Kinetic modeling of nalidixic acid degradation by clinoptilolite nanorod-catalyzed ozonation process

Alireza Khataee*, Mehrangiz Fathinia and Tannaz Sadeghi Rad
Research Laboratory of Advanced Water and Wastewater Treatment Processes, Department of Applied Chemistry, Faculty of Chemistry, University of Tabriz, 51666-16471 Tabriz, Iran. E-mail: a_khataee@tabrizu.ac.ir; ar_khataee@yahoo.com; Fax: +98 41 33340191; Tel: +98 41 33393165

Received 19th February 2016 , Accepted 18th April 2016

First published on 20th April 2016


Abstract

The removal of nalidixic acid (NAD) through the clinoptilolite nanorod (CN)-catalyzed ozonation process was modeled by three types of kinetic approaches. By using the glow discharge plasma (GDP) technique, natural clinoptilolite microparticles (NCMs) were successfully converted to CNs. The samples were characterized by scanning electron microscopy (SEM) and Brunauer–Emmett–Teller (BET) analysis. The impacts of ozone inlet flow rate, catalyst concentration, pH and NAD initial concentration were examined to find out the kinetic characteristics of the heterogeneous catalytic ozonation. Based on the intrinsic elementary reactions of the heterogeneous catalytic ozonation process, a novel kinetic model was developed and validated. An empirical kinetic model and an artificial neural network (ANN) model were established in order to appraise the accuracy of the proposed intrinsic kinetic model by a 3-layer feed-forward back-propagation network with the topology of 4[thin space (1/6-em)]:[thin space (1/6-em)]14[thin space (1/6-em)]:[thin space (1/6-em)]1. The error functions and analysis of variance (ANOVA) were used to compare the performance of the three models.


1. Introduction

According to the literature, pharmaceutical and personal care products companies are considered to be a dominant concern, due to the release of significant amounts of non-biodegradable organics and refractory hazardous contaminants into the environment.1 Ecosystem and human health are threatened by the presence of pharmaceuticals in the aquatic environment, which leads to the proliferation of drug-resistant bacteria.2

One of the most detected pharmaceuticals in water and wastewater is NAD, which cannot be thoroughly degraded by typical wastewater treatment processes.3 NAD is a widely spread antibacterial agent, and its residual occurrence through natural water, soils and sediments is confirmed.4

Through recent years, advanced oxidation processes (AOPs) have been developed as effective water treatment methods. AOPs can generate highly reactive oxygen species, like hydroxyl radicals (E° = 2.6 eV), which can be applied for the mineralization of refractory organic pollutants.5 Particularly, as an AOP technique, the heterogeneous catalytic ozonation process has received considerable attention due to its promising results among the high-performance water treatment processes.6–9

One of the most commonly used catalysts in the heterogeneous catalytic ozonation is zeolite. Among zeolites, as an abundant and non-toxic compound, clinoptilolite has been utilized in different catalytic processes.10 In spite of the advantages of clinoptilolite, low specific surface area restricts its usage in heterogeneous catalytic ozonation.11 In order to overcome the limitations and increase the specific surface area of catalysts, distinct methods have been utilized.12,13 In recent decades, the nonthermal plasma technique, as a green method for generating nanomaterials and catalyst surface modification, has been utilized extensively.14–16 According to various researches' results, the morphology of a catalyst can be altered to nanostructures during the GDP technique. Thereby, the specific surface area of a catalyst can be improved.14,15 By using a nanocatalyst, the mineralization rate of refractory organic substances is increased considerably through the catalytic ozonation process for water and wastewater treatment.

Heterogeneous catalytic ozonation process involves free radical, catalyst surface reactions and mass transfer processes; thus, the kinetic modeling of the process is complicated. With the aim of studying the process variables and effective parameters, various kinetic models have been developed.17 However, the validation of the developed models was not completely investigated and evaluated using other kinds of models.

In addition, there are a few studies which focused on the kinetic modeling of the heterogeneous catalytic ozonation process for contaminant decomposition.18,19 As an example, Valdés et al., in their research including the heterogeneous and homogeneous catalytic ozonation of benzothiazole in the presence of activated carbon, reported the contribution of the heterogeneous and homogeneous reactions in benzothiazole decomposition.20 They used the reactions in order to just develop a kinetic model for the degradation process. In this context, Zhang et al. investigated the degradation kinetics of a reactive azo dye by ferrous-catalyzed ozonation in a bubble column reactor. The obtained results indicated that the degradation process was well fitted by an irreversible second-order kinetic model.21 Pocostales et al. studied the ozonation of sulfamethoxazole catalyzed by a powder activated carbon. They proposed a mechanism that involved both homogeneous and heterogeneous reactions for the mineralization of the pharmaceutical. Also, regarding the proposed mechanism a mathematical kinetic model was developed using the corresponding mass balances of the main species present.22 Andreozzi et al. investigated the ozonation of pyruvic acid in aqueous solution, catalyzed by Mn(II) and Mn(IV) ions, at three different pH values (1.1, 2.0 and 3.0). A mathematical model of the process was developed taking into account the reactions occurring in the liquid phase and the ozone mass transfer from the gas bubbles. The reactions proposed were then used to establish two alternative kinetic models. The two kinetic models correlated with the experimental data with a fair accuracy at the lowest and at the highest pH values examined.23

In the present study, CNs were modified by the GDP technique and applied as a heterogeneous catalyst in ozonation of NAD. The characterization of the prepared catalyst was conducted by SEM and BET analysis. Based on the intrinsic elementary reactions of the heterogeneous catalytic ozonation process, an innovative kinetic model was established for the degradation of NAD. In addition, the accuracy and precision of the developed model were validated and evaluated using the experimental data and empirical and ANN modeling, respectively.

