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Article

Assessment of Blast Furnace Slags as a Potential Catalyst in Ozonation to Degrade Bezafibrate: Degradation Study and Kinetic Study via Non-Parametric Modeling

by
Alexandra Galina-Licea
1,
Mariel Alfaro-Ponce
2,
Isaac Chairez
3,
Elizabeth Reyes
1 and
Arizbeth Perez-Martínez
4,*
1
Facultad de Ciencias Químicas, Universidad La Salle, Mexico City 06140, Mexico
2
Institute of Advanced Materials for the Sustainable Manufacturing, Tecnológico de Monterrey, Mexico City 14380, Mexico
3
Institute of Advanced Materials for the Sustainable Manufacturing, Tecnológico de Monterrey, Zapopan 45210, Mexico
4
Grupo de Investigación Desarrollo e Innovación en Ciencia y Tecnología Ambiental Aplicada, Vicerrectoría de Investigación, Universidad La Salle México, Mexico City 06140, Mexico
*
Author to whom correspondence should be addressed.
Processes 2024, 12(9), 1998; https://doi.org/10.3390/pr12091998
Submission received: 26 April 2024 / Revised: 15 June 2024 / Accepted: 24 June 2024 / Published: 17 September 2024
(This article belongs to the Special Issue Machine Learning Applied in Wastewater Treatment)

Abstract

:
This study investigates the effectiveness of blast furnace slags (BFSs) as catalysts in the ozonation process to degrade complex contaminants such as bezafibrate (BFZ) at different pH levels. The findings reveal that the presence of BFS enhances degradation efficiency, achieving a 10% improvement at pH 10 and a 30% improvement at pH 5.5 compared to simple ozonation. The highest degradation efficiency was observed in the Ozonation–BFS system at pH 10, with 90% decomposition of BFZ. These results were corroborated through ozone consumption analysis, BOD5 measurements, and the identification of oxalic acid as the final decomposition product. Due to the complexity of the reaction system, kinetic characterization was performed using non-parametric modeling based on differential neural networks. The model indicated that the observed reaction rate for BFZ degradation in the presence of ozone and BFS was 4.12 times higher at pH 5.0 and 1.08 times higher at pH 10.0 compared to simple ozonation. These results underscore the potential of using BFS in catalytic ozonation processes for the effective treatment of recalcitrant contaminants in wastewater.

1. Introduction

Blast furnace slag (BFS) is a byproduct of steel manufacturing, which represents a significant challenge because of its volume and potential environmental consequences. According to some statistics, the world produces about 190–290 Mt of steel slags every year [1,2,3]. BFS has a bulk density between 1200 and 1300 kg/cm3 and a specific gravity of about 2.90 [4]. Moreover, BFS contains several inorganic components, including SiO2 (30–36%), Al2O3/Fe2O3 (11–25%), CaO (28–41%), and MgO (1–9%), among others [5]. Today, most BFS is not reused or recycled. The main problem of BFS sustainable handling is the heavy economic burden to the steel manufacturer, which directly impedes the development of the steel production industry [6].
The potential re-utilization of BFS has been widely studied in construction industries and civil engineering. Mostly, BFS is being used as a partial replacement material for the Portland cement industry [7,8]. This reuse option has become a significant means of absorbing the slags’ solid waste, avoiding adverse effects and harm to the environment, underground water, the surrounding atmosphere, and soil pollution [9]. Also, BFS has been used to improve soil and marine ecological environments. Asaoka (2010) used BFS to control nutrient release fluxes from organic sediments into overlying water [10]. Haynes Richard, 2013, found that BFS was the most effective silicon fertilizer compared to coal fly ash [11]. Kim et al. obtained high-purity nano silica from BFS using acidic pre-treatment and surface modification by cetyltrimethylannonium bromide [12].
In addition to its applications in construction, BFS may have significant potential for enforcing active environmental modifications, particularly as complements for water and wastewater treatment processes. BFS versatility is evident in various roles, including using such components as coagulants, filters, adsorbents, neutralizers/stabilizers, and fillers in soil aquifers. It also serves as engineered wetland bed media, effectively removing toxic and recalcitrant pollutants in water and wastewater (WWT) [13].
Recalcitrant contaminants include compounds with very complex chemical structures that can resist the effect of traditional degradation systems based on physical and biological principles. Some chemical treatments that use effective oxidants could decompose some recalcitrant pollutants but with a low reaction rate. Such recalcitrant compounds include the residual pharmaceutical products discharged in domestic and industrial residual water streams.

