Abstract
This paper focused on the adsorption behavior of Fe (III) in aqueous solution on melamine. The effects of experimental conditions including dosage of melamine, reaction time and reaction temperature were investigated. The results showed that nearly 99% Fe (III) was adsorbed under the optimal conditions: melamine dosage (mole ratio) at n(C3H6N6)/n(Fe) = 3.5:1, reaction time of 60 min and reaction temperature of 90 °C. The optimal processing factors were obtained from response surface methodology and the effects of processing parameters on the removal efficiency of Fe (III) followed the order: mole ratio (n(C3N6H6):n(Fe)) > reaction temperature > reaction time. The adsorption kinetics behavior was fitted well with the pseudo-second-order model. The thermodynamic study showed that the adsorption process was unspontaneous and endothermic. The value of free energy change and standard enthalpy change disclosed that the mechanism of adsorption onto melamine was physisorption. The results will be useful for further applications of system design in the treatment of practical waste effluents.
HIGHLIGHTS
Fe (III) was easily adsorbed by melamine.
The adsorption kinetics was analyzed.
Graphical Abstract
INTRODUCTION
Water contamination by heavy metal ions is a serious environmental problem (Chamnongpol et al. 2002). Heavy metals can be toxic pollutants that are nonbiodegradable, and have environmental, public health and economic impacts. In the environment, one element can be present in different chemical forms, which differ in their chemical behavior, bioavailability and toxicity. Some elements such as iron, arsenic, manganese and chromium are mainly present in natural water as two oxidation states. For instance, Cr (VI), As (III) and As (V) are known carcinogens, while Fe (II), Fe (III), Mn (II), Mn (VII) and Cr (III) are essential micronutrients for organisms and plants. However, they become toxic at higher levels. Iron is a major one of these heavy metals; it is the second most abundant metal element in the Earth's crust, and is mainly present in natural water as two oxidation states: Fe (II) and Fe (III). Iron is very important in the biosphere; it plays an essential role in photosynthesis and is the limiting growth nutrient for phytoplankton in some parts of the ocean. Waste effluents from steel tempering, coal coking and mining industries, for example, contain significant quantities of iron, nickel, copper and zinc. Excessive amounts of iron in the water not only cause formation of unpleasant smells, turbidity, color of water, and corrosion of water pipes but also adversely affect human health (Martynov et al. 2018). Therefore, before using water for drinking purposes, it is necessary to remove this component.
The efficient technologies used for the removal of trace metals from wastewater include chemical precipitation (Peng et al. 2018; Xiong et al. 2018; Shu et al. 2019; Peng & Guo 2020), electro-reduction (Peng et al. 2019a, 2019b), solvent extraction (Elwakeel & Guibal 2015; Ming et al. 2017; Xiaobo et al. 2017) and adsorption (El-Sikaily et al. 2007; Al-Wakeel et al. 2015; Kumar & Jena 2017). Among these methods, adsorption had been proved to be an efficient and economical technique because of its simple operation without producing by-products, high efficiency and low input (Tangtubtim & Saikrasun 2019; Wang et al. 2019; Zhang et al. 2019). The surface and active sites on the surface of adsorbents are very important for the adsorption process. Although activated carbon and silica gel have played an important role in trace element analysis, they are limited by their high cost (El-Shafey et al. 2002). Melamine, used in manufacture of plastics, adhesives, cleaners, and yellow dye (Bischoff 2018; Fink 2018), had been proved to be highly efficient at removing heavy metal ions from dilute solutions when it was modified with metal–organic frameworks (Cao et al. 2016; Seo et al. 2016; Yin et al. 2018). Also, the adsorption with unmodified melamine has attracted much more attention, and has been successfully applied in removal of heavy metal ions in the wastewater, like vanadium (V) (Peng et al. 2017a, 2017b), silver (Huang et al. 2006), and chromium (III) (Guo et al. 2020; Peng et al. 2020b).
In this paper, adsorption behavior of Fe (III) in aqueous solution on melamine was studied. The effects of experimental conditions including dosage of melamine, reaction time and reaction temperature were investigated. The results will be useful for further applications of system design in the treatment of practical waste effluents.
MATERIALS AND METHODS
Materials
All the reagents including sulfuric acid (H2SO4), ferric sulfate (Fe2(SO4)3) and melamine (C3N6H6) were analytical grade, purchased from Kelong Co., Ltd, Chengdu, China, and used as received without purification. The deionized water used in the experiments was produced by a water purification system (HMC-WS10).