2. Experimental

2.1. Reagents and materials

It is important to mention that the NAD was purchased from Rouz Darou Co. (Tehran, Iran). The NCMs were prepared from the Mianeh (Kan Azar Co., Tabriz, Iran). All the reagents supplied by Merck Co. (Germany) were of analytical grade and used without any purification.

2.2. Producing CNs by the GDP technique

The primary structure of the GDP reactor was built from a 40 cm × 5 cm dimensions Pyrex tube. In order to generate the GDP, an AC power supply (3000 V, Tabriz, Iran) was applied to two electrodes that were connected to the two ends of the GDP tube. Almost 2 g of dry natural clinoptilolite was situated on a Pyrex plate which was fixed near the positive column area of the GDP reactor.

It should be mentioned that the GDP technique operating conditions were investigated and properly optimized in previous reports.14,24 Ar, O2 and N2 gases were tested as plasma-forming gases and plasma treatment time ranged from 15 to 60 min. The best results were obtained at a treatment time of 60 min, when using N2 as a feeding plasma-forming gas. So, in this work, after evacuating the reactor, N2 gas was introduced to the reactor at a pressure of 53.3 Pa. The duration of the GDP procedure was about 60 min. After that, the process was stopped and CNs were collected for use in the catalytic ozonation process as a catalyst.

2.3. The procedure of heterogeneous catalytic ozonation

The catalytic ozonation experiments were performed in a 250 mL capacity cylindrical Pyrex reactor in a semi-batch mode at atmospheric pressure and ambient temperature. The ozonation reactor consists of an ozone gas inlet, a non-reacted ozone gas outlet, a sampling port and a porous ceramic diffuser. A magnetic stirrer was used in order to mix the NAD solution constantly. As a feed gas for the laboratory ozone generator, oxygen (1–5 L min−1) was supplied by an oxygen generator (Airsep, USA). Ozone gas constantly entered to the solution via a diffuser at the bottom of the reactor. A gas flow meter was utilized to regulate the ozone flow rate from 1 to 7 L h−1. Distinct amount of CNs was added into 250 mL of NAD solution in each typical run.

2.4. Experimental conditions for kinetic analysis

A series of experiments were done using various heterogeneous catalytic ozonation conditions, in order to investigate the dependence of the catalytic ozonation kinetic characteristics on the initial ozone inlet flow rate, catalyst concentration, pH of solution and initial NAD concentration. The initial pH of the solution was adjusted from 3 to 7 by using H2SO4 and NaOH solution. The initial concentration of the NAD was varied from 20 to 50 mg L−1. The ozone inlet flow rate was changed from 1 to 7 L h−1. Table 1 presents the dissolved, inlet and outlet ozone concentrations for each of the applied inlet flow rates.
Table 1 The concentration of ozone for each of the applied ozone inlet flow rates
Ozone inlet flow rate (L h−1) Ozone dissolved concentration (mg L−1) Ozone concentration in the inlet gas (mg L−1) Ozone concentration in the outlet gas (mg L−1)
1 0.10 1.17 1.07
3 0.62 2.37 1.75
5 1.31 3.79 2.48
7 2.16 7.10 4.94


3 mL of the sample was taken at distinct time intervals. To suppress any ozone development or oxidation of hydroxyl radicals before the analytical determination of NAD, 1 mL of ethanol was added to the samples. By using a UV-visible spectrophotometer (Lightwave S2000, England), the absorbance at the maximum wavelength (λmax = 330 nm) was computed and the removal efficiency of the NAD was measured. By concentrating on other studies, it can be understood that the NAD removal efficiency (%) was determined as the percentage ratio of the removed drug concentration to the initial one. Also, the dissolved ozone concentration was determined by the indigo colorimetric method which was proposed by Bader and Hoigné.25

2.5. Analytical methods

SEM analysis (Mira3 FEG-SEM, Tescan, Czech) was applied to investigate the morphology of the samples. The microstructural properties of the samples were investigated by nitrogen sorption analysis with a ChemBET 3000 (Quantachrome, USA) at 77.35 K by using calcined samples.

3. Results and discussion

3.1. Characterizations of CNs by SEM and BET

In order to examine the morphology of CNs, SEM analysis was performed. As can be seen in Fig. 1, rod-shaped nanocrystals and uniform structure of the clinoptilolite with an approximate particle size of 20–40 nm have been formed through the GDP method. The nitrogen adsorption isotherms were measured by using the BET surface area method to find out the specific surface area properties of the samples.26 After treating with plasma, the obtained results indicated that the specific surface area of the clinoptilolite varied from 13.35 to 316 m2 g−1. So, it can be concluded that the GDP method is able to increase the specific surface area of the NCMs significantly.
image file: c6ra04500f-f1.tif
Fig. 1 SEM images of CNs with different magnifications.

3.2. Comparison of different oxidation processes

To investigate the effectiveness of each oxidation process, a series of experiments were carried out. Fig. 2a shows the NAD removal efficiency with increasing reaction time in the catalytic ozonation and adsorption processes. It can be discerned that the removal efficiencies of the adsorption processes in the presence of the NCMs and CNs were about 10% and 20% during 20 min. Thus, the adsorption processes were not able to degrade NAD thoroughly. The removal of NAD was examined by ozonation and catalytic ozonation in the presence of NCMs and CNs at 20 min. The catalytic ozonation processes, in comparison with ozonation alone, show a high removal efficiency. Also, the plasma-treated catalyst improved the removal efficiency of NAD notably (see Fig. 2a). With high specific surface area, it can be concluded that CNs provide more active sites for the ozone molecules to be adsorbed and then decomposed to hydroxyl radicals. The present results are in good agreement with those of Beltrán et al., who investigated the catalytic ozonation of various contaminants.27–29 It can be concluded that the degradation of NAD can be performed by two main pathways, namely oxidation in solution via direct and indirect mechanism and oxidation by the heterogeneous hydroxyl radicals formed on the surface of CNs which are discussed in more detail in Section 3.4.
image file: c6ra04500f-f2.tif
Fig. 2 (a) Removal efficiency of nalidixic acid for the different processes: a, adsorption with NCMs; b, adsorption with CNs; c, ozonation; d, catalytic ozonation with NCMs; e, catalytic ozonation with CNs. (b) The corresponding kinetic analysis assuming pseudo-first-order kinetics for degradation of NAD. Experimental conditions: ozone inlet flow rate: 7 L h−1, [NCMs] = 2 g L−1, [CNs] = 2 g L−1, pH = 7 and [NAD]0 = 20 mg L−1.

