Research on catalytic denitrification by zero-valent iron (Fe0) and Pd-Ag catalyst.

This study primarily focused on how to effectively remove nitrate by catalytic denitrification through zero-valent iron (Fe0) and Pd-Ag catalyst. Response surface methodology (RSM), instead of the single factor experiments and orthogonal tests, was firstly applied to optimize the condition parameters of the catalytic process. Results indicated that RSM is accurate and feasible for the condition optimization of catalytic denitrification. Better catalytic performance (71.6% N2 Selectivity) was obtained under the following conditions: 5.1 pH, 127 min reaction time, 3.2 mass ration (Pd: Ag), and 4.2 g/L Fe0, which was higher than the previous study designed by single factor experiments and orthogonal tests, 68.1% and 68.7% of N2 Selectivity, respectively. However, under this optimal conditions, N2 selectivity showed a mild decrease (69.3%), when the real wastewater was used as influent. Further study revealed that cations (K+, Na+, Ca2+, Mg2+, and Al3+) and anions (Cl-, HCO3-, and SO42-) exist in wastewater could have distinctive influence on N2 selectivity. Finally, the reaction mechanism and kinetic model of catalytic denitrification were further studied.

Introduction

Contamination with nitrate (NO3-) in water resource has attracted increasing public concern. Nitrate detected in water body is a common contaminant that may cause severe health risks, such as blue baby syndrome, cancer, as well as the eutrophication of water bodies [1]. Agricultural activities (mainly the over-fertilization of nitrogenous fertilizers), atmospheric deposition, and sewage discharges mainly contribute to nitrate pollution [2].

Several technologies have been developed for treatment of nitrate-contaminated water, including physico-chemical denitrification (such as ion exchange, reverse osmosis, chemical precipitation, and electrocoagulation), biological treatment, and chemical reduction [3]. Among these approaches, biological denitrification, and catalytic hydrogenation enable to selectively reduce nitrate to nontoxic nitrogen (N2) [4, 5]. However, the biological method requires intensive maintenance, excessive biomass disposal, and constant addition of carbon resources [6]. In recent years, the technology of chemical catalytic reduction of nitrate attracts more attention. In 1989, Vorlop and Tacke first put forward the traditional chemical catalytic hydrogenation that utilized the reductant H2 and bimetal catalyst for nitrate reduction [7]. In this catalytic process, catalyst plays the indispensable role, while H2 has been regarded as the reductant, which provides the active H that can participate in the deoxidation process of the nitrate reduction. However, the low solubility of H2 in aqueous media and the operational complexity (appropriate H2 flow rate, pressure) have been the big problem [8]. Several researchers replaced H2 with organic acid (e.g., HCOOH) or its salt (e.g., NaCOOH) to convert nitrate to N2 [9]. However, the incomplete decomposition of acid or its salt and the threat to human health greatly restricts its wide application. Based on these, the novel synergistic effect of zero-valent iron (Fe0) and bimetallic catalyst for nitrate reduction was proposed.

The experimental design for evaluating and optimizing experimental parameters can minimize costs and maximize desired responses [10, 11]. For most researchers, the single factor experiments and orthogonal tests have been widely used for experimental design. However, these two methods are incapable of getting true optimal conditions due to ignoring the interactions among influential variables [12]. Therefore, instead of these two methods, Response Surface Methodology (RSM) was utilized for the optimization of catalytic denitrification conditions in this paper. RSM is a particular set of mathematical and statistical approach that develops for building models, evaluating the effects of variables, and determining the optimal conditions of variables [13]. This method contributes to completing the comprehensive design with a minimum number of experiments, analyzing the interaction between the parameters, and more directly and accurately obtaining the optimal operation parameters [14].

Actually, until now, RSM has not been used as an optimization tool for catalytic reduction of nitrate. Hence, in this research, as a design framework in RSM, Box-Behnken Design (BBD) was used to model and optimize the processes of catalytic denitrification achieved by zero-valent iron (Fe0) and Pd-Ag catalyst. Finally, the reaction mechanism of catalytic denitrification was comprehensively illustrated.

Material and methods

Materials

The chemical reagents used in this research were: sodium nitrate (NaNO3), silver nitrate (AgNO3), palladium chloride (PdCl2), hydrochloric acid (HCl), iron powder (<0.07 nm, >98%), graphene, SiO2, diatomite, kaolin, γ-Al2O3, and silica gel. The catalyst (Pd-Ag/graphene) can be obtained through the traditional wet impregnation method [15].