Batch adsorption experiments
Experimental design and optimization
The effects of variables (alone and their interactions) on the adsorption of Fe (III) and optimization of independent variables (mole ratio of melamine to Fe (A), reaction temperature (B) and reaction time (C)) on the response variable (removal efficiency) were determined with Design Expert 8.0 software (Zhang et al. 2015; Peng et al. 2019a, 2020a, 2019c). The actual values for experimental parameters are detailed in Table 1.
Independent variables and factor levels
Independent variable . | Unit . | Level . | ||
---|---|---|---|---|
−1 . | 0 . | 1 . | ||
A:Mole ratio (n(C3N6H6)/n(Fe)) | – | 0.50 | 2.00 | 3.50 |
B: Reaction temperature | °C | 30.00 | 60.00 | 90.00 |
C: Reaction time | min | 15.00 | 37.50 | 60.00 |
Independent variable . | Unit . | Level . | ||
---|---|---|---|---|
−1 . | 0 . | 1 . | ||
A:Mole ratio (n(C3N6H6)/n(Fe)) | – | 0.50 | 2.00 | 3.50 |
B: Reaction temperature | °C | 30.00 | 60.00 | 90.00 |
C: Reaction time | min | 15.00 | 37.50 | 60.00 |
Central composite design experimental matrix and experimental results for this study
Run . | n(C3N6H6)/n(Fe) . | Reaction temperature . | Reaction time . | Removal efficiency (%) . |
---|---|---|---|---|
1 | 2.0 | 30 | 60 | 58.42 |
2 | 2.0 | 60 | 37.5 | 61.20 |
3 | 3.5 | 60 | 60 | 97.83 |
4 | 2.0 | 30 | 15 | 53.33 |
5 | 2.0 | 60 | 37.5 | 61.20 |
6 | 0.5 | 90 | 37.5 | 58.48 |
7 | 2.0 | 60 | 37.5 | 61.20 |
8 | 0.5 | 30 | 37.5 | 31.07 |
9 | 2.0 | 60 | 37.5 | 61.20 |
10 | 2.0 | 90 | 15 | 74.74 |
11 | 3.5 | 60 | 15 | 95.47 |
12 | 0.5 | 60 | 60 | 42.28 |
13 | 3.5 | 30 | 37.5 | 95.43 |
14 | 2.0 | 90 | 60 | 84.84 |
15 | 0.5 | 60 | 15 | 28.46 |
16 | 3.5 | 90 | 37.5 | 98.75 |
17 | 2.0 | 60 | 37.5 | 61.20 |
Run . | n(C3N6H6)/n(Fe) . | Reaction temperature . | Reaction time . | Removal efficiency (%) . |
---|---|---|---|---|
1 | 2.0 | 30 | 60 | 58.42 |
2 | 2.0 | 60 | 37.5 | 61.20 |
3 | 3.5 | 60 | 60 | 97.83 |
4 | 2.0 | 30 | 15 | 53.33 |
5 | 2.0 | 60 | 37.5 | 61.20 |
6 | 0.5 | 90 | 37.5 | 58.48 |
7 | 2.0 | 60 | 37.5 | 61.20 |
8 | 0.5 | 30 | 37.5 | 31.07 |
9 | 2.0 | 60 | 37.5 | 61.20 |
10 | 2.0 | 90 | 15 | 74.74 |
11 | 3.5 | 60 | 15 | 95.47 |
12 | 0.5 | 60 | 60 | 42.28 |
13 | 3.5 | 30 | 37.5 | 95.43 |
14 | 2.0 | 90 | 60 | 84.84 |
15 | 0.5 | 60 | 15 | 28.46 |
16 | 3.5 | 90 | 37.5 | 98.75 |
17 | 2.0 | 60 | 37.5 | 61.20 |
Kinetics study
Thermodynamic study
RESULTS AND DISCUSSION
The composition in the solution
A mole fraction distribution diagram of Fe species in the Fe-H2O system was calculated when pH = 1–7 and [Fe] = 1,120 mg/L at different temperatures; the results are shown in Figure 1. It can be seen that the Fe (III) existed in the form of Fe(OH)2+, Fe(OH)3(aq), , Fe3+, Fe2(OH)34+, Fe3(OH)54+ and FeOH2+. Almost 100% of Fe in the solution existed in the form of Fe3+ when the pH = 1. With the increasing of pH value, the mole fraction of Fe3+ reduced gradually and started to transform to FeOH2+, and the mole fraction of FeOH2+ reached 75% at pH = 3. With further increased pH, FeOH2+ gradually transformed to Fe(OH)2+, with the corresponding mole fraction reaching 99.6% at pH = 6.5. Fe(OH)3(aq) existed in the pH ranged 1–4 and the maximum mole fraction was 2.92% at pH = 3. As reaction temperature increased, the pH of maximum mole fraction of various Fe species occurring was changed respectively: pH = 3 changed to 2.5 for FeOH2+, pH = 6.5 changed to 6 for Fe(OH)2+. The mole fraction of
was increased from 0.24% to 83% as reaction temperature increased from 30 °C to 90 °C.