Regarding the kinetics of the oxidation process, the pseudo-first-order kinetics depicted in eqn (1) can be used to fit the experimental data of NAD removal efficiency in the various processes:

 
image file: c6ra04500f-t1.tif(1)
where kapp is the apparent pseudo-first-order rate constant, C = C0 at t = 0 and C0 is the initial concentration of the pollutant. Fig. 2b is plotted according to eqn (1). The curves in Fig. 2b have correlation coefficients (R2) of about 0.990 which is evidence of the pseudo-first-order kinetic hypothesis.

3.3. The effects of the various operational parameters on the heterogeneous catalytic ozonation of NAD

In order to study the effect of different operational parameters such as the ozone inlet flow rate, catalyst concentration, pH, and NAD concentration, four series of tests were executed. Fig. 3a–d show plots of ln(C0/C) versus time for all of the tests which were performed. As can be seen in these figures, the correlation coefficients (R2) reported for all of the experimental data of NAD degradation under the different operational conditions were above 0.90 which fitted well with the pseudo-first-order kinetics. Herein, the operational parameters of the heterogeneous catalytic ozonation have a remarkable influence on the NAD removal efficiency.
image file: c6ra04500f-f3.tif
Fig. 3 Linear relationship of NAD degradation during heterogeneous catalytic ozonation process at (a) different ozone inlet flow rates (experimental conditions: [CNs] = 6 g L−1, pH = 7 and [NAD]0 = 20 mg L−1); (b) various catalyst concentrations (experimental conditions: ozone inlet flow rate = 7 L h−1, pH = 7 and [NAD]0 = 20 mg L−1); (c) different pH of solution (experimental conditions: ozone inlet flow rate = 7 L h−1, [CNs] = 2 g L−1 and [NAD]0 = 20 mg L−1); (d) various initial NAD concentrations (experimental conditions: ozone inlet flow rate = 7 L h−1, [CNs] = 2 g L−1 and pH = 7), fitted by pseudo-first-order model. (e) Comparison of the calculated NAD removal efficiency via developed intrinsic kinetic model (eqn (17)) with experimental removal efficiency.

Fig. 3a shows that kapp increases gradually with the ozone inlet flow rate increasing up to 7 L h−1. Also, with an increase of ozone inlet flow rate from 1 L h−1 to 7 L h−1, the dissolved ozone concentration altered from 0.10 to 2.16 mg L−1. Also it is worth mentioning that the increase in the concentration of the ozone molecules leads to an increase in the ozone decomposition on the catalyst surface, which leads to the production of reactive oxygen species such as hydroxyl radicals which are responsible for NAD degradation in the catalytic ozonation process.30,31 The obtained results are compatible with the literature studying ozone inlet flow rate influence on catalytic ozonation.32,33

Fig. 3b illustrates that the pseudo-first-order rate constants increase with increasing catalyst concentration from 0.4 g L−1 to 8 g L−1. It has been well mentioned that more surface active sites of the catalyst are provided by a high concentration of catalyst, and thus more ozone molecules decomposed and converted to hydroxyl radicals.30

Fig. 3c shows that kapp continuously increases with increasing pH value. The cause of this phenomenon can be related to the production of more hydroxyl radicals at a higher pH of the solution due to the faster decomposition of ozone.34,35 Hence, producing more hydroxyl radicals leads to an increase of the NAD removal efficiency.36

Fig. 3d shows that the pseudo-first-order removal rate constant of NAD decreases systematically on increasing the NAD initial concentration. This is because of the fact that under the optimum conditions, the flow rate of ozone is constant and also the amount of ˙OH generated by ozone decomposition is constant. Thus, the limited concentration of ˙OH is not sufficient to degrade all of the pollutants thoroughly and finally the removal efficiency is reduced by increasing the NAD concentration.37

3.4. Reaction mechanism

From the present work, by the catalytic ozonation process, the removal of NAD is assumed to be completed through two main pathways. (i) Degradation is described as direct and indirect reactions in the solution bulk following pseudo-first-order oxidation kinetics. The complete mechanism is discussed in Section 3.4.1 (see Table 2). (ii) Oxidation is explained in Section 3.4.2 by heterogeneous catalytic ozonation process on the surface of CNs via reactive hydroxyl radicals (see Table 3). The secondary oxidation route kinetics is demonstrated as a second-order surface reaction equation.
Table 2 Reactions involved in oxidation of NAD by homogeneous ozonation processa
Reaction no. Elementary reaction Kinetic constant (M−1 s−1) Ref.
a IP: refers to intermediate products, DP: refers to degradation products.
1 O3 + NAD → DP + O3˙    
2 O3˙ + H+ ↔ HO3˙ 5.2 × 1010, k = 3.7 × 104 22,31,38
3 HO3˙ → ˙OH + O2 5.2 × 1010 22,31,38
4 O3 + OH →HO2 + O2 70 22,31,38
5 O3 + HO2 → HO2˙ + O3˙ 2.8 × 106 22,31,38
6 image file: c6ra04500f-t2.tif 3.2 × 105, k = 2.0 × 1010 22,31,38
7 O2˙ + O3 → O3˙ + O2 1.6 × 109 22,31,38
8 2HO2˙ → O2 + H2O2 5.0 × 109 22,31,38
9 2HO3˙ → 2O2 + H2O2 5.0 × 109 22,31,38
10 ˙OH + HO3˙ → O2 + H2O2 5.0 × 109 22,31,38
11 ˙OH + NAD → IP    
12 ˙OH + IP → DP    
13 O3 + ˙OH → HO2˙ + O2 2 × 109 22,31,38
14 H2O2 + ˙OH → HO2˙ + H2O 2.7 × 107 22,31,38
15 HO2 + ˙OH → HO2˙ + OH 7.5 × 109 22,31,38


