study, dimensional analysis was employed to determine the Cu(II)
removal performance as functions of the adsorbent dosage, initial solution concentration, and superficial liquid velocity based on dynamic adsorption experiments in an LSFBR. The dynamic adsorption capacity of 001*7 resin was 92.03
mg/g under the optimum reaction conditions comprising 001*7 resin dosage = 0.5 g/L, superficial liquid velocity = 1.18 cm/s, and initial Cu(II) concentration = 100 mg/L. The model predicted the fluidized adsorption performance under different operating conditions very well for the Cu(II)/001*7 resin system. The Cu(II) removal performance increased as the superficial liquid velocity decreased and as the 001*7 resin dosage increased. Furthermore, we explored the effect of the superficial liquid velocity on the Cu(II) removal performance based on the adsorption and mass transfer mechanism to verify the theoretical rationality of the model. We found that a larger superficial liquid velocity yielded a higher Reynolds number with enhanced fluidized adsorption, whereas a higher void fraction weakened the fluidized adsorption, and thus the effect of the superficial liquid velocity on Cu(II) removal was a combination of the Reynolds number and the void fraction. In conclusion, the model developed in this study could be a valuable tool for process development and optimization with minimal risk of product losses when removing Cu(II) from water by fluidized adsorption.
5.
ACKNOWLEDGMENTS This research was supported by National Water Pollution Control and
Management Technology major projects (2012ZX07205-005) and the Central Public Research Institute Basic Fund for Research and Development (2018-jbkyywf-dzl). This research was also supported by Open Project of State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology (NO.QAK201604).
Nomenclature Cu(II) Copper ion LSFBR Liquid–Solid Fluidized Bed Reactor
am Surface area per unit mass of 001*7 resin in the LSFBR (m2/g) aV Surface area per unit volume of 001*7 resin in the LSFBR (m2/m3) C0 Initial concentration of the Cu(II) in the solution (mg/L) Ce Equilibrium concentration of the Cu(II) in the solution (mg/L)
Ci Cu(II) concentration in the outer surface at the specified time (mg/L) Ct Concentration of the Cu(II) in the solution at the specified time (mg/L)
d s Average particle size of the 001*7 resin (m) g Acceleration of gravity (m/s2) k f Mass transfer coefficient in the external process (m/s)
k s Mass transfer coefficient in the internal diffusion process (g/(s·m2)) K F Overall mass transfer coefficient (its impetus on concentration difference) (m/s)
K S Overall mass transfer coefficient (its impetus on adsorption capacity difference)
(g/(s·m2)).
m Dry weight of the 001*7 resin (g) qe Equilibrium adsorption capacity corresponding to the Ce (mg/g) qi Adsorption capacity of 001*7 resin corresponding to the Ci (mg/g) qt Adsorption capacity of 001*7 resin corresponding to the Ct (mg/g) R Removal efficiency of the Cu(II) from aqueous solution (%)
t Adsorption time (s)
u Superficial liquid velocity (cm/s) V Volume of the solution in the tank (L)
Greek letters
Void fraction (-) l Density of water (kg/m3) p Average wet real density of 001*7 resin (kg/m3)
Dynamic viscosity of water (N·s/m2) Kinematic viscosity of water (m2/s)
Subscripts l Liquid phase p Particle phase