Characterization
The specific surface area of melamine was measured on an ASAP 2020 (Micrometrics, USA) by N2 adsorption/desorption isotherms. The results shown in Figure 2 indicate that small micro-pores existed in the surface of melamine, and monolayer adsorption existed during the adsorption process. And the pores in the adsorbents were narrow slit-like pores. The melamine showed a great specific surface area (8.71 m2/g) and adsorption pore volume (0.0040 mL/g) (Peng et al. 2020b).
Single factor experiments
The effect of dosage of melamine (n(C3H6N6)/n(Fe)) on the removal efficiency of Fe (III) was investigated at various reaction temperatures and reaction time. Figure 3 shows that the dosage of melamine had a significant effect on the removal efficiency of Fe (III). Only 33% Fe (III) was adsorbed at reaction time of 60 min with n(C3H6N6)/n(Fe) = 0.5, while the removal efficiency increased up to 95% as the dosage of melamine increased to n(C3H6N6)/n(Fe) = 3.5. There were not enough vacant sites for Fe (III) adsorption on the surface of melamine at n(C3H6N6)/n(Fe) = 0.5, and the vacant sites increased with the increase of melamine dosage. Thus, the removal efficiency was increased with continuous increase of dosage of melamine, which meant the higher the dosage of melamine, the greater the removal efficiency was.
The effect of reaction temperature on the removal efficiency was also investigated. It can be seen from Figure 3 that the removal efficiency was promoted obviously as the reaction temperature increased, and reached up to 99% at the reaction temperature of 90 °C with n(C3H6N6)/n(Fe) = 3.5. Diffusion rate of Fe (III) increased and the viscosity of the solution decreased along with the increase of reaction temperature, which was beneficial for the contact of Fe (III) and melamine, and contributed to high removal efficiency of Fe (III).
Figure 3 also summarizes the effect of reaction time on the removal efficiency. The reaction time had a positive effect on removal of Fe (III) at low melamine dosage: the removal efficiency of Fe (III) increased with the increase of reaction time. As melamine dosage increased, the effect of reaction time was weakened and the removal efficiency of Fe (III) was up to 96% at 15 min when the melamine dosage was n(C3H6N6)/n(Fe) = 3.5. The vacant sites on the surface of melamine were enough at high melamine dosage, and the Fe (III) ions could easily be adsorbed on the active sites. With the increasing of reaction time, the adsorption sites of melamine would reach saturation, the adsorption rate decreased and the removal efficiency increased slowly.
From the above analysis, it was found that the effect of melamine dosage was more significant than other factors. The removal efficiency of Fe (III) was up to 99% under the optimal conditions: melamine dosage at n(C3H6N6)/n(Fe) = 3.5:1, reaction time of 60 min and reaction temperature of 90 °C.
Response surface methodology
Model fitting
Perturbation plot for the removal efficiency of Fe (III) in the design space. A: n(C3N6H6)/n(Fe); B: reaction temperature; C: reaction time.
The variance analysis of the polynomial equation is shown in Table 3. If the p-value was less than 0.05, we could think that the fitting degree was better. The index p-value of the model was <0.0001, indicating that the simulation effect of the model on this reaction system was significant and had a good regression effect.