Table 3 Reactions involved in oxidation of NAD by heterogeneous catalytic ozonation processa
Reaction no. Elementary reaction Kinetic constant (M−1 s−1) Ref.
a S: refers to catalyst surface.
16 NAD + S → NAD − S    
17 O3 + S → O3 − S    
18 O3 − S + OH − S → HO2 + O2 70 22,31,38
19 HO2 + O3 → HO2˙ + O3˙ 2.8 × 106 22,31,38
20 image file: c6ra04500f-t3.tif 3.2 × 105, k = 2.0 × 1010 22,31,38
21 O2˙ + O3 → O3˙ + O2 1.6 × 109 22,31,38
22 O3˙+ H + ↔ HO3˙  5.2 × 1010, k = 3.3 × 102 22,31,38
23 HO3˙ → ˙OH + O2 1.1 × 105 22,31,38
24 ˙OH + O3 → HO2˙ + O2 2 × 109 22,31,38
25 HO2 + ˙OH → HO2˙ + OH 7.5 × 109 22,31,38
26 ˙OH + NAD → IP     
27 ˙OH + IP → DP     


3.4.1. Mechanistic studies of homogeneous ozonation process. A literature review would report that in the ozonation process, fast reacting compounds, containing specific functional groups like double bonds, activated aromatic systems and nucleophilic positions, react selectively with ozone molecules.39 The first intermediates and free radicals are formed by those reactions. Ultimately, hydroxyl radicals are generated by the formation of free radicals. The free radicals are responsible for reacting with NAD and producing intermediates.40

Considering Table 2, reactions 1 to 15 are suggested for homogeneous ozonation of NAD. According to reaction 1, ozonide ion radical is formed by the direct reaction of NAD with ozone molecule. The ozonide ion radical provokes a radical mechanism that ends with hydroxyl free radical production (reactions 2 and 3). As can be seen from reactions 4 and 5, ozone reacts with hydroxyl ions and ionic form of hydrogen peroxide in order to generate the hydroxyl radicals. The hydroperoxide radical (HO2˙) is changed to a superoxide ion (O2˙) by shifting reaction 6 to the right at pH = 7. Finally, via reactions of 10 to 15, hydroxyl radicals commence reacting with NAD, intermediates, ozone and hydrogen peroxide to yield further free radicals, intermediates and so on.

3.4.2. Mechanistic aspects of heterogeneous catalytic ozonation process. The mechanism of the heterogeneous catalytic ozonation process is precisely described in reactions 16 to 27 of Table 3.38,41,42 The first steps, which are presented in reactions 16 and 17, are the adsorption of NAD and O3 molecules on the catalyst surface. The adsorbed ozone molecules then react with catalyst surface hydroxide ions that finally results in HO2 molecule formation (reactions 18 and 19). Herein, the heterogeneous catalytic ozonation process is performed at a pH of 7, and thus reaction 20 is shifted to the right and more hydroxyl radicals are generated. Due to the fact that ozone and hydroxyl radicals react together in homogeneous phase, reaction 24 seems to be of low probability. Consequently, the produced hydroxyl radical is able to react with generated intermediates and NAD molecules.42

3.5. Development of a kinetic model on the basis of intrinsic elementary reactions

The main reactions that participate in the heterogeneous catalytic ozonation of NAD on the surface of CNs are depicted in Table 3. In the present work, in order to simplify the reaction mechanism based on the experimental conditions, the NAD degradation rate expression is deduced from the following hypothesis:

- equilibrium conditions are obtained between the bulk and adsorbed concentrations of NAD;

- in order to calculate the concentration of unstable reactive species such as radicals, the steady state approximation (SSA) is used;

- the reaction between ozone and hydroxyl radical which occurs in homogeneous phase is neglected;

- the recombination of reactive oxygen species with each other is of low probability due to the presence of organic compounds.

The overall reaction rate for the pseudo-first-order homogeneous reaction and second-order catalyst surface reaction of NAD can be represented as eqn (2):

 
image file: c6ra04500f-t4.tif(2)
where rTot is the total reaction rate, V is the reaction volume, N is the moles of NAD, t is the reaction duration, C is the concentration of NAD at time t, khomo is the pseudo-first-order rate constant of the homogeneous ozonation in the bulk solution and khetero is the second-order kinetic constant of the catalytic ozonation reaction among the NAD molecules and hydroxyl radicals.

The total concentration of the adsorbed hydroxyl radicals according to SSA is given in eqn (3):

 
image file: c6ra04500f-t5.tif(3)

Eqn (3) can be rearranged to eqn (4):

 
image file: c6ra04500f-t6.tif(4)

As can be seen from eqn (4), the concentration measurement of [NAD] seems to be difficult.43 Thus, by considering the assumption given in eqn (5) and eqn (4), eqn (4) can be altered to eqn (6).