Experimental design

Batch experiments were completed to investigate the potential factors that may impact catalytic performance. All tests were performed in a 1 L plexiglas reactor (Fig 1). Certain amounts of Fe0 and catalysts were added to the reactor prior to the experiments. To guarantee the better mass transfer effect for catalytic denitrification, the reactor was placed on an magnetic stirrer under 450 rpm at room temperature (20±5°C). 1 mol/L HCl was added to reactor by one automatic titrator to remain needed solution pH during catalytic process.

Fig 1

Schematic of the reactor (1: Influent; 2: Magnetic stirrer; 3: Rotor; 4: Reactor; 5: Effluent; 6: Thermometer; 7: pH meter; 8: Automatic titrator).

Samples were periodically collected to determine the concentration of nitrate-nitrogen (NO3—N), nitrite-nitrogen (NO2—N), ammonium (NH4+-N) and total nitrogen (TN) after 0.45 μm membrane filtration. NO3-, NO2- and TN were measured with an ion chromatograph (DIONEX-120), while NH4+ was tested via the Nessler’s reagent spectrophotometry.

The N2 selectivity was calculated as:

Where C is the initial nitrate concentration (mg/L), C is the nitrate concentration (mg/L) at time t (min), is the content of N2 (mg/L).

(1) Box-Behnken design (BBD)

Results and discussion

RSM analysis

BBD was used for experimental design. The levels of BBD were shown in Table 1.

Table 1

Levels of Box-Behnken design.

Factor	Levels
	-1	0	+1
pH(X 1 )	4.1	5.1	6.1
Time/min ((X 2 )	90	120	150
Pd:Ag mass ratio(X 3 )	2:1	3:1	4:1
Fe0 dosage/g/L(X4)	3	4	5

(2) Regression equation fitting and analysis of variance (ANOVA)

Minitab 19 was applied to the multiple regression fitting. The experiments were conducted and the quadratic multinomial regression equation was listed as follows, and the regression equation coefficients and T test can be seen in Table 2:

Table 2

Regression equation coefficients and T test.

Term	Coefficient	Standard error coefficient	T-Value	P-Value
Constant	69.67	1.12	62.14	0.000
X 1	0.583	0.561	1.04	0.019
X 2	1.000	0.561	1.78	0.100
X 3	1.833	0.561	3.27	0.007
X 4	1.750	0.561	3.12	0.009
X1 X1	-17.708	0.841	-21.06	0.000
X1 X2	-2.500	0.971	-2.57	0.024
X1 X3	0.500	1.14	0.44	0.670
X1 X4	-1.250	0.971	-1.29	0.222
X2 X2	-2.833	0.841	-3.37	0.006
X2 X3	0.750	0.971	0.77	0.455
X2 X4	1.250	0.971	1.29	0.222
X3 X3	-2.833	0.841	-3.37	0.006
X3 X4	0.250	0.971	0.26	0.801
X 4 X 4	-1.958	0.841	-2.33	0.038

Note: P < 0.05, significant level; P > 0.05, below significant level [16].

Y (N2 selectivity) = 69.67 + 0.583 X1 + 1.000 X2 + 1.833 X3 + 1.750 X4−17.708 X1*X1−2.833 X2*X2−2.833 X3*X3−1.958 X4*X4 + 2.500 X1*X2 + 1.000 X1*X3−1.250 X1*X4 + 0.750 X2*X3 + 1.250 X2*X4+ 0.250 X3*X4

As depicted in Table 2, the linear term- X1, X3 and X4, the interaction terms- X1X2, X1X4, and all the square terms- X1X1, X2X2, X3X3 X4X4 remarkably affect test results (P < 0.05). Whereas, X1, X2, X1X3, X1X4, X2X3, X2X4, X3X4 have no significant impact on the experimental results.

As exhibited in Table 3, P-value = 0.000 <0.01, R2 = 90.47, which prove the model built above accurately and the regression equation obtained has been better fitted [17]. Therefore, it comes to the conclusion that this model can be used to continuously analyze and predict experimental data.

Table 3

Analysis of variance (ANOVA) results of the quadratic experimental model.