Analysis of variance for the response
Source . | Sum of squares . | Df . | Mean square . | F-value . | p-value prob > F . |
---|---|---|---|---|---|
Model | 31.30 | 9 | 3.48 | 81.57 | <0.0001 |
A | 25.60 | 1 | 25.60 | 600.40 | <0.0001 |
B | 3.32 | 1 | 3.32 | 77.82 | <0.0001 |
C | 0.60 | 1 | 0.60 | 14.11 | 0.0071 |
AB | 0.91 | 1 | 0.91 | 21.28 | 0.0024 |
AC | 0.27 | 1 | 0.27 | 6.43 | 0.0389 |
BC | 0.013 | 1 | 0.013 | 0.30 | 0.6026 |
AA | 7.251 × 10−3 | 1 | 7.251 × 10−3 | 0.17 | 0.6924 |
BB | 0.57 | 1 | 0.57 | 13.33 | 0.0082 |
CC | 4.363 × 10−4 | 1 | 4.363 × 10−4 | 0.010 | 0.9223 |
Residual | 0.30 | 7 | 0.043 | – | – |
Lack-of-fit | 0.03 | 3 | 0.099 | – | – |
Pure error | 0.000 | 4 | 0.000 | – | – |
Source . | Sum of squares . | Df . | Mean square . | F-value . | p-value prob > F . |
---|---|---|---|---|---|
Model | 31.30 | 9 | 3.48 | 81.57 | <0.0001 |
A | 25.60 | 1 | 25.60 | 600.40 | <0.0001 |
B | 3.32 | 1 | 3.32 | 77.82 | <0.0001 |
C | 0.60 | 1 | 0.60 | 14.11 | 0.0071 |
AB | 0.91 | 1 | 0.91 | 21.28 | 0.0024 |
AC | 0.27 | 1 | 0.27 | 6.43 | 0.0389 |
BC | 0.013 | 1 | 0.013 | 0.30 | 0.6026 |
AA | 7.251 × 10−3 | 1 | 7.251 × 10−3 | 0.17 | 0.6924 |
BB | 0.57 | 1 | 0.57 | 13.33 | 0.0082 |
CC | 4.363 × 10−4 | 1 | 4.363 × 10−4 | 0.010 | 0.9223 |
Residual | 0.30 | 7 | 0.043 | – | – |
Lack-of-fit | 0.03 | 3 | 0.099 | – | – |
Pure error | 0.000 | 4 | 0.000 | – | – |
Response surface analysis
In order to further evaluate the fitting effect of the model on the experimental data, some important diagnostic plots including internally studentized residuals against run number (ISRRN), predicted against actual (PA), internally studentized residuals against predicted (ISRP) and normal probability against internally studentized residuals (NPISR), respectively, are shown in Figure 5.
Diagnostic plots of quadratic model: (a) normal probability against internally studentized residuals; (b) predicted against actual; (c) internally studentized residuals against run number; (d) internally studentized residuals against predicted.
The NPISR plot displayed in Figure 5(a) shows all points were approximately concentrated in a straight line, which illustrated that the error terms were normally distributed and independent of each other. In the plot of Figure 5(c) and 5(d), whether it was the predicted value or the experimental value of 17 runs, their residuals were randomly distributed between +3.00 and −3.00, indicating that the model successfully established the relationship between the independent variable and the removal efficiency. The plot of PA displayed in Figure 5(b) shows that the points were approximately distributed on a straight line with a slope of 1, which indicated that this model could accurately predict the actual value.
The 3D plots for the effect of interaction between independent factors on removal efficiency of Fe (III) can be seen in Figure 6. According to the 3D response, the degree of influence of the variable could be judged. As far as the influence of individual variables was concerned, the three factors all had great effects on the removal efficiency. It was clear that removal efficiency increased with the n(C3N6H6)/n(Fe) and then reached the maximum, as shown in Figure 6.
Kinetics analysis
The obtained constant coefficients of the kinetics models for Fe (III) adsorption onto melamine are shown in Table 4. The kinetics of adsorption and extent of adsorption at equilibrium were easily affected by the physical and chemical characteristics of the adsorbent and adsorbate and experimental conditions. The kinetics adsorption also allowed estimation of sorption rates. The kinetics study is necessary to select the optimum operating conditions at full scale. Also, useful information about adsorption rate prediction and interpretation, required for designing and modeling of the adsorption process, can be obtained from kinetics study. For the second-order kinetics model, the correlation coefficients were greater than 0.99. The values of pseudo-second-order coefficients (qe and K) confirmed that the adsorption kinetics were dictated through the amount of Fe (III) adsorbed at equilibrium condition and the amount of Fe (III) adsorbed on the surface of melamine. The results obtained were in line with some research (Peng et al. 2017a, 2017b).
Constants and correlation coefficients of pseudo-second-order kinetics models for adsorption of Fe (III) onto melamine (3.0:1)
Temperature . | qe (mg/g) . | K . | R2 . |
---|---|---|---|
30 | 260.22 | 0.00051 | 0.9969 |
45 | 270.56 | 0.00059 | 0.9965 |
60 | 270.57 | 0.00018 | 0.9998 |
75 | 279.93 | 0.00273 | 0.9998 |
90 | 345.22 | 0.00228 | 0.9999 |
Temperature . | qe (mg/g) . | K . | R2 . |
---|---|---|---|
30 | 260.22 | 0.00051 | 0.9969 |
45 | 270.56 | 0.00059 | 0.9965 |
60 | 270.57 | 0.00018 | 0.9998 |
75 | 279.93 | 0.00273 | 0.9998 |
90 | 345.22 | 0.00228 | 0.9999 |
Thermodynamic analysis
Enhancement of the diffusion of adsorbate onto the adsorbent pores, formation of new adsorption sites, the strong bond between adsorbate and active sites of adsorbent and increase of the chemical interaction between Fe (III) and melamine may be the main reasons for improvement of the adsorption capacity of melamine for Fe (III) with temperature increase. Thermodynamic studies are helpful for finding out the nature of adsorption (Wu 2007; Afroze et al. 2016; Konggidinata et al. 2017). The calculated values of ΔS, ΔH and ΔG are shown in Table 5.