REFERENCES [1] A. Labidi, A.M. Salaberria, S.C.M. Fernandes, J. Labidi, M. Abderrabba, Adsorption of copper on chitin-based materials: Kinetic and thermodynamic studies, Journal of the Taiwan Institute of Chemical Engineers, 65 (2016) 140-148. [2] K.H. Chu, M.A. Hashim, Adsorption of copper(II) and EDTA‐chelated copper(II) onto granular activated carbons, Journal of Chemical Technology & Biotechnology, 75 (2015) 1054-1060. [3] J.P. Chen, S. Wu, Study on EDTA‐chelated copper adsorption by granular activated carbon, Journal of Chemical Technology & Biotechnology, 75 (2015) 791-797. [4] A. Bogusz, P. Oleszczuk, R. Dobrowolski, Application of laboratory prepared and commercially available biochars to adsorption of cadmium, copper and zinc ions from water, Bioresour. Technol. , 196 (2015) 540. [5] M.K. Ahmadi, M. Ghafari, J.D. Atkinson, B.A. Pfeifer, A Copper Removal Process for Water based upon Biosynthesis of Yersiniabactin, a Metal-binding Natural Product, Chem. Eng. J. , 306 (2016) 772-776. [6] S.H. Park, H.J. Cho, C. Ryu, Y.-K. Park, Removal of copper(II) in aqueous solution using pyrolytic biochars derived from red macroalga Porphyra tenera, J. Ind. Eng. Chem. , 36 (2016) 314-319. [7] F. Ciesielczyk, P. Bartczak, Ł. Klapiszewski, T. Jesionowski, Treatment of model and galvanic waste solutions of copper(II) ions using a lignin/inorganic oxide hybrid as an effective sorbent, J. Hazard. Mater. , 328 (2017) 150-159. [8] C.A. Papadimitriou, G. Krey, N. Stamatis, A. Kallaniotis, The use of waste mussel shells for the adsorption of dyes and heavy metals, Journal of Chemical Technology & Biotechnology, (2017). [9] C. Jeon, Removal of cesium ions from aqueous solutions using immobilized nickel hexacyanoferrate-sericite beads in the batch and continuous processes, Journal of Industrial & Engineering Chemistry, 40 (2016) 93-98. [10] M. Sajid, M.K. Nazal, Ihsanullah, N. Baig, A.M. Osman, Removal of heavy metals and organic pollutants from water using dendritic polymers based adsorbents: A critical review, Separation & Purification Technology, (2017). [11] Ihsanullah, A. Abbas, A.M. Al-Amer, T. Laoui, M.J. Al-Marri, M.S. Nasser, M. Khraisheh, M.A. Atieh, Heavy metal removal from aqueous solution by advanced carbon nanotubes: Critical review of adsorption applications, Separation & Purification Technology, 157 (2016) 141-161. [12] S.S. Rath, S. Singh, D.S. Rao, B.B. Nayak, B.K. Mishra, Adsorption of Heavy Metals on a Complex Al-Si-O Bearing Mineral System: Insights from Theory and Experiments, Separation & Purification Technology, 186 (2017). [13] S. Shanmuganathan, T.V. Nguyen, W.G. Shim, J. Kandasamy, A. Listowski, S. Vigneswaran, Effluent organic matter removal from reverse osmosis feed by granular activated carbon and purolite A502PS fluidized beds, J. Ind. Eng. Chem. , 20 (2014) 4499-4508. [14] C. Lee, J. Jung, R.R. Pawar, M. Kim, Lalhmunsiama, S.-M. Lee, Arsenate and phosphate removal from water using Fe-sericite composite beads in batch and fixed-bed systems, J. Ind. Eng. Chem. , 47 (2017) 375-383. [15] A. Gaikwad, S. Kale, A. Lali, Modeling of counter-current adsorption in continuous liquid-solid circulating fluidized bed adsorption chromatography, Chem. Eng. Sci. , 63 (2008) 1062-1071. [16] M.S. Rahaman, D.S. Mavinic, A. Meikleham, N. Ellis, Modeling phosphorus removal and recovery from anaerobic digester supernatant through struvite crystallization in a fluidized bed reactor,
Water Res. , 51 (2014) 1-10. [17] D. Wang, E. McLaughlin, R. Pfeffer, Y.S. Lin, Aqueous phase adsorption of toluene in a packed and fluidized bed of hydrophobic aerogels, Chem. Eng. J. , 168 (2011) 1201-1208. [18] C.I. Lee, W.F. Yang, C.S. Chiou, Utilization of water clarifier sludge for copper removal in a liquid fluidized-bed reactor, J. Hazard. Mater. , 129 (2006) 58. [19] L. Li, C. Yu, J. Bai, Q. Wang, Z. Luo, Heavy metal characterization of circulating fluidized bed derived biomass ash, J. Hazard. Mater. , 233–234 (2012) 41-47. [20] D.V. Kalaga, A. Dhar, S.V. Dalvi, J.B. Joshi, Particle-liquid mass transfer in solid–liquid fluidized beds, Chem. Eng. J. , 245 (2014) 323-341. [21] J.J. Derksen, Simulations of solid–liquid mass transfer in fixed and fluidized beds, Chem. Eng. J. , 255 (2014) 233-244. [22] S. Veeraraghavan, L.T. Fan, A.P. Mathews, Modeling adsorption