 
[NAD]ads = [NAD]0 − [IP] ⇒ k26[NAD]ads + k27[IP] = k26[NAD]0k26[IP] + k27[NAD]0k27[NAD]ads ≈ (k26 + k27)[NAD]0 (5)

By substituting eqn (5) in (4), eqn (6) is obtained:

 
image file: c6ra04500f-t7.tif(6)

In order to state the concentration of the radical species, eqn (7)–(10) are used by applying SSA:

 
image file: c6ra04500f-t8.tif(7)
 
image file: c6ra04500f-t9.tif(8)
 
image file: c6ra04500f-t10.tif(9)
 
image file: c6ra04500f-t11.tif(10)

The concentration of [HO2] can be obtained by eqn (11):

 
image file: c6ra04500f-t12.tif(11)

By considering the fact that the ozone inlet flow rate and the specific surface area of catalyst are constant during the experiments, the concentration of OH remains constant, too. Consequently, instead of k18 [OH], k18 can be used and eqn (12) is obtained:

 
image file: c6ra04500f-t13.tif(12)

By replacing eqn (12) in (10), eqn (10) in (9), eqn (9) in (8), eqn (8) in (7) and finally eqn (7) in (6), the expression for the ˙OH concentration is obtained from eqn (13):

 
image file: c6ra04500f-t14.tif(13)

The expression for the total degradation rate of NAD can be written as eqn (14):

 
image file: c6ra04500f-t15.tif(14)

Eqn (14) can be altered to a pseudo-first-order rate equation by considering eqn (15) as a kinetic rate constant:

 
image file: c6ra04500f-t16.tif(15)

Eqn (15) can be simplified to eqn (16) by considering α = khomo, β = 2kheterok18 and γ = k26 + k27 as the constants of eqn (15):

 
image file: c6ra04500f-t17.tif(16)

Eqn (16) elucidates the dependency of the pseudo-first-order kinetic constant on dissolved ozone concentration and initial NAD concentration. Constants of eqn (16) such as α, β and γ were computed by using a least squares curve fit method for the given dissolved ozone concentration and initial NAD concentration and which are given in Table 4. Finally, by combining eqn (1) and (16) and by considering the equation constants, the expression for predicting the NAD removal efficiency (RE) can be defined as eqn (17):

 
image file: c6ra04500f-t18.tif(17)

Table 4 Values of the constants for equation obtained from least square curve fit method
Model Parameter Value
image file: c6ra04500f-t19.tif α 0.0059 (±0.0038)
β 2.1810 (±0.5925)
γ 16.8310 (±2.0970)


In order to assess the performance of the obtained kinetic model (eqn (17)), the dispersion diagram of the predicted NAD removal efficiency calculated from eqn (17) against the experimental removal efficiency is indicated in Fig. 3e. The correlation coefficient (R2) is 0.980 which implies a significant correlation between the experimental and theoretical NAD removal efficiency calculated by using the developed kinetic model (eqn (17)). Based on the obtained R2, it can be proposed that the obtained model can notably represent the real trend and the mechanism of the heterogeneous catalytic ozonation process. It can be deduced that the proposed mechanism and assumptions are valid. Also, the process is modeled by empirical modeling based on nonlinear regression analysis and ANN in the following sections, to evaluate further the efficiency of the developed model (eqn (17)).

3.6. Development of an empirical relation based on nonlinear regression analysis

By considering the experimental results of Section 3.3, it can be deduced that the pseudo-first-order rate constant, kapp, is a function of the ozone inlet flow rate, catalyst concentration, pH and initial NAD concentration. In addition to kinetic modeling of the degradation process based on the intrinsic elementary reactions (Section 3.5), a very simple relation such as the empirical power law type method based on nonlinear regression analysis (eqn (18)) can also be used to describe the relationship among kapp and each of the effective experimental parameters, X:
 
kapp = μi(Xi)θiμia(X1)θ1 × (X2)θ2 ×, …, × (Xn)θn (18)
where μi and θi are the parameters of the model and μia is the average of the calculated μi. By nonlinear regression analysis of the experimental data series, the constants of the model were computed for each operational parameter (X). The results of Fig. 4a–d and 5 show the relation of each experimental parameter with kapp based on nonlinear regression analysis. These results demonstrate that kapp can be empirically correlated to the ozone inlet flow rate, catalyst concentration, pH and NAD initial concentration. The expression for estimating kapp at various experimental conditions can be written as eqn (19), through substituting the obtained constant values given in Table 5 into eqn (18):
 
kapp = 0.254[pH]0.707[ozone inlet flow rate]0.525[NAD]−0.447[catalyst]0.414 (19)

image file: c6ra04500f-f4.tif
Fig. 4 The effect of (a) ozone inlet flow rate, (b) catalyst concentration, (c) pH, and (d) initial NAD concentration on pseudo-first-order kinetic constant (kapp). (e) Comparison of the calculated NAD removal efficiency via developed empirical kinetic model (eqn (20)) with experimental values. For experimental details, refer to Table 5.
Table 5 Values of the constants for equation obtained from nonlinear fitting of experimental data
Parameter Range μ θ R2
pH 3–9 0.0260 (±0.0104) 0.7070 (±0.1357) 0.980
Ozone inlet flow rate (L h−1) 1–7 0.0298 (±0.0072) 0.5250 (±0.1012) 0.971
[NAD]0 (mg L−1) 10–50 0.31050 (±0.1006) −0.4470 (±0.0831) 0.951
Catalyst concentration (g L−1) 0.4–8 0.0526 (±0.0124) 0.4140 (±0.1758) 0.950


Finally, by incorporating the presented eqn (1) and (19), (20) can be formed as an empirical kinetic expression for calculating NAD removal efficiency:

 
REpredicted = 1 − exp(−0.254[pH]0.707[ozone inlet flow rate]0.525[NAD]−0.447[catalyst]0.414 × t) (20)

In order to validate the empirical model (eqn (20)), the experimental NAD removal efficiencies were plotted against the calculated ones and the results are depicted in Fig. 4e. The high correlation coefficient (R2 = 0.998) confirms that the calculated removal efficiency and the experimental data are in good agreement. So, it can be stated that the empirical modeling of the process is a simple and effective method for defining the interdependence of the kinetic rate constants of the process with the main operational parameters.