Blast Furnace Slag Application in Pharmaceutical Wastewater Treatment

Pharmaceutical compounds are found in freshwater bodies and wastewater effluents by excretion of non-metabolized active ingredients by direct disposal to public sewage [14]. Bezafibrate (BZF, p-[4-[cholobenzoylamino-ethyl]-phenoxyl]-b-methylpropionic acid) was selected as the tested pollutant in this study due to is a drug used extensively as a lipid regulating agent [15]. However, a recent investigation found that BZF was present in aquatic systems, including influent/effluent waters of sewage treatment plants [16]. The BZF accumulation could be potentially dangerous to the aquatic environment due to its bioaccumulation, toxicity, synergistic, and additives effects [17].
Many processes, such as nanofiltration, reverse osmosis, or flocculation/coagulation, can transfer BZF to the concentrate WWT and not be degraded [18]. On the contrary, advanced oxidation processes (AOPs) are among the more effective options for decomposing recalcitrant contaminants, leading to less toxicity, a higher degree of biodegradability, and a larger mineralization degree. Ozonation is considered a reliable, environmentally friendly, and highly effective AOP. The degradation effectiveness of complex contaminants by ozonation has been studied for many years, showing diverse advantages such as producing oxygen as the only subproduct of the injected gas, the eventual complete mineralization if the reaction time is large enough, etc. However, the ozonation of recalcitrant contaminants could not be so effective given the small reaction rates observed in diverse studies, which enforces high ozone consumption and long reaction periods to reach admissible contaminants decomposition given the applicable regulations [19].
Catalytic ozonation is an AOP that shows several advantages on WWT compared to traditional ozonation. Catalytic ozonation could reduce the reaction time to reach the decomposition of recalcitrant contaminants, augment the mineralization degree of the treated contaminants, and reduce the accumulation of the toxic intermediates formed during ozonation [16,20,21]. Including metallic oxides has proven to be an effective method to enforce catalytic ozonation, producing radicals that can decompose recalcitrant contaminants faster than the molecular reaction between ozone and the contaminant. A complementary source of radicals considers irradiating the BZP solution with photons in the ultraviolet wavelength range in the presence of ozone and BFS. This photocatalytic reaction system can augment the decomposition efficiency, as proven in [22,23,24].
As already mentioned, BFS contains oxide compounds such as CaO, MgO, Al2O3, SiO2, and Fe2O3, among others, that could be used in wastewater treatment. Table 1 shows some recent research articles in which metallurgic slags were used to treat water contaminated with some type of drug and the main results obtained.
As can be seen in the results obtained in previous research, metallurgic slags could provide raw materials for wastewater treatment, and this research assesses the use of BFS as a catalyst for the ozonation of BZF. The study considers that BFS could also be used as part of a photocatalytic ozonation process due to its physicochemical properties and composition. The results of decomposition efficiency were corroborated by BOD 5, HPLC analysis, ozone consumption, and the non-parametric modeling of BZF degradation.

2. Materials and Methods

2.1. Chemical Reagents

BZF was acquired from Silanes Laboratories S.A. de C.V., Mexico City, Mexico. in a presentation of 200 mg per pill. The selected chemical reagent was an analytical degree of 99 % purity. The initial concentration of BZF was 100 mg/L, and the solvent was water.
Four different systems were studied according to Table 2. Two experimental cases considered a pH = 5.5, and two more were fixed with an initial pH = 10.0. Each selected BZF solution with a different pH was prepared with and without BFS as a non-homogeneous catalyst. The adjustment of the initial pH was performed with solutions of HCl, 1.0 M, and NaOH, 1.0 M.
The volume of the BZF solution was 0.1 L for all the studied cases.