Thermodynamic factors of Fe (III) adsorption onto melamine (3.0:1)
Temperature . | ΔG (kJ/mol) . | ΔH (kJ/mol) . | ΔS (J/(mol·K)) . | R2 . |
---|---|---|---|---|
303 K (30 °C) | −0.22 | 23.14 | 77.08 | 0.9138 |
318 K (45 °C) | −1.37 | |||
333 K (60 °C) | −2.52 | |||
348 K (75 °C) | −3.68 | |||
363 K (90 °C) | −4.84 |
Temperature . | ΔG (kJ/mol) . | ΔH (kJ/mol) . | ΔS (J/(mol·K)) . | R2 . |
---|---|---|---|---|
303 K (30 °C) | −0.22 | 23.14 | 77.08 | 0.9138 |
318 K (45 °C) | −1.37 | |||
333 K (60 °C) | −2.52 | |||
348 K (75 °C) | −3.68 | |||
363 K (90 °C) | −4.84 |
The positive value of enthalpy change showed that the adsorption reaction was unspontaneous. In addition, the negative values of the enthalpy change implied that the nature of the adsorption reaction was endothermic. Therefore, the value of the enthalpy parameter showed that the adsorption followed physi-sorption mechanism and the process was not stable energetically. The positive ΔS values confirmed that the adsorption reaction was random at the interface of the liquid/solid phase for the Fe (III) adsorption using melamine. Also, the feasibility and spontaneous nature of the Fe (III) adsorption onto melamine was confirmed with the negative value of ΔG. Decreasing the feasibility and spontaneity degree of the adsorption reaction at high temperature has been illustrated with the decline in ΔG. The values of free energy could show the type of adsorption mechanism (chemical and/or physical mechanism). The value of change in free energy between −20 and 0 kJ/mol indicates physisorption, −20 to −80 kJ/mol indicates physi-chemisorption and −80 to −400 kJ/mol indicates chemisorption. In addition, the value of change in standard enthalpy between 83 and 830 kJ/mol indicates a chemical mechanism and 8–25 kJ/mol indicates a physical mechanism. Thus, the value of ΔG (between −20 and 0 kJ/mol) and ΔH (23.14 kJ/mol) disclosed that the mechanism of adsorption onto melamine was physisorption. Therefore, it could be stated that the adsorption process of Fe (III) onto melamine was unspontaneous, endothermic and physisorption. The positive amount of entropy showed that the system randomness of the solid–solution interface during adsorption increases.
CONCLUSIONS
This paper focused on the adsorption behavior of Fe (III) on melamine. The following conclusions could be obtained.
- (1)
The optimal processing factors were obtained from response surface methodology, and the influence of processing parameters on the removal efficiency of Fe (III) followed the order: mole ratio (n(C3N6H6):n(Fe)) (A) > reaction temperature (B) > reaction time (C). Nearly 99% Fe (III) could be adsorbed under the optimal conditions: melamine dosage at n(C3H6N6)/n(Fe) = 3.5:1, reaction time of 60 min and reaction temperature of 90 °C.
- (2)
The results of the thermodynamic study showed that the adsorption process was unspontaneous and endothermic. The value of free energy change and standard enthalpy change disclosed that the mechanism of adsorption onto melamine was physisorption.
ACKNOWLEDGEMENT
This work was supported by the Science and Technology Research Program of Chongqing Municipal Education Commission (No. KJQN201901403) and the Chongqing Science and Technology Commission (No. cstc2018jcyjAX0018).
AUTHOR CONTRIBUTIONS
Conceptualization, formal analysis and writing – original draft preparation, H. Peng; validation, project administration and funding acquisition, H. Peng; investigation and data curation, J. Guo, B. Wang; writing – review and editing and supervision, H. Peng.
CONFLICTS OF INTEREST
The authors declare no conflict of interest.
DATA AVAILABILITY STATEMENT
All relevant data are included in the paper or its Supplementary Information.