3.7. Artificial neural network modeling of the catalytic ozonation process

Artificial neural networks (ANNs) are computer-based systems, which are used to predict a model, recognize the patterns of processes and evaluate the accuracy of a kinetic model. Thus, ANNs can be applied where systems present nonlinearities and complex behavior.44 This method allows us to consider the effect of the operational parameters on the ultimate response under a diverse variety of operating conditions. Eqn (21) and (22) depict the mathematical relationship among the input and output parameters:45,46
 
image file: c6ra04500f-t20.tif(21)
 
Yt = ϕ(Dt + Bt) (22)

X1, …, XM are the data of the input, Wt1, …,WtM are the weights of neuron t, Dt is the output of the linear combiner due to the input data, Bt is the bias, ϕ is the transfer function and Yt is the neuron output data.

In this section, modeling of the heterogeneous catalytic ozonation process is demonstrated by using an ANN. The samples were divided into training, validation and test subsets of 69, 15 and 15 samples. In order to figure out the validating ability of the model, the validation and test sets were randomly selected from the experimental data. By normalizing the input data in the range of −1 to 1, the pre-processing was performed and all the data (Yi), containing the training, validation and test sets, were scaled to a new value Ynorm by using eqn (23):47

 
image file: c6ra04500f-t21.tif(23)
where Yi,min and Yi,max are the variable Yi extreme values. Due to the fact that a purely linear transfer function was used to join the hidden layer to the output layer, the target data were not normalized to a new scale.

3.7.1. The optimal ANN topology. ANN topology optimization is the main step in model development. The topology of the ANN is defined by the number of layers, number of nodes in each layer and the nature of the transfer functions.48 In the present research, a three-layered feed-forward back-propagation neural network (4[thin space (1/6-em)]:[thin space (1/6-em)]14[thin space (1/6-em)]:[thin space (1/6-em)]1) was utilized for modeling of the NAD removal efficiency by the heterogeneous catalytic ozonation process for the first time. The train scaled conjugate gradient ‘trainscg’, hyperbolic tangent sigmoid ‘tansig’ and a purely linear transfer function ‘purelin’ were chosen as the training, as the input to the hidden layer and as the hidden layer to the output layer transfer functions, respectively. Eqn (24)–(26) indicate the relations of the mentioned transfer functions:45
 
image file: c6ra04500f-t22.tif(24)
 
image file: c6ra04500f-t23.tif(25)
 
purelin(x) = x (26)

As described in eqn (27), the ANN model performed a nonlinear functional assessment from the previous observation to the future values (Yt):45

 
Yt = f(Xi,Wti) + Bt (27)

A schematic of a typical ANN with four neurons in the input, fourteen neurons in the hidden layer and one neuron in the output is represented in Fig. 5a. Ozone inlet flow rate (L h−1), catalyst concentration (g L−1), pH and initial NAD concentration (mg L−1) were the input variables to the feed-forward neural network. Also, the output variable was the NAD removal efficiency.


image file: c6ra04500f-f5.tif
Fig. 5 (a) The optimized topology of the ANN scheme and (b) mean squared errors in the NAD removal efficiency (%) prediction with various numbers of neurons in the hidden layer for train, validation and test data sets.

The number of the nodes was changed from 1 to 19, in order to determine the optimum number of hidden neurons. A mean squared error (MSE) function was applied in order to compare the performance of the ANN for the various configurations. Eqn (28) defines the MSE properly:45,49

 
image file: c6ra04500f-t24.tif(28)
where Yi,pred and Yi,exp are the prediction of the network and the experimental response, i is the data index and M is the number of target data.

The relationship among the network error and the number of neurons in the hidden layer for the train, validation and test data is shown in Fig. 5b. It can be clearly seen that the MSE is minimum at about 14 neurons for the train, validation and test data. Thus, as the best topology for modeling approach, a network with 14 neurons in the hidden layer is selected due to its best behavior without over-fitting.

In order to compute the train, validation and test errors, all output results were altered to the original scale and compared with the experimental values. The comparisons among the calculated and the experimental values of the variables of the output for the train, validation, test and all sets of data are represented in Fig. 6a–d. The reported R2 values of Fig. 6 are 0.991, 0.994, 0.994 and 0.991, which prove the fact of the small differences present among the experimental and predicted values of the output variable for the various subsets. It can be concluded that the developed topology (4[thin space (1/6-em)]:[thin space (1/6-em)]14[thin space (1/6-em)]:[thin space (1/6-em)]1) can appropriately predict the NAD removal efficiency. Also, this topology can predict the processes' suitable generalization ability.


image file: c6ra04500f-f6.tif
Fig. 6 Regression plots for comparison of the calculated NAD removal efficiency via neural network modeling with experimental values by using four input variables, fourteen processing elements in the hidden layer and one output variable (4[thin space (1/6-em)]:[thin space (1/6-em)]14[thin space (1/6-em)]:[thin space (1/6-em)]1) for the (a) train set, (b) validation set, (c) test set and (d) all data set.

3.8. Comparison of developed intrinsic kinetic, empirical kinetic and ANN models

In order to evaluate the performance of the developed intrinsic kinetic, empirical kinetic and ANN models for predicting NAD removal efficiency under the diverse variety of operating conditions, MSE, mean absolute error (MAE) and R2 were evaluated and compared. The MSE function was computed by eqn (28) and MAE function was computed by eqn (29):44
 
image file: c6ra04500f-t25.tif(29)