2.2. Blast Furnace Slag Source

BFS was obtained from Exiros company in Puebla, Mexico. These BFS were screened to homogenize the size of the catalyst particles. The physic-chemical properties of BFS particles are shown in Table 3, determined by a certified laboratory. The components that form BFS particles are metallic oxides. The oxides compounds shown in Table 2, such as FeO, CaO, and SiO2 compounds, are common catalysts for complex organics such as pharmaceutical products, as shown in different studies [31,32,33]. In this study, BFS was not pre-treated before the ozonation process.

2.3. Ozonation Procedure

Figure 1 shows the ozonation process at laboratory level. All ozonation experiments were conducted at room temperature ( 25   ±   3 °C) using a semi-continuous glass reactor with a sample volume of 0.1 L. The initial ozone concentration of 30 mg/L was generated utilizing a corona discharge-type ozone generator, the HTU500G model from AZCO Industries Limited, based in Langley, British Columbia, Canada. The ozone was produced with an oxygen flow rate of 0.25 L/min. To distribute the ozone and oxygen mixture evenly within the reactor, a porous ceramic diffuser was strategically positioned at the reactor’s bottom, which was exposed to a light source of 395–405 nm in the systems where BFS are used as catalysts.
The continuous monitoring of the inlet and outlet gas streams was achieved with precision using an ozone analyzer model BMT-930, BMT Messtechnik GmbH, Stahnsdorf, Germany. The ozone concentration sensor was connected to a personal computer where data were collected. A dedicated data acquisition software (Matlab R2020a, MathWorks Inc., Natick, MA, USA) was custom-designed to capture the ozone concentration variations over time, commonly called an ozonogram. The collected data obtained from the ozonogram was used to determine the kinetic ozone reaction and the ozone consumed through the reaction. These two data characterize the ozonation effectiveness as a function of the initial pH variation and the presence of the catalyst.
The BFSs that were used as catalysts were packed in the glass reactor. Once the reaction was finished, the slags were removed from the reactor at the top to further reutilization in the ozonation process.
Samples of the ozonated solution were collected at 0.0, 5.0, 7.5, 15.0, 22.0 and 30.0 min to perform the analytical studies that characterize the decomposition of the initial contaminants as well as the formed byproducts obtained during ozonation in the presence of the selected catalyst.

2.4. Analytic Method Analysis

The samples obtained during the ozonation were analyzed to determine the biochemical oxygen demand at day five (BOD5) and UV Spectroscopy variations. These studies characterize the BZF decomposition efficiency considering the variation of absorbance measured at 227 nm.
BOD5 was determined according to the operating instructions for the 890 BOD5 detector (Standard method 5210D, reactives Hach BODTrakTM II Hach, Hach Co., Loveland, CO, USA).
An HPLC System YL9100, UYL Instruments, Co., Ltd., Anyang, Republic of Korea The device with solvent degassed and a UV detector at 210 nm was used to determine the variation of initial, intermediate, and final accumulated reaction compounds. The used column was a 4.6 × 150 mm Agilent Eclipse XDB-C18, Agilent Technologies, Santa Clara, CA, USA, and the mobile phase was a mixture of phosphate buffer, pH = 4/acetonitrile (9:1) in isocratic elution, with a flow of 1.1 mL min−1 at room temperature (Acetonitrile and Sodium Phosphate, J.T. Baker, Phillipsburg, NJ, USA).