in liquid—solid fluidized beds, Chem. Eng. Sci. , 44 (1989) 2333-2345. [23] R.C. Wang, C.C. Kuo, C.C. Shyu, Adsorption of phenols onto granular activated carbon in a liquid-solid fluidized bed, J. Chem. Technol. Biotechnol. , 68 (1997) 187-194. [24] A. Izady, O. Abdalla, M. Sadeghi, M. Majidi, A. Karimi, M. Chen, A Novel Approach to Modeling Wastewater Evaporation Based on Dimensional Analysis, Water Resour. Manage 30 (2016) 1-14. [25] S. Gillot, S. Capela-Marsal, M. Roustan, A. Héduit, Predicting oxygen transfer of fine bubble diffused aeration systems--model issued from dimensional analysis, Water Res. , 39 (2005) 1379. [26] R. Hassan, K. Loubiere, J. Legrand, G. Delaplace, A consistent dimensional analysis of gas–liquid mass transfer in an aerated stirred tank containing purely viscous fluids with shear-thinning properties, Chem. Eng. J. , 184 (2012) 42-56. [27] V.J. Inglezakis∗, M. Loizidou‡, Hydrodynamic Studies on Zeolite Fluidized Beds, Int. J. Chem. Reactor Eng. , 8 (2010) 47-54. [28] R.C. Wang, S.C. Chang, Adsorption/desorption of phenols onto granular activated carbon in a liquid–solid fluidized bed, Journal of Chemical Technology & Biotechnology, 74 (2015) 647-654. [29] J. Jian-Hong, The treatment of heavy metal wastewater by ion-exchange resin, Tianjin Chemical Industry, 25 (2011) 60-62. [30] Q.D. Lan, J.X. Zhu, A.S. Bassi, A. Margaritis, Y. Zheng, G.E. Rowe, Continuous protein recovery using a liquid-solid circulating fluidized bed ion exchange system: Modelling and experimental studies, Can. J. Chem. Eng. , 78 (2000) 858-866. [31] E. Demirbas, N. Dizge, M.T. Sulak, Adsorption kinetics and equilibrium of copper from aqueous solutions using hazelnut shell activated carbon, Chemical Engineering Journal, 148 (2009) 480-487. [32] K.Z. Elwakeel, A.A. El-Bindary, E.Y. Kouta, Retention of copper, cadmium and lead from water by Na-Y-Zeolite confined in methyl methacrylate shell, Journal of Environmental Chemical Engineering, 5 (2017). [33] S.O. Yong, J.E. Yang, Y.S. Zhang, S.J. Kim, D.Y. Chung, Heavy metal adsorption by a formulated zeolite-Portland cement mixture, Journal of Hazardous Materials, 147 (2007) 91. [34] N.S. Rajurkar, A.N. Gokarn, K. Dimya, Adsorption of Chromium(III), Nickel(II), and Copper(II) from Aqueous Solution by Activated Alumina, Clean – Soil Air Water, 39 (2011) 767–773. [35] H. Merrikhpour, M. Jalali, Comparative and competitive adsorption of cadmium, copper, nickel, and lead ions by Iranian natural zeolite, Clean Technologies & Environmental Policy, 15 (2013) 303-316. [36] D.V. Kalaga, A. Dhar, S.V. Dalvi, J.B. Joshi, Particle-liquid mass transfer in solid–liquid
fluidized beds, Chem. Eng. J. , 245 (2014) 323–341. [37] S.L. Dashtban Kenari, B. Barbeau, Pyrolucite fluidized-bed reactor (PFBR): A robust and compact process for removing manganese from groundwater, Water Res. , 49 (2014) 475-483. [38] E. Rezaei, O. Mowla, D. Mowla, Aqueous phase adsorption of organic compounds on activated carbon in fluidized bed, Desalination and Water Treatment, 28 (2011) 35-41. [39] N. Boskovic-Vragolovic, R. Garic-Grulovic, Z. Grbavcic, R. Pjanovic, Mass transfer and fluid flow visualization in packed and fluidized beds by the adsorption method, Russ. J. Phys. Chem. A 83 (2009) 1550-1553. [40] W.K. Lewis, E.R. Gilliland, W.C. Bauer, Characteristics of Fluidized Particles, Ind. Eng. Chem. , 41 (1949) 1104-1117. [41] N. BoškovićVragolović, R. GarićGrulović, Ž. Grbavčić, R. Pjanović, Mass transfer and fluid flow visualization in packed and fluidized beds by the adsorption method, Russ. J. Phys. Chem. , 83 (2009) 1550-1553.
1-tank; 2-submersible pump; 3-a globe valve at the bed inlet; 4-water inlet; 5-water distributor; 6-001*7 resin bed layer; 7-001*7 resin outlet; 8- pressure hole; 9-overflow port; 10-water outlet. Fig.1. Schematic diagram of the LSFBR.
90
60
80
50
70
40
50
30
40
q(mg/g)
30
R(%)
20
R (%)
q (mg/g)
60
20 10
10 0
0 0
20
40
60
80 100 120 140 160 180 200 t (min)
Fig.2. Change curves of Cu(II) removal by the fluidized adsorption process under the specified operating conditions (001*7 resin dosage = 0.5 g/L, superficial liquid velocity = 1.78 cm/s, and initial Cu(II) concentration = 100 mg/L).
100 90 80 70 60 50 40 30 20 10 0
R (%)
q (mg/g)
100 90 80 70 60 50 40 30 20 10 0
q(mg/g) R(%)
0.0
0.5
1.0 1.5 2.0 001*7 resin dosage (g/L)
2.5
3.0
Fig.3. Effect of 001*7 resin dosage on the Cu(II) removal by the fluidized adsorption technique under the specified operating conditions (adsorption time = 120 min, superficial liquid velocity = 1.78 cm/s, and initial Cu(II) concentration = 100 mg/L).
80
q (mg/g)
70 60
q(mg/g)
50
R(%)
40 30 20 10 0 0
20
100 90 80 70 60 50 40 30 20 10 0
R (%)
90
40 60 80 100 120 140 160 initial concentration (mg/L)
Fig.4. Effect of initial concentration on the Cu(II) removal by the fluidized adsorption technique under specified operating conditions (adsorption time = 120 min, 001*7 resin dosage = 0.5 g/L, and superficial liquid velocity of 1.78 cm/s).
q (mg/g)
100 90 80 70 60 50 40 30 20 10 0
1.18cm/s 1.78cm/s 2.07cm/s
0
20
40
60
80 100 120 140 160 180 200 t (min)
Fig.5. Change curves of Cu(II) adsorption capacity by the fluidized adsorption process at different superficial liquid velocities under specified operating conditions (adsorption time = 120 min, 001*7 resin dosage = 0.5 g/L, and initial Cu(II) concentration = 100 mg/L).
5 1.18cm/s
ln(qe-q)
4
1.78cm/s 2.07cm/s
3 2 1 0 0
20
40
60 80 t (min)
100
120
140
Fig.6. Model fitting curves for the copper removal effect by the fluidized adsorption technique at different superficial liquid velocities under specified operating conditions (adsorption time = 120 min, 001*7 resin dosage = 0.5 g/L, and initial Cu(II) concentration = 100 mg/L).
Table 1 Comparison of the uptake capacity toward Cu(II) by adsorbents Experimental conditions Adsorbents 001*7 resin Strong acidic cation exchange fiber hazelnut shell activated carbon MMA-Na-YZeolite zeolite-Portland cement mixture Activated alumina powder polyacrylonitrile fiber Iranian natural zeolite
Adsorption capacity (mg/g)
References
30
103.09
Present study
293
30
89.44
Present study
6.0
293
90
48.64
[32]
4.5
298
90
37.97
[33]
6.0
298
200
23.25
[34]
3.0
318
40
15.50
[3]
6.0
293
60
5.02
Present study
6.0
298
400
4.70
[36]
pH
Temperature (K)
Equilibrium time (min)
6.0
293
6.0
Table 2 The parameters and ANOVA results of multiple linear regression analysis
α
β
τ
Multiple correlation coefficient (R)
Multiple determination coefficient (R2)
Standard error (SE)
significance of F-test
1.501
1.575
2.102
0.907
0.823
0.488
＜0.05
Table 3 Hydraulic and mass transfer parameters under different superficial liquid velocities u (10-2 m/s) 1.18 1.78 2.07
Hydraulic parameters
Mass transfer parameters -1
Re
ε
K (min )
av (m2/m3)
KF (10-7 m/s)
R2
10.56 15.92 18.52
0.71 0.81 0.86
0.0348 0.0344 0.0329
1910.40 1203.64 918.58
3.03 4.76 5.97
0.9938 0.9970 0.9978
Adsorption time t 001*7 resin dosage m/V Initial concentration C0 Superficial liquid velocity u
Dynamic experiments in the LSFBR
Process parameters
Dimensional analysis
Predictive model 1.501
C Ct 2.102 0 M
tu
0.222
V 1.575
Mechanism analysis 5
ln(qe-q)
Effect of superficial liquid velocity was the combination of Re and bed voidage.
1.18cm/s
4
1.78cm/s 2.07cm/s
3 2 1
LSFBR
0 0
20
40
60 80 t (min)
100
120
140
Highlights
A predictive model for fluidized adsorption was developed by dimensional analysis The model predicted adsorption performance well under different operating conditions Effect of superficial liquid velocity was the combination of Re and void fraction