Source	DF	Adj SS	Adj MSS	F-Value	P-Value
Model	14	596.935	42.638	8.13	0.000
Linear	4	138.167	34.542	6.59	0.005
X1	1	65.333	65.333	12.46	0.004
X2	1	24.083	24.083	4.59	0.053
X3	1	36.750	36.750	7.01	0.021
X4	1	12.000	12.000	2.29	0.156
Square	4	447.519	111.880	21.34	0.000
X1 X1	1	436.009	436.009	83.16	0.000
X2 X2	1	45.370	45.370	8.65	0.012
X3X3	1	25.037	25.037	4.78	0.049
X4X4	1	17.120	17.120	3.27	0.096
2-way interaction	6	11.250	1.875	0.36	0.892
X1 X2	1	1.000	1.000	0.19	0.670
X1 X3	1	1.000	1.000	0.19	0.670
X1 X4	1	9.000	9.000	1.72	0.215
X2 X3	1	0.250	0.250	0.05	0.831
X2 X4	1	0.000	0.000	0.00	1.000
X3X4	1	0.000	0.000	0.00	1.000
Error	12	62.917	5.243
Total	26	659.852
Lack-of-Fit	10	60.917	6.092	6.09	0.149
Pure error	2	2.000	1.000

R2 = 90.47%.

In addition, in order to validate the model proposed above, the residual plots were checked, listed in Fig 2. It’s believed that randomness and unpredictability are essential components for any valid regression model. Through the residual plots analyses, whether the observed error (residuals) is consistent with stochastic error can be accurately assessed. The residuals should be centered on zero throughout the range of fitted values indicated in Fig 2B and 2D. Random errors assumed to produce residuals should be normally distributed. In other words, the residuals should fall in a symmetrical pattern and have a constant spread throughout the range which can be proved in Fig 2A and 2C.

Fig 2

Residual plots for N2 selectivity.

(3) 3D response surface analyses

3D response surface analyses were further conducted for four factors, including pH, time, Pd:Ag mass ratio, and Fe0 dosage, which can be seen in Fig 3. Response surface and contour plots have been applied to intuitively indicate the influence of various factors on N2 selectivity, so as to find out the optimal parameters and the interaction between the factors [18]. In the contour plots, the central point of the minimum ellipse is the highest point of the response surface. Additionally, the shape of the contour line can reflect the strength of the interaction, and the oval indicates that the interaction between the two factors is significant, while the circle reflects the opposite meaning.

Fig 3

Response surface (Left) and Contour plots (Right) between two factors (a) X1 and X2; (b) X1 and X3; (c) X1 and X4; (d) X2 and X3; (e) X2 and X4; (f) X3 and X4.

As depicted in Fig 3A, compare with others, response surface and contour plots of X1 and X2 on N2 selectivity show the significant influence trend, which is consistent with the data in Table 4. In order to obtain the predicted maximum value through the model we build, the canonical analysis of response surface was conducted, which was listed in Table 4.

Table 4

Canonical analysis of response surface.

Factor	X1	X2	X3	X4	Type of stable point
Coded value	0.13	0.23	0.41	0.23	maximum value
Actual value	5.1	127	3.2	4.2	69.8%

As indicated in Table 4, the predicted maximum value is 69.8%. The actual values of the four factors (X1, X2, X3, and X4) obtained from the coded value are: 5.1 pH, 127 min time, 3.2 Pd: Ag, and 4.2 g/L Fe0, respectively, which are the predicted optimal parameters.

(4) Validation test

The validation experiments were conducted under the predicted optimal parameters: 5.1 pH, 127 min reaction time, 3.2 mass ration (Pd: Ag), and 4.2 g/L Fe0. Results showed that the N2 selectivity of catalytic denitrification reached 71.6%, higher than the study designed by the single factor experiments (68.1%) and orthogonal test (68.7%) in Table 5, which proves that the model used in this research is accurate and can get the true optimal conditions for the catalytic reduction of nitrate.

Table 5

N2 selectivity with different designs.

Design method	pH	Time (min)	Pd:Ag mass ratio	Fe0 dosage (g/L)	N2 selectivity (%)
Single-factor design	5.2	120	3:1	4	68.1
Orthogonal test	4.2	120	3:1	5	68.7
RSM design	5.1	127	3.2:1	4.2	71.6

Simulation experiments of real wastewater

To test the effect of water quality on N2 selectivity, real wastewater obtained from the secondary effluent of a municipal wastewater treatment plant in Beijing, China, was adopted for batch experiments. The properties of water samples were: concentration of NO3−- N: 19.2 mg/L, NO2−- N: 0.1 mg/L, NH4+- N: 0.2 mg/L, TN: 21 mg/L, and pH: 6.7. The catalytic conditions were: 5.1 pH, 127 min reaction time, 4 g/L catalyst: Pd-Ag/graphene, Pd:Ag = 3.2:1, Pd: 5 wt%, and 4.2 g/L Fe0.