2.5. Non-Parametric Modeling of BZF Degradation to Determine the Ozonation Kinetics

The complexity of the reaction between the initial contaminants and ozone can hardly be characterized by traditional chemical models based on first principles. Regularly, the class of reaction between ozone and the hydroxyl radicals with contaminants can be represented as follows:
d d t c ( t ) = k O 3 ( t ) c ( t ) + ξ ( t )
Here, c, [mol·L 1 ] is the concentration of the initial contaminant, O3, [mol·L 1 ] is the concentration of ozone in the liquid phase, and k, L·mol 1 ·s 1 . The term ξ represents the imprecision of the kinetic model once the order of the reaction has been selected. Notice that if the estimate of the derivative of c is known, namely d d t c ^ ( t ) , then a parametric identification method can be implemented to estimate the value of the reaction rate constant k.
The implementation of approximate models based on Differential Neural Networks (DNN) can be used to estimate the derivative of the concentration of BZF. The estimated model that approximates the left-hand side of (1) can be used to construct the following alternative model:
d d t c ( t ) = D N N ( c ( t ) , W ( t ) ) ψ ( t )
Here, DNN represents the approximate model based on DNN with time-varying parameters W that must be adjusted so that c ^ c can be minimized. The term ψ introduces the modeling error contained as a consequence of selecting a finite number of activation functions in the DNN.
DNN can address problems with long-term and short-term reliance on the state (such as in the case of ozonation systems) and gradient vanishing that prevent the effective adjustment of weights. The advantage of having feedback connections makes them different from traditional feed-forward ANNs. Because of this property, they are primarily used for tasks involving time series data, where there may be erratic delays between essential events. DNNs were created primarily to solve the approximate modeling of complex dynamics with continuous evolution on time. DNNs frequently outperform the performance of other sequence-based learning techniques because of their superior ability to handle sequence gaps of various lengths.
The hyperbolic tangent (tanh) type and the sigmoid function are two activation functions incorporated into the DNN [34]. In most cases, the sigmoid activation function Equation (3) is furthermore implemented in DNN, having the following definition:
δ ( x ) = 1 + e d x 1 , d R
The sigmoid function’s output range is [ 0 , 1 ] , and the neural network discards any irrelevant data. According to [35], if the output is close to zero, it is irrelevant information and should be discarded; if it is close to one, it should be kept.
According to the machine learning fundamentals, the DNN structure in Equation (2) may have diverse topologies. This study considers the following structure for the DNN form
D N N c ( t ) , W ( t ) = W ( t ) σ c ^ ( t )
Here, w R p represents the weights in the network structure. The term σ R p is a vector formed with diverse sigmoidal functions such as the ones presented in (3). The function c ^ represents the estimated dynamics of the contaminant concentration, which the proposed DNN generates, that is,
d d t c ^ ( t ) = W ( t ) σ c ^ ( t )
The dynamics of the weights w is given by
d d t w ( t ) = α σ c ^ ( t ) c ( t ) c ^
In Expression (6), α is a positive scalar. This particular form defines a class of continuous dependent gradient descent methods that adjust weights in the DNN. This particular form that drives the weights’ adjustment has been designed using formal tools such as Lyapunov’s stability theory [35].
Noticing that the estimates of the time derivative of c using the DNN structure in (4) permit us to estimate the value of the reaction constant rate k. The application of the least mean square method leads to the following algorithm to estimate the rate [36,37]
k ^ = τ = 0 t O 3 ( τ ) c ( τ ) 2 d τ 1 τ = 0 t W ( τ ) σ σ ( c ^ ( τ ) ) O 3 ( τ ) c ( τ ) d τ
The estimate of the reaction rate constant k ^ can be used to characterize the effectiveness of the ozonation reaction catalyzed by the metallic oxides in the slag. Moreover, the relative value of this parameter establishes a simplified manner to compare such effectiveness for different ozonation systems with and without the presence of the catalyst.

3. Results and Discussion

3.1. Ozone Consumption and pH Effect

Figure 2 shows the ozonograms from the different treated systems. Once the oxidation reaction begins, the ozone concentration decreases due to the saturation of the ozone reactor and its reaction with the contaminant compound in solution. The dissolved ozone concentration reaches a minimum and then increases again after the complete decomposition of the initial contaminant molecule, eventually restoring the initial ozone concentration. For all treated systems, the period during which no significant variation in ozone concentration was observed lasted 20 min (the total reaction time). According to Poznyak et al. [38], the ozonogram gave information about the reaction in an indirect way because each slope variation of the graph corresponded to a major step of the compound decomposition reaction, in this case, the BZF decomposition, the accumulation–decomposition of intermediate compounds, and the potential accumulation of final byproducts such as oxalic acid, formic acid, and ketones among other compounds with a low reaction constant with ozone [39].
Table 4 shows the area under the total discrete data was calculated, and then a discrete integration was carried out, applying the trapezoidal rule considering the sampling period of 0.0167 min. Finally, the computer program showed the total area under the curve. The result obtained provided the amount of ozone consumed in the ozonation process, and then it was multiplied by the ozonation time, giving the ozone consumed through the reaction in mg/L [38].
This study was carried out at two different pHs: 5.5 and 10; hence, there are two different reaction mechanisms, one at acidic pH where the ozone reacts directly, and at basic pH, where the ozone decomposes itself into other reactive species. Stoichiometrically, at acidic pH, the reaction scheme that is carried out for BZF decomposition is:
C 19 H 20 ClNO 4 + 11 O 3 2 CO 2 H 2 + COC 2 H 6 + 7 C 2 H 2 O 4 + NO 2 H + HCl
The final organic products of the reaction are formic acid, ketone, and oxalic acid, the last most abundant in the system. At this pH, the reaction occurs more slowly than the SOP pH 10 because the only reactive molecule present in the media is the ozone.
At basic pH, the ozone decomposes into reactive oxygen species, according to the following reaction scheme [40,41]:
  • The ozone molecule (EV: +2.07 V) reacts with hydroxyl ion ( OH ) , obtained as the main products of hydroperoxyl ion and molecular oxygen.
    O 3 + OH HO 2 + O 2
  • The next step is the ion hydroperoxyl dissociation into superoxide ion and a proton.
    HO 2 O 2 + H +
  • Then, the ozone reacts with the superoxide ion, yielding the ozonide radical and molecular oxygen.
    O 2 + O 3 O 3 + O 2
  • Finally, the ozonide radical reacts with water molecule, producing hydroxyl radicals (EV: +2.80 V), hydroxyl ions (EV: +1.50 V) and molecular oxygen.
    O 3 + H 2 O 2 OH + OH + O 2
According to the SOP pH 10 system, the ozonogram curve decreases faster than the SOP pH 5 system in the first two minutes of the reaction. This phenomenon occurs due to those two reaction systems happening at the same time: the ozone decomposition into oxygen-reactive species and the reaction of ozone with the BZF molecule. This is the reason why the ozone consumed in SOP pH 10 is higher at 11.4% compared with the SOP pH 5.
In the systems where BFS is present, the ozonograms have a completely different dynamic. The ozone consumed in the ozonation pH 5.5 + BFS system was 27.3% higher than the ozonation pH 10 + BFS system; according to Zhou et al. [42] and Quan et al. [43] at basic pH, the presence of calcium favors the generation of hydroxyl radicals (EV: +2.80 V), whereas Zhou et al. [42] reported that the ozone molecule was absorbed on the BFS surface to form the ozonide of calcium by a terminal atom, which finally decomposes to form oxygen atoms and superoxide ions (EV: +0.89 V). So, many reactive oxygen species stimulate the BFS decomposition and the formation of oxalic acid, the main final organic compound obtained from the ozonation process.

3.2. Bezafibrate Decomposition Efficiency and pH Effect

Figure 3 shows BZF degradation efficiency obtained by UV analysis at 227 nm and the related % BOD5 variation after 20 min of reaction. The gathered presentation permits us to observe the high correlation between the ozonation efficiency and how the obtained mixture after the process can be used eventually as a substrate for a controlled bioprocess that can polish the treatment strategy.
The ozonation performed at pH = 10 and BFS obtains the maximum degradation efficiency after 20 min of reaction. This value is 47% higher compared to SOP pH = 5 and 11% higher than the SOP with pH = 10 (Figure 3).
The BFS commercial composition indicates that 22% is CaO; Zhou et al. [33] and Fasce et al. [29] reported the use of CaO/Ca(OH)2-based materials as a catalyst in the ozonation process to degrade organic pollutants. Other studies agreed that calcium oxides dissolved in water form Ca(OH)2 promote an alkaline environment and improve the OH generation. Zhou et al. [33] reported that the ozone molecule could be absorbed into the Ca atom on the surface of calcium hydroxide to form an ozonide of calcium. Then, aqueous media would accelerate the ozonide of calcium to form oxygen atoms and O2•− and decompose to form OH [29,42,43]. Moreover, the presence of calcium ions could promote the precipitation of other molecules, such as oxalic acid, which, in this case, is the main final product of BZF degradation, provokes the highest % degradation efficiency and BOD5 value.
Allen and Hayhurst [44] and Li [45] indicate that the reaction mechanism between calcium oxide and ozone implies several steps and the formation of different by-products, according to the following reaction steps:
1.
Ozone adsorption on the BFS surface: the gaseous ozone is absorbed on the BFS surface.
CaO ( s ) + O 3 ( g ) CaO · O 3 ( a d s )
2.
Decomposition of the ozone adsorbed: the ozone molecule decomposes into molecular oxygen and one reactive oxygen atom, generating reactive species on the BFS surface. The atomic oxygen obtained is highly reactive and it can participate in other reactions.
CaO · O 3 ( a d s ) CaO · O 2 ( a d s ) + O ( a d s )
3.
Oxidation of CaO by oxygen atom: this reaction forms calcium peroxide (CaO2, EV: + 0.70 V).
CaO ( s ) + O ( a d s ) CaO 2
4.
Molecular oxygen desorption: de oxygen is desorbing from the BFS surface, regenerating CaO molecules, ensuring that the BFS surface is available for new ozone molecules.
CaO · O 2 ( a d s ) CaO ( s ) + O 2 ( g )
At alkaline conditions, leached species of Fe and Mn are very low, so their contributions to BZF degradation seemed despicable (Figure 4).
Ozonation with pH = 5 + BFS increases the degradation efficiency up to 31% higher than the case with pH = 5 without the presence of the BFS. According to Andreozzi et al. [46] and Ma and Graham [47], the presence of manganese at low concentrations as a catalyst in the ozonation process improved the ozone decomposition-producing radical species. Andreozzi et al. [46] reported that MnO2 facilitated the adsorption of oxalic acid over the protonated surface of the managed oxalic acid complex, giving an intermediate product that was a more easily degraded compound by the ozonation process (Figure 4).
Manganese catalyzed ozone decomposition, generating hydroxyl radicals and, simultaneously, being oxidated by one to produce manganese oxide [46,47]. All these compounds formed could be why the ozonation reaction system with initial pH=10 in the presence of BFS increases the BZF degradation efficiency at large.
Notice that pH = 10 was the key factor in decomposing BFZ, the initial contaminant in the reaction system. This fact indicates that even pH = 5 could produce more ozone consumption, which, by the reaction kinetics, was not strictly related to the total consumed ozone. This fact is confirmed in the following subsection.
According to Xu and Yang [48], Pei [49], and Dong et al. [50], the reaction mechanism between manganese oxide and ozone is a multi-layer process that involves the ozone adsorption on the BFS surface. This process is very important due to the high MnO2 efficiency as a catalyst in the ozone decomposition, as the following mechanism describes:
1.
Ozone adsorption on BFS MnO2 surface. The gaseous ozone adheres to the BFS surface where MnO2 lies, where the following reactions occurs:
MnO 2 s + O 3 g MnO 2 · O 3 ( a d s )
2.
Decomposition of adsorbed ozone. The ozone decomposes in molecular oxygen and one reactive oxygen atom; in this stage species highly reactive are generated to react with MnO2 and other molecules adsorbed in the BFS surface.
MnO 2 · O 3 ( a d s ) MnO 2 · O 2 ( a d s ) + O ( a d s )
3.
MnO2 oxidation: the reactive oxygen atom generated oxidizes MnO2, leading to the formation of a higher valency manganese oxide, Mn2O5(EV= + 1.2 V).
2 MnO 2 + O ( a d s ) MnO 2 · O 5
4.
Molecular oxygen desorption: MnO2 is regenerated to allow the reaction cycle to continue, adsorbing more ozone.
MnO 2 · O 2 ( a d s ) MnO 2 ( a d s ) + O 2 ( g a s )
Figure 4 shows the correlation between the BFZ degradation efficiency and the BOD5 variation test to all studied systems. It is observed that the systems with the higher degradation efficiency were at pH 10 due to the presence of reactive oxygen species through the ozonation process, with the Ozonation pH 10 + BFS system being the most efficient system with a 90% of BFZ elimination. It can be seen that the presence of BFS is the key factor in improving the BFZ degradation, which increases by 50% compared with the SOP pH 5.5 system, 14% with the Ozonation pH 10 + BFS system, and 28% with the Ozonation pH 5.5 system. These results could be corroborated with the BOD5 variation, where the same dynamics could be observed. This test measures the amount of oxygen required by aerobic microorganisms to decompose the organic matter present in the treated water by the SOP and Ozonation + BFS systems over a five-day period at a temperature of 20 °C, obtaining the water quality and the organic load of wastewater. In this case, BOD5 is reported as a normalized value, which means that SOP pH 5 has a higher concentration of biodegradable compounds, such as BFZ molecules, according to its low degradation efficiency plus the byproducts of the ozonation reaction. On the other hand, the Ozonation pH 10 + BFS system has the highest degradation efficiency and observed the lowest BOD5 variation, which means that this system has the lowest organic load compared with the other systems.

3.3. Final Compound Identification

This study identified oxalic acid as the main final accumulated compound, increasing its accumulation when BFS was in the reactor, especially in the ozonation system, with the initial pH fixed to 10. The selection of these final byproducts obeys the strategy proposed by some authors who have found this compound to be a reliable indicator of ozonation quality despite the presence of catalysts or hydroxyl ions (Figure 5).
Li et al. [16] studied the degradation of BZF by catalytic ozonation and found oxalic acid and mesoxalic acid as final reaction compounds. It correlates with the results presented in Figure 5, where compounds derived from Ca, Mn, OH, and ozone promote the decomposition of the BZF molecule to oxalic acid.
The variation of the accumulated oxalic acid as a function of the presence of BFS and the initial fixed pH demonstrates that the accumulated effect of both indirect mechanisms with the catalyst and the radicals is observed when the final accumulated byproduct is studied. The superior performance of the combined effect on the ozonation effectiveness measured with the final accumulated byproduct has been considered recently. This fact has been attributed to the equalized reaction rate of the initial contaminant with ozone. This characteristic limits the detection of significant differences among the catalytic and non-catalytic cases.

3.4. Kinetic Study of BZF Decomposition via LSTM Modeling

Figure 6, Figure 7, Figure 8 and Figure 9 show the evolution of the BZF decomposition over time and its corresponding comparison with the trajectories produced by the proposed DNN identifier. The identifier considered the evolution of the ozonogram, the pH variation, and the initial concentration of BFZ as inputs. The state of the approximated system was the evolution of the contaminant compound over time. The approximate dynamics generated by the DNN identifier produced a mean square error, which was gradually improved through the training process. The training method considered in this study is a traditional method based on training and generalization. A set of 10 variants of each temporal evolution of BZF decomposition was produced using a synthetic data augmentation strategy. The addition of random noise with normal distribution, a mean value of zero, and a standard deviation of 5 was selected to produce each signal. The established experimental study and the detection of errors in the analytical equipment enforce this value selection.
The 70% of the entire set of produced samples was used to perform the training process, while the remaining 30% was used for validation. The re-substitution error was 0.5%. There was no early stop criterion for performing the study, which was enforced using all the data sequences of BFZ decomposition and the corresponding ozonograms.
Using the dynamic evolution of the DNN states and the admitted model for the ozonation reaction system, applying the least mean square error presented in the methodology section leads to an estimate of the observed reaction rate constants presented in Table 5.
The estimated reaction rates corroborate the observed experimental results regarding the ozonation effectiveness, considering the presence of BFS and the pH variation. The estimated reaction rates detected in other studies for BZF are in the range of 10 2 when conventional ozonation is considered. This fact demonstrates that using basic pH and BFZ may represent not only an efficient manner to decompose a complex contaminant such as BFZ but also the intermediate compounds formed by BFZ decomposition. This fact is confirmed by using oxalic acid as a key indicator of ozonation efficiency.

3.5. Blast Furnace Slag XRD Analysis

BFS were subjected to X-ray diffraction (XRD) analysis both before and after their application in the catalytic ozonation processes aimed at degrading BFZ. Figure 10 shows the XRD patterns obtained indicate that there were no significant changes in the crystalline structure of the slags following exposure to the ozonation process. This stability in the crystalline structure suggests that BFS can maintain their integrity during the catalytic reactions, making them suitable for repeated use in such processes.
The analysis identified the most important crystalline phases present in BFS, including Fe2O3 (iron oxide), Al2O3 (aluminum oxide), CaO (calcium oxide), MgO (magnesium oxide), MnO (manganese oxide), and SiO2(silicon dioxide). These components play a crucial role in the catalytic ozonