Abstract

Iron contamination, causing staining, discoloration and bad taste, is a worldwide water problem. It is necessary to focus on iron oxidation from the water. This work aims to develop nanobubbles (NBs) technology to remove iron (Fe2+) from aqueous solutions. In batch experiments, the effects of initial Fe2+ concentration, pH, and aeration pressure on the Fe2+ oxidation efficiency were carried out. The results showed that initial concentrations, pH and aeration pressure are significant parameters influencing Fe2+ oxidation. On the basis of single factor experiments, the Box–Behnken design was used to optimize the Fe2+ oxidation conditions with NBs using three parameters (Fe2+ concentration, pH, and aeration pressure) under the response surface methodology. The optimal Fe2+ oxidation was achieved when the initial concentration was 13.7 mg·L−1, pH = 9, and the aeration pressure was 290 kPa. The regression model of Fe2+ oxidation rate under optimized test conditions is accurate and effective. The results showed that the combination of single factor test and response surface optimization can be used to optimize the Fe2+ oxidation process with NBs. It is concluded that NBs technology is promising for Fe2+ oxidation from water.

HIGHLIGHTS

  • Nanobubbles (NBs) were developed to remove iron (Fe2+) from water.

  • The initial Fe2+ concentrations, pH, and aeration pressure significantly affected Fe2+ oxidation.

  • The Box–Behnken design was used to optimize the iron oxidation conditions in NBs system.

Graphical Abstract

Graphical Abstract
Graphical Abstract

INTRODUCTION

With the increase in industrial development, groundwater pollution is increasing worldwide. Iron is an indispensable basic element for human and animals. Its deficiency can cause changes in children's physical activities and mental development (Demlie et al. 2014). However, high iron can cause water pollution and many risks to human health, such as heart and liver damage, diabetes, liver cirrhosis, cartilage calcium, and other diseases. For water supply, iron (Fe2+) causes staining, discoloration, bad taste, corrosion of pipes, and increase fouling and clogging in the pipelines, thereby improving bacterial growth and affecting water quality (Xie et al. 2018). For industries, such as printing, dyeing, and papermaking, high iron in water can decrease the quality of the products. According to the current Sanitary Standard for Drinking Water of China (GB5749-2006), iron in drinking water should not be higher than 0.3 mg·L−1 (Wang et al. 2018). Therefore, more attention must be paid to iron removal from water.

A common iron treatment for groundwater uses the processes of oxidation followed by sedimentation or filtration. The chemicals for iron oxidation include potassium permanganate (KMnO4), chlorine, and ozone (Choo et al. 2005; Phatai et al. 2014). KMnO4 is more expensive than other oxidants and it is important to control the dosage to ensure all the iron has been oxidized and prevent the formation of pink sediment. Chlorination may cause trihalomethanes (THMs) formation, which can cause serious problems to human health. The oxidation efficiency of ozone may be obviously reduced by the humic or fulvic substances in water (Chaturvedi & Dave 2012). Therefore, a suitable oxidizing agent for iron is oxygen without the addition of chemicals. The natural oxidation process takes a long time and requires large-scale equipment, resulting in increased costs. It is important to have a simple operation with high oxidation efficiency and sustainable development for iron pre-oxidation technology.