As described in Table 6, compared to the artificial solution (NaNO3) as influent, N2 selectivity showed a mild decrease as the real wastewater was used as the influent. This phenomenon may be due to the ions that exist in wastewater. Therefore, the effect of the ions on catalytic performance was further investigated.

Table 6

Water quality analyses of the effluent.

Water sample	pH	NO3--N (mg/L)	NH4+-N (mg/L)	NO2--N (mg/L)	TN (mg/L)	N2 selectivity (%)
Wastewater	8.4	10.2	3.3	0.2	14.7	69.3
NaNO 3	8.2	8.9	3.4	0.1	13.6	71.6

Fig 4A shows the effect of different cations on N2 selectivity for nitrate reduction. 20 mg/L of artificial solutions (Al(NO3)3, Ca(NO3)2, Mg(NO3)2, KNO3, NaNO3) were prepared prior to the experiment, respectively.

Fig 4

Catalytic performances with different cations (a) and anions (b) in solution.

A series of experiments with various nitrate salts as the source of nitrate ions revealed that the catalytic performance increased in the following order: K+ < Na+ < Ca2+ < Mg2+ < Al3+. It has been reported that these cations have different influence on the migration rate of NO3- and OH- in solution [19]. Cations with high valence or small radius seem more likely to have a strong ability to bond with NO3-, preventing NO3- from catalytic reduction. Similarly, the cations in solution tend to strongly adsorb the formed OH- that may have a negative impact on catalytic denitrification, enhancing the separation of OH- from bimetallic active sites on surface of the catalyst and offering suitable space and conditions for a the catalytic reaction [20].

As depicted in Fig 4B, the impact on N2 selectivity with Cl-, SO42-, and HCO3- were respectively investigated. It’s obvious that HCO3- partially contributed to the decrease of catalytic performance. The higher the HCO3- concentration, the worse the catalytic performance was. This result was mainly derived from the fact that HCO3- possesses similar plane structure than NO3-, leading to the competitive adsorption with NO3- on surface of the catalyst, which leads to adverse influence on nitrate reduction [20]. In contrast, due to the different structure, Cl- and SO42- both had little to do with catalytic nitrate reduction [21].

(1) Role of the reductant-Fe0

Reaction mechanism

Fe0 primarily served as electron donor in catalytic process. In general, the catalytic denitrification involved the directional electron transfer from Fe0 to nitrate, which is then converted into non-toxic N2 or less toxic species (NO2- and NH4+) [22]. In practical terms, at the metal active sites at the surface of carrier, the electron that Fe0 lost could bond with H+ in solution and form active H, which took part in the deoxidization process and reduced NO3-, as shown in Fig 5A.

Fig 5

a: Role of Fe0 in catalytic process; b: XRD patterns.

XRD patterns of Fe0 before and after catalytic reaction were exhibited in Fig 5B. It’s obvious to find that magnetite (Fe3O4) and hematite (Fe2O3) were detected on surface of Fe0, which is consistent with the Schlicker’s finding [23]. The possible reaction equations are listed as below:

(2) Catalytic denitrification process

It’s believed that the catalytic reduction of nitrate has been the stepwise processes. As indicated in Eqs (Eq 5–11) [24], H+ receives the electron from Fe0, forming the active H, which takes part in the deoxidization process, converting NO3- to N species (NO2-, NH4+, or N2) [25]. It’s worth noting that more N2 can be produced, only the appropriate H+ concentration in solution has been remained. High H+ concentration may lead to the generation of undesired NH4+, which has to be treated again. Additionally, H+ can also reduce the accumulation of OH- generated with the catalytic processes.

In the catalytic denitrification processes, catalyst composed of the active ingredients and the carrier significantly influences the catalytic performance [26]. The carrier that supports the active ingredients can provide reaction sites for catalytic reaction [27]. In addition, the physico-chemical properties (pore structure, surface area, mechanical strength, and the chemical components) of the carrier determine the dispersion degree of the supported active metal particles (Pd, Ag) that control the processes of adsorption, diffusion, reaction, and desorption of the reactants (mainly NO3-, NO2-) and the products (mainly NH4+, N2) that occurred on the catalyst’s surface, which may greatly affect the catalytic reduction of nitrate [28]. Therefore, the materials that possess the porous structure, larger specific surface area, good adsorptive capacity, and stable physico-chemical properties tend to be selected as the carrier of the catalyst.