M is the number of target data, Yi,pred and Yi,exp are the prediction of the model and the experimental response and i is the data index. The MSE, MAE and R2 values are represented in Table 6 for each developed model. As can be observed from Table 6, the R2 values for intrinsic kinetic, empirical kinetic and ANN models are 98.0%, 99.8% and 99.1%, respectively. The largest deviation is observed for the ANN model compared to the other two models. For further investigation of the developed models' performances, analysis of the variance was performed. The results are indicated in Table 7. By considering the ANOVA results, it can be deduced that there are no considerable differences between the three models for predicting the NAD removal efficiency. Thus, the three developed models have the capability for anticipating the NAD removal efficiency. By investigating the results, it is evident that the intrinsic kinetic model is developed depending on the precise elementary reactions which are involved in heterogeneous catalytic ozonation (see Section 3.5) and also that the model assumptions are valid. Therefore, the heterogeneous catalytic ozonation process can be well modeled by the three proposed models. By considering the fact that all kinetic models have advantages and disadvantages, they can be selected by regarding the operating conditions of the system and their performances. The kinetic model based on the intrinsic elementary reactions can be used for modeling heterogeneous catalytic ozonation with different reactor configurations and can be developed for scaling up the existing objectives. In order to investigate the influence and the contribution of the operational parameters, the empirical model is the preferred method. By using the empirical method, the notable main factors are identified, so the complexity of the system is lessened. Of course, it is necessary to mention that the ANN method is a nonlinear and simple mathematical model. By using ANN, it is not necessary to explain the reactions occurring in the system mathematically.

Table 6 Comparison among the performances of the developed modelsa
Developed model REpredicted R2 MSE MAE
a R2: correlation coefficient, MSE: mean squared error, MAE: mean absolute error.
Intrinsic image file: c6ra04500f-t26.tif 0.980 0.129 0.026
Empirical REpredicted = 1 − exp(−0.254[pH]0.707[ozone flow rate]0.525[NAD]−0.447[catalyst]0.414 × t) 0.998 0.041 0.017
Artificial neural network A three-layered feed-forward back-propagation neural network with 4[thin space (1/6-em)]:[thin space (1/6-em)]14[thin space (1/6-em)]:[thin space (1/6-em)]1 topology 0.991 4.212 0.015


Table 7 ANOVA for assessing differences among the predicted NAD removal efficiencies by the developed models
Component Source of variation SSa dfb MSc Fratio P-Value Fcritical
a Sum of squares.b Degrees of freedom.c Mean squares.
REpredicted Between groups 0.0967 2 0.04839 0.7875 0.4559 3.0267
Within groups 17.8825 291 0.06145      
Total 17.9793 293        


4. Conclusions

Degradation of NAD by the heterogeneous catalytic ozonation process in the presence of CNs was modeled by the intrinsic kinetic, empirical kinetic and ANN methods. CNs were modified by the GDP technique. SEM analysis confirmed the nanorod structure of the plasma-treated clinoptilolite. The ozone inlet flow rate, catalyst concentration, pH and initial NAD concentration effects were studied in order to explore the kinetic characteristics of the heterogeneous catalytic ozonation process. Afterward, to predict the removal efficiency of NAD by catalytic ozonation, a novel intrinsic kinetic model was developed. By the use of the experimental data as well as the error function, the proposed model was validated. The developed model could completely explain the influence of the ozone inlet flow rate, catalyst concentration, pH and initial NAD concentration on the degradation rate constant. With the aim of investigating the developed model precision, an empirical kinetic model and ANN method were established. A good agreement was observed between the predicted NAD removal efficiencies and the experimental results. The three developed models were evaluated by ANOVA, which indicated no significant differences between the predicting capabilities of the three models. It can be deduced that the reactions used in the intrinsic kinetic model were correct and the assumptions were valid.

Acknowledgements

The authors sincerely thank the University of Tabriz for providing all of the support.