In the past decades, nanobubbles (NBs) have been used in many water or wastewater treatment processes. The lead (Pb(II)) adsorption process of activated carbon can be accelerated by 366% using air NBs (Kyzas et al. 2019). The combination of precipitation and flotation with micro-bubbles and NBs can remove 81% calcium ions (Ca2+) and 91% magnesium ions (Mg2+) from water (Silva et al. 2020). Using a micro-nano bubble aeration device to treat domestic sewage, the removal efficiency of chemical oxygen demand (COD), suspended solids (SS) and ammonia nitrogen (NH3–N) can be increased by 4.4%, 7.3%, and 15%, respectively (Ghadimkhani et al. 2016). Quiñones (Quiñones & Flores 2018) concluded that the chloroform in wastewater can be reduced from 0.8 mg·L−1 to 0.2 mg·L−1 by NBs. The supplementation of N2 NBs, air NBs, and CO2 NBs increased methane yields by 22%, 18%, and 10%, respectively, in the process of anaerobic digestion (AD) of refractory cellulose because cellulase activity was elevated (Ho et al. 2020; Wang et al. 2020c). After 5 days of NBs aeration in an urban black-odor river, dissolved oxygen (DO) improved from 0.60 mg·L−1 to over 5.00 mg·L−1, and COD and NH4-N removal were increased by 50% (Wu et al. 2019). O2 NBs can enhance photodegradation of oxytetracycline to 98% at pH 11.0 (Wang et al. 2020b). O2 NBs-modified mineral reduced the release of total phosphorus (TP), total nitrogen (TN), and NH3-N from sediments in eutrophic lakes due to the improvement of DO levels near the sediment–water interface and also decreased TP, TN and NH3-N fluxes loading by 96.4%, 24.9% and 51.1% (Wang et al. 2020a). The advantages of using NBs include the extraordinary longevity, good stability, large specific surface area, high gas transfer rate, and release a large amount of free radicals (Ahmed et al. 2018; Atkinson et al. 2019). NBs have great potential to improve or replace the current technologies without the addition of chemicals. However, as a high-efficiency green technology, NBs have not been used to remove Fe2+. Therefore, developing a NBs method for oxidizing Fe2+ in water is the purpose of this work.

The Fe2+ oxidation from water may depend on many parameters using NBs. In order to study the influence of different variables on the treatment, obtain the best operating conditions for Fe2+ oxidation, and reduce the number of experiments, the response surface method (RSM) was used to optimize the process and experimental design. RSM can fully consider the interactional influence of different operating parameters (Tasaki et al. 2009; Moorthy et al. 2015; Temesgen et al. 2017). RSM can also be used to find the optimal combination of parameters and evaluate the influence of various factors in multivariable complex systems by establishing an empirical numerical model and analyzing regression. Box–Behnken design (BBD) is one of the most effective RSMs (Dhiman et al. 2017). Many researchers have applied BBD technology to optimize various types of processes, such as antibiotics adsorption by activated carbon, Acid Black 1 dye oxidation by microalgae, and Pb(II) oxidation by pistachio (Yetilmezsoy et al. 2009; Kousha et al. 2012; Teixeira et al. 2019). However, no publications has focused on investigating the response surface modeling of Fe2+ oxidation in water by NBs using BBD. Experimental analysis using BBD is a special research field for developing NBs technology for Fe2+ oxidation, as well as achieving optimum oxidation efficiency.

This work investigated Fe2+ oxidation from water and examined the influence of initial Fe2+ concentration, pH, and aeration pressure on the oxidation efficiency using NBs. The three-factor BBD was applied to optimize the operation conditions of Fe2+ oxidation using NBs with the comprehensive evaluation index of Fe2+ oxidation.

METHODS

Preparation of NBs

NBs are generated in the Fe2+ solution by aerating with O2 (purity 99.999%). The NBs generation system is shown in Figure 1. The Fe2+ solution was prepared using ferric sulfate produced by Tianjin Damao Chemical Reagent Factory. The length, inner diameter, and outer diameter of the NBs ceramic tube (model WFA0.1-Refractron, USA) are 51 mm, 8 mm, and 13 mm, respectively, with many 100 nm pores.

Figure 1

Design of NBs generation system and scanning electron microscopy image of the ceramic tubular filter.

Figure 1

Design of NBs generation system and scanning electron microscopy image of the ceramic tubular filter.

Batch experiments

At 25 °C, the effects of initial Fe2+ concentration (1.5 mg·L−1–15 mg·L−1), pH (4.0–9.0), and aeration pressure (100 kPa–400 kPa) on the Fe2+ oxidation were investigated. The solution pH was adjusted using 0.1 M NaOH and 0.1 M HCl. Under the continuous aeration with a NBs system, a water sample was taken every 30 min to determine the Fe2+ concentration. The phenanthroline spectrophotometry method was used to determine Fe2+ concentration using an atomic absorption spectrophotometer.

Box–Behnken response surface optimization

In order to obtain the optimum conditions of Fe2+ oxidation using NBs, the BBD with three factors (pH, initial Fe2+ concentration, and aeration pressure) and three levels (−1, 0, 1) were used to design and analyze the experiments. The corresponding design coded is shown in Table 1. The comprehensive evaluation index is Fe2+ oxidation. The design and statistical analysis were carried out using the Design-Expert 8.0.6 software. Regression models were obtained.

Table 1

Experimental coded factors and levels in the BBD

Factors and coded factorsRanges and levels
Low levelCenter levelHigh level
pH, A 4.0 6.5 9.0 
Fe2+ concentration (mg·L−1), B 12 15 
Aeration pressure (kPa), C 100 250 400 
Factors and coded factorsRanges and levels
Low levelCenter levelHigh level
pH, A 4.0 6.5 9.0 
Fe2+ concentration (mg·L−1), B 12 15 
Aeration pressure (kPa), C 100 250 400 

RESULTS AND DISCUSSION

Batch experiments

The results in Figure 2(a) show that Fe2+ oxidation using NBs was highly dependent on pH. The Fe2+ oxidation efficiency progressively increased when pH was enhanced from 4.0 to 9.0 at aeration pressure of 300 kPa and the initial Fe2+ concentration of 15 mg·L−1. The oxidation rate of Fe2+ was higher with an increase in pH, which is consistent with other reports (Sharma et al. 2005a, 2005b). At pH 4, 6, 8, and 9, the maximum oxidation rates of Fe2+ were 36.2%, 46.8%, 98%, and 98.8%, respectively.

Figure 2

Iron removal under different reaction conditions: (a) pressure = 300 kPa, pH = 9, and different initial Fe2+ concentration; (b) pressure = 300 kPa, initial Fe2+ concentration = 15 mg·L−1, and different pH; (c) pH = 9, initial Fe2+ concentration = 15 mg·L−1, and different pressures.

Figure 2

Iron removal under different reaction conditions: (a) pressure = 300 kPa, pH = 9, and different initial Fe2+ concentration; (b) pressure = 300 kPa, initial Fe2+ concentration = 15 mg·L−1, and different pH; (c) pH = 9, initial Fe2+ concentration = 15 mg·L−1, and different pressures.

Figure 2(b) shows the Fe2+ oxidation at aeration pressure of 300 kPa and pH 9. There is no obvious difference in oxidation efficiency (98.3–98.8%) when Fe2+ concentration was 9, 12, and 15 mg·L−1. However, increasing Fe2+ concentration from 1.5 to 6 mg·L−1 slightly affected the oxidation efficiency (92.3%, 95.2%, and 97.6%, respectively).

The effect of aeration pressure on the Fe2+ oxidation is presented in Figure 2(c) at pH 9 and initial Fe2+ concentration of 15 mg·L−1. The oxidation is promoted with increasing aeration pressure. During the first 120 min, the Fe2+ oxidation is higher. After 120 min, there was almost no change in the oxidation efficiency under different pressure.

At lower pH, Fe2+ can combine with water to form hydrated ions Fe(H2O)6+, thereby decreasing its oxidation. This has been confirmed scientifically. Electron transfer between the oxidant and the reducing agent is needed to complete the redox reaction. The central ion of Fe(H2O)6+ is surrounded by six H2O molecules, which shield the electron migration between O2 molecules and Fe2+. Therefore, the oxidation of Fe(H2O)6+ is very slow. When the solution pH increases, OH can capture H+ in the water molecules around Fe2+ and promote the hydrolysis of Fe(H2O)6+ ions. The hydrolysis products of the ferrous ion are separated due to the neutralization of the electrical properties of the central ion, which weakens the polarization of the coordinated water molecules. Therefore, the shielding effect of the coordination water disappears and the oxidation rate of Fe2+ increases.

The injection pressure can promote the dissolution of O2 in water. When the pressure increased, the size of the generated nanobubbles declined and their specific surface area was enhanced. The mass transfer efficiency was improved. In the first 120 min, the amount of NBs increased with increasing pressure. The number of OH radicals and DO in the solution increased, which enhanced the oxidation rate of Fe2+. When the reaction lasted for 120 min, the OH and DO reached saturation, and the reaction rate became stable.

Box–Behnken experimental design and variance analysis

The Box–Behnken experimental design, and the experimental and predicted Fe2+ removal efficiency using NBs are presented in Table 2. To determine the impact of operating parameters on the response, the experimental values were fitted to the following second regression equation:
formula
(1)
where Y is the response; a0 refers to a constant; a1, a2, a3 are linear influences of factors; a12, a23, a13 are interactive effects of the factors; and a11, a22, a33 are quadratic influences of factors. A, B, C refer to pH, initial Fe2+ concentration, and aeration pressure, respectively.
The quadratic model analyzed by the Design-Expert software is given in the following equation:
formula
(2)
Table 2

Experimental designs, and the actual and predicted responses for Fe2+ removal using NBs

RunReal (coded) value for variables
Response (Fe2+ removal efficiency, %)
ABCExperimentalPredicted
6.5 (0) 12 (0) 250 (0) 47.50 47.52 
4.0 (−1) 15 (1) 250 (0) 46.72 46.69 
6.5 (0) 15 (1) 100 (−1) 33.00 32.88 
9.0 (1) 12 (0) 100 (−1) 99.50 99.70 
6.5 (0) 12 (0) 250 (0) 47.50 47.52 
9.0 (1) 12 (0) 400 (1) 97.79 97.64 
6.5 (0) 12 (0) 250 (0) 47.58 47.52 
6.5 (0) 9 (−1) 100 (−1) 29.40 29.17 
6.5 (0) 12 (0) 250 (0) 47.33 47.52 
10 9.0 (1) 9 (−1) 250 (0) 92.30 92.33 
11 9.0 (1) 15 (1) 250 (0) 93.07 92.99 
12 4.0 (−1) 9 (−1) 250 (0) 26.50 26.58 
13 4.0 (−1) 12 (0) 400 (1) 43.80 43.60 
14 4.0 (−1) 12 (0) 100 (−1) 41.54 41.69 
15 6.5 (0) 12 (0) 250 (0) 47.67 47.52 
16 6.5 (0) 15 (1) 400 (1) 39.26 39.49 
17 6.5 (0) 9 (−1) 400 (1) 22.32 22.44 
RunReal (coded) value for variables
Response (Fe2+ removal efficiency, %)
ABCExperimentalPredicted
6.5 (0) 12 (0) 250 (0) 47.50 47.52 
4.0 (−1) 15 (1) 250 (0) 46.72 46.69 
6.5 (0) 15 (1) 100 (−1) 33.00 32.88 
9.0 (1) 12 (0) 100 (−1) 99.50 99.70 
6.5 (0) 12 (0) 250 (0) 47.50 47.52 
9.0 (1) 12 (0) 400 (1) 97.79 97.64 
6.5 (0) 12 (0) 250 (0) 47.58 47.52 
6.5 (0) 9 (−1) 100 (−1) 29.40 29.17 
6.5 (0) 12 (0) 250 (0) 47.33 47.52 
10 9.0 (1) 9 (−1) 250 (0) 92.30 92.33 
11 9.0 (1) 15 (1) 250 (0) 93.07 92.99 
12 4.0 (−1) 9 (−1) 250 (0) 26.50 26.58 
13 4.0 (−1) 12 (0) 400 (1) 43.80 43.60 
14 4.0 (−1) 12 (0) 100 (−1) 41.54 41.69 
15 6.5 (0) 12 (0) 250 (0) 47.67 47.52 
16 6.5 (0) 15 (1) 400 (1) 39.26 39.49 
17 6.5 (0) 9 (−1) 400 (1) 22.32 22.44 

This regression equation suggested that the Fe2+ removal efficiency was affected by the variables. The effective analysis of variance (ANOVA) is shown in Table 3 to evaluate the significance of the model. When P>F-value is less than 0.05, the model is significant under experimental conditions. When P>F-value is less than 0.01, the model is extremely important. For Fe2+ removal with NBs, the quadratic model is very significant. Only the aeration pressure has no linear effect on the response due to the higher P>F-value (0.6724) and the lower F-value (0.19). All interactive and quadratic effects of variables are highly significant to NBs system. The P>F-value (0.0645) for lack-of-fit is greater than 0.05, suggesting that the quadratic equation is valid for the prediction of all NBs systems. Additionally, the second-order model can explain all the variables in the response because of the high F-value. The linear term of pH is the most influential of all terms due to its F-value of 134,000.