In addition, the active ingredients can affect the catalytic performance by directly and indirectly participate in the catalytic reaction. Research found that the active ingredients loaded on the carrier should better comprise of a noble metal (such as Pd or Pt) and an auxiliary element (such as Ag, Cu or In) [29]. The bimetallic- Pd and Ag can active the formed H, which involves in the deoxidization process to reduce nitrate. Actually, Ag-H mainly acts with the reactant-NO3-, producing NO2-. Furthermore, on Pd active sites, the product- NO2- can be continuously reduced to other N species (NO, NH, N2, and NH4+) [30]. The catalytic reaction mechanism is illustrated in Fig 6.

Fig 6

Catalytic process for nitrate reduction.

Kinetic study

Currently, significant research focuses on the kinetics of catalytic hydrogenation. Rare research on the catalytic process using Fe0 and bimetallic catalyst to reduce nitrate was conducted. It can be assumed that the zero-order kinetics and first-order equation of Langmuir-Hinshelwood could be employed to describe this process. According to our previous study, the catalytic denitrification process could be better explained by the first order kinetic model [31]. The kinetic equation could be obtained: y = 247.1x +0.1398, R2 = 0.9975.

It has been suggested that in the process of catalytic denitrification, the produced intermediates such as NO and NH have been negligible [32]. Based on the first-order equation above, the reaction rates are presented in Eqs 12–15. A kinetic study on catalytic denitrification with different catalysts was further conducted, as listed in Table 7.

Table 7

First-order kinetics of catalytic denitrification with different catalysts.

Catalysts	Kinetic equation	R2	Rate constant 102 (min-1)
			k	k1	k2	k3	k4	k5
Pd-Ag/SiO 2	y = 0.0077x+0.9763	0.9972	0.77	0.14	0.43	0.26	0.37	0.53
Pd-Ag/diatomite	y = 0.006x+0.9939	0.9976	0.60	0.08	0.32	0.24	0.32	0.42
Pd-Ag/kaolin	y = 0.0121x+1.0223	0.9968	1.21	0.23	0.79	0.35	0.47	0.86
Pd-Ag/γ-Al 2 O 3	y = 0.0209x+0.8919	0.9977	2.09	0.46	1.12	0.68	0.81	1.24
Pd-Ag/silica gel	y = 0.0094x+0.9799	0.9964	0. 94	0.15	0.61	0.29	0.43	0.73
Pd-Ag/graphene	y = 0.0414x+0.5349	0.9982	4.14	0.88	2.11	1.21	1.32	2.25

Where k1, k2, and k3 are the rate constants for reduction of NO3- to NO2-, N2 and NH4+, respectively; k4 and k5 are the rate constants for reduction of NO2- to NH4+ and N2.

Results indicated that different catalysts performed distinct reaction rates in catalytic denitrification, which can be explained by k value in Table 7. According to the calculation, for each catalytic process, the summation of k1, k2, k3 that stands for the overall reaction rate constant was close to k, which implies the catalytic process is a stepwise process. Results indicated that compared to other catalysts, Pd-Ag/graphene showed a higher catalytic rate, which has been proved by data in Table 2. This may be due to the unique properties of graphene, including the porous structure, active surface area, outstanding electronic properties and promising mechanical and thermal stability [33].

Conclusion

Response surface methodology was used to optimize parameters of catalytic reduction of nitrate. Results indicated that the application of response surface methodology was proved to be feasible. 71.6% of N2 Selectivity was obtained under the optimum conditions: 5.1 pH, 127 min reaction time, 3.2 mass ration (Pd: Ag), and 4.2 g/L Fe0. However, the cations (K+, Na+, Ca2+, Mg2+, and Al3+) and anions (Cl-, SO42-, and HCO3-) in water body performed different influence on catalytic denitrification. Study on reaction mechanism found that the catalytic denitrification can be achieved with deoxidization processes. Additionally, as the components of catalyst, active ingredients (Pd-Ag) and carrier (graphene) played different role in the catalytic denitrification. The catalytic process could be better explained by first order kinetic model.