Notes and references

  1. J. Akhtar, N. A. S. Amin and K. Shahzad, Desalin. Water Treat., 2015, 1–19 CrossRef.
  2. M. Sayed, M. Ismail, S. Khan, S. Tabassum and H. M. Khan, Environ. Technol., 2015, 1–35 Search PubMed.
  3. C. Sirtori, A. Zapata, W. Gernjak, S. Malato, A. Lopez and A. Agüera, Water Res., 2011, 45, 1736–1744 CrossRef CAS PubMed.
  4. S. G. Ardo, S. Nélieu, G. Ona-Nguema, G. Delarue, J. Brest, E. Pironin and G. Morin, Environ. Sci. Technol., 2015, 49, 4506–4514 CrossRef CAS PubMed.
  5. M. M. Huber, S. Canonica, G.-Y. Park and U. Von Gunten, Environ. Sci. Technol., 2003, 37, 1016–1024 CrossRef CAS PubMed.
  6. C.-H. Wu, C.-Y. Kuo and C.-L. Chang, J. Hazard. Mater., 2008, 154, 748–755 CrossRef CAS PubMed.
  7. J. Nawrocki, Appl. Catal., B, 2013, 142, 465–471 CrossRef.
  8. X. Lü, Q. Zhang, W. Yang, X. Li, L. Zeng and L. Li, RSC Adv., 2015, 5, 10537–10545 RSC.
  9. O. Oputu, M. Chowdhury, K. Nyamayaro, F. Cummings, V. Fester and O. Fatoki, RSC Adv., 2015, 5, 59513–59521 RSC.
  10. S.-Y. Lee, J.-H. Yoon, J.-R. Kim and D.-W. Park, J. Anal. Appl. Pyrolysis, 2002, 64, 71–83 CrossRef CAS.
  11. B. Legube and N. K. V. Leitner, Catal. Today, 1999, 53, 61–72 CrossRef CAS.
  12. N. Rajic, D. Stojakovic, M. Jovanovic, N. Z. Logar, M. Mazaj and V. Kaucic, Appl. Surf. Sci., 2010, 257, 1524–1532 CrossRef CAS.
  13. A. Nezamzadeh-Ejhieh and S. Khorsandi, J. Ind. Eng. Chem., 2014, 20, 937–946 CrossRef CAS.
  14. A. Khataee, S. Bozorg, S. Khorram, M. Fathinia, Y. Hanifehpour and S. W. Joo, Ind. Eng. Chem. Res., 2013, 52, 18225–18233 CrossRef CAS.
  15. A. Khataee, M. Taseidifar, S. Khorram, M. Sheydaei and S. W. Joo, J. Taiwan Inst. Chem. Eng., 2015, 53, 132–139 CrossRef CAS.
  16. C.-J. Liu, J. Zou, K. Yu, D. Cheng, Y. Han, J. Zhan, C. Ratanatawanate and B. W.-L. Jang, Pure Appl. Chem., 2006, 78, 1227–1238 CAS.
  17. A. Khataee, M. Fathinia and S. Aber, Ind. Eng. Chem. Res., 2010, 49, 12358–12364 CrossRef CAS.
  18. A. Audirac, F. Pontlevoy and N. K. V. Leitner, Chem. Eng. J., 2015, 279, 1004–1009 CrossRef CAS.
  19. M. Mehrjouei, S. Müller and D. Möller, Chem. Eng. J., 2014, 248, 184–190 CrossRef CAS.
  20. H. Valdés and C. A. Zaror, Chemosphere, 2006, 65, 1131–1136 CrossRef PubMed.
  21. X.-B. Zhang, W.-Y. Dong and W. Yang, Chem. Eng. J., 2013, 233, 14–23 CrossRef CAS.
  22. J. Pocostales, P. Alvarez and F. Beltrán, Chem. Eng. J., 2010, 164, 70–76 CrossRef CAS.
  23. R. Andreozzi, V. Caprio, R. Marotta and V. Tufano, Water Res., 2001, 35, 109–120 CrossRef CAS PubMed.
  24. M. Taseidifar, A. Khataee, B. Vahid, S. Khorram and S. W. Joo, J. Mol. Catal. A: Chem., 2015, 404, 218–226 CrossRef.
  25. H. Bader and J. Hoigné, Water Res., 1981, 15, 449–456 CrossRef CAS.
  26. R. Huang, B. Lan, Z. Chen, H. Yan, Q. Zhang and L. Li, Chem. Eng. J., 2012, 180, 19–24 CrossRef CAS.
  27. F. F. Rivas, M. Carbajo, F. Beltrán, B. Acedo and O. Gimeno, Appl. Catal., B, 2006, 62, 93–103 CrossRef CAS.
  28. P. Pocostales, P. Álvarez and F. Beltrán, Chem. Eng. J., 2011, 168, 1289–1295 CrossRef CAS.
  29. F. J. Beltrán, F. J. Rivas and R. Montero-de-Espinosa, Ind. Eng. Chem. Res., 2003, 42, 3218–3224 CrossRef.
  30. L. Yuan, J. Shen, Z. Chen and Y. Liu, Appl. Catal., B, 2012, 117, 414–419 CrossRef.
  31. M. Fathinia, A. Khataee, S. Aber and A. Naseri, Appl. Catal., B, 2016, 184, 270–284 CrossRef CAS.
  32. R. C. Martins, M. Cardoso, R. F. Dantas, C. Sans, S. Esplugas and R. M. Quinta-Ferreira, J. Chem. Technol. Biotechnol., 2015, 90, 1611–1618 CrossRef CAS.
  33. L. Lei, L. Gu, X. Zhang and Y. Su, Appl. Catal., A, 2007, 327, 287–294 CrossRef CAS.
  34. M. Fathinia and A. Khataee, Appl. Catal., A, 2015, 491, 136–154 CrossRef CAS.
  35. A. Aguinaco, F. J. Beltrán, J. F. García-Araya and A. Oropesa, Chem. Eng. J., 2012, 189–190, 275–282 CrossRef CAS.
  36. F. Qi, B. Xu, L. Zhao, Z. Chen, L. Zhang, D. Sun and J. Ma, Appl. Catal., B, 2012, 121, 171–181 CrossRef.
  37. M. Sui, S. Xing, L. Sheng, S. Huang and H. Guo, J. Hazard. Mater., 2012, 227, 227–236 CrossRef PubMed.
  38. F. J. Beltran, Ozone reaction kinetics for water and wastewater systems, CRC Press, 2003 Search PubMed.
  39. F. J. Beltrán, A. Aguinaco and J. F. García-Araya, Ozone: Sci. Eng., 2012, 34, 3–15 CrossRef.
  40. U. Von Gunten, Water Sci. Technol., 2007, 55, 25–29 CrossRef CAS PubMed.
  41. F. J. Beltrán, J. Rivas, P. Alvarez and R. Montero-de-Espinosa, Ozone: Sci. Eng., 2002, 24, 227–237 CrossRef.
  42. B. Kasprzyk-Hordern, M. Ziółek and J. Nawrocki, Appl. Catal., B, 2003, 46, 639–669 CrossRef CAS.
  43. C. Zhao, M. Pelaez, X. Duan, H. Deng, K. O'Shea, D. Fatta-Kassinos and D. D. Dionysiou, Appl. Catal., B, 2013, 134, 83–92 CrossRef.
  44. A. Khataee and M. Kasiri, J. Mol. Catal. A: Chem., 2010, 331, 86–100 CrossRef CAS.
  45. H. Zheng, S. Fang, H. Lou, Y. Chen, L. Jiang and H. Lu, Expert Syst. Appl., 2011, 38, 5591–5602 CrossRef.
  46. A. Akbarpour, A. Khataee, M. Fathinia and B. Vahid, Electrochim. Acta, 2016, 187, 300–311 CrossRef CAS.
  47. E. S. Elmolla, M. Chaudhuri and M. M. Eltoukhy, J. Hazard. Mater., 2010, 179, 127–134 CrossRef CAS PubMed.
  48. M. Zarei, A. Khataee, R. Ordikhani-Seyedlar and M. Fathinia, Electrochim. Acta, 2010, 55, 7259–7265 CrossRef CAS.
  49. A. R. Khataee, M. Fathinia, M. Zarei, B. Izadkhah and S. W. Joo, J. Ind. Eng. Chem., 2014, 20, 1852–1860 CrossRef CAS.

This journal is © The Royal Society of Chemistry 2016
Click here to see how this site uses Cookies. View our privacy policy here.