Table 3

Effective ANOVA of RSM equation model for Fe2+ removal

ModeSum of squaresDegree of freedomMean squareF-valueP>F
6,277.6 6,277.60 134,000 <0.001 
215.59 215.59 4,603.24 <0.001 
9.112E-003 9.112E-003 0.19 0.6724 
AB 94.58 94.58 2,019.34 <0.001 
AC 3.94 3.94 84.13 <0.001 
BC 44.49 44.49 949.91 <0.001 
A2 3,395.32 3,395.32 72,495.46 <0.001 
B2 534.36 534.36 11,409.51 <0.001 
C2 116.30 116.30 2,483.10 <0.001 
Residual 0.33 0.047 – – 
Lack-of-fit 0.26 0.088 5.61 0.0645 
Pure error 0.063 0.016 – – 
Cor. total 10,513.41 16 – – – 
ModeSum of squaresDegree of freedomMean squareF-valueP>F
6,277.6 6,277.60 134,000 <0.001 
215.59 215.59 4,603.24 <0.001 
9.112E-003 9.112E-003 0.19 0.6724 
AB 94.58 94.58 2,019.34 <0.001 
AC 3.94 3.94 84.13 <0.001 
BC 44.49 44.49 949.91 <0.001 
A2 3,395.32 3,395.32 72,495.46 <0.001 
B2 534.36 534.36 11,409.51 <0.001 
C2 116.30 116.30 2,483.10 <0.001 
Residual 0.33 0.047 – – 
Lack-of-fit 0.26 0.088 5.61 0.0645 
Pure error 0.063 0.016 – – 
Cor. total 10,513.41 16 – – – 

The high correlation coefficient value (R2 = 0.986) indicated that only 1.4% of the total variation cannot be explained by this model, and the predicted removal efficiency is close to the measured results (Sulaiman et al. 2018). The high adjusted determination coefficient (Radj2 = 0.999) confirmed that the model is of great significance to the NB system. The coefficient of variation (CV) is an important index of model accuracy and reliability (Chi et al. 2012; Bedin et al. 2018). Low CV proves the high reliability of the model. The low CV (0.41%) of this experimental model indicated that the model is very reliable.

Statistical analysis for experimental model

Residual analysis can be used for the diagnosis of response surface optimization model. The residuals distribution and the normal probability are extremely significant to detect error variance homogeneity for checking the model's adequacy (Mohammed et al. 2017). As seen in Figure 3(a), the homogeneity of residual variances is in line with the requirements because the residuals of the model predicted values are independently distributed. By displaying the linear residual fitting curve in Figure 3(b), the experimental model was able to represent the system under experimental conditions. This is confirmed by the plots of the predicted and experimental results in Figure 4(a). The predicted Fe2+ removal efficiency is satisfactorily correlated with the experimental values because the data distribution along the diagnostic line of the model is approximately linear, and the deviation between the predicted and experimental results is small.

Figure 3

Internal t-residual distribution of (a) predicted value and (b) normal value.

Figure 3

Internal t-residual distribution of (a) predicted value and (b) normal value.

Figure 4

(a) Model predicted and experimental Fe2+ removal efficiency; (b) perturbation of variables.

Figure 4

(a) Model predicted and experimental Fe2+ removal efficiency; (b) perturbation of variables.

Figure 4(b) represented the perturbation diagrams of different variables to investigate their effects on response for NB technology. The Fe2+ removal efficiency of the NBs system is controlled by pH (A), initial Fe2+ concentration (B), and aeration pressure (C). Fe2+ removal is highly sensitive to pH (A) on the sharp curve. Initial Fe2+ concentration (B) and aeration pressure (C) have little influence on Fe2+ removal efficiency on the flat curves.

Three-dimensional surface response

The three-dimensional (3D) surfaces graph obtained on the basis of the regression equation is shown in Figure 5 and was used to intuitively check the interactive influence of factors on the dependent variable. The non-linear nature of curves indicated that the interaction of initial Fe2+ concentration, pH, and aeration pressure had an important effect on the Fe2+ removal efficiency.

Figure 5

3D response surface plot for the NBs system: (a) initial Fe2+ concentration and pH at 300 kPa; (b) aeration pressure and pH with initial Fe2+ concentration = 15 mg·L−1; (c) aeration pressure and initial Fe2+ concentration at pH 9.

Figure 5

3D response surface plot for the NBs system: (a) initial Fe2+ concentration and pH at 300 kPa; (b) aeration pressure and pH with initial Fe2+ concentration = 15 mg·L−1; (c) aeration pressure and initial Fe2+ concentration at pH 9.

As seen in Figure 5(a), incremental pH increased Fe2+ oxidation efficiency under constant aeration pressure. The higher Fe2+ concentration cause the oxidation efficiency of Fe2+ to increase to the maximum. In addition, a further increase in Fe2+ concentration led to a decrease in oxidation. At a fixed aeration pressure of 250 kPa, the maximum oxidation efficiency was obtained at pH of 9.0 and Fe2+ of 12 mg·L−1. It can be seen from Figure 5(b) that the Fe2+ removal is enhanced with increasing pH, while aeration pressure has little effect on the response. When the fixed Fe2+ concentration was 12 mg·L−1, the maximum oxidation efficiency was obtained at pH 9.0 and aeration pressure of 250 kPa. It can be seen from Figure 5(c) that the maximum oxidation efficiency was obtained at Fe2+ concentration of 12 mg·L−1, aeration pressure of 250 kPa for a fixed pH of 6.5.

If the regression equation is taken as the partial derivative of each variable and is equal to 0, the quaternary system of first-order equation can be obtained. The virtual values of the corresponding optimization factor level are 1 (pH), 0.47 mg·L−1 (Fe2+), and 0.51 kPa (ventilation pressure). The corresponding actual values are 9.0 (pH), 11.97 mg·L−1 (Fe2+), and 251 5 kPa (aeration pressure). For the purpose of model validation, three other experiments were performed under the optimal conditions. The response of the proposed model was compared with the measured Fe2+ oxidation efficiency, and the results are listed in Table 4. At pH 9.0, Fe2+ concentration of 12 mg·L−1 and aeration pressure of 250 kPa, the predicted value (99.33%) of the theoretical Fe2+ oxidation efficiency regression model was clearly consistent with the measured value (99.21%). This demonstrated that the regression model for describing the Fe2+ oxidation process using NBs was accurate and effective.

Table 4

Predicted values and measured values of Fe2+ removal with NBs

pHFe2+(mg·L−1)Aeration pressure (kPa)Fe2+ removal rate (%)
Measured valuePredicted value
12 250 99.39 99.25 
12 250 99.33 99.18 
12 250 99.27 99.20 
pHFe2+(mg·L−1)Aeration pressure (kPa)Fe2+ removal rate (%)
Measured valuePredicted value
12 250 99.39 99.25 
12 250 99.33 99.18 
12 250 99.27 99.20 

CONCLUSIONS

NBs have the characteristics of long hydraulic retention time, good stability, large specific surface area, self-pressurized dissolution, and can release a large number of free radicals. NBs can be used to degrade pollutants and heavy metals that are difficult to degrade, and to purify water. Therefore, the use of NBs to remove Fe2+ has important social and economic significance. In order to analyze the process of Fe2+ oxidation using NBs and study the influence of various factors on the response value, response surface optimization analysis techniques can be adopted. On the basis of the above experiments, the Box–Behnken response surface optimization method was used to optimize the parameters of NB oxidation, and a corresponding numerical model was established. The regression analysis on variance of the model was carried out. The response surface optimization conditions are pH 9, Fe2+ concentration 12 mg·L−1, and aeration pressure of 250 kPa. The regression model for the Fe2+ oxidation rate under optimized test conditions is accurate and effective. The results show that the combination of single factor test and response surface optimization can be used to optimize the oxidation process of NBs on Fe2+.

ACKNOWLEDGEMENTS

Funded by Science and Technology Plans of Ministry of Housing and Urban-Rural Development of the People's Republic of China, and Opening Projects of Beijing Advanced Innovation Center for Future Urban Design Beijing University of Civil Engineering and Architecture (UDC201703151).

DATA AVAILABILITY STATEMENT

All relevant data are included in the paper or its Supplementary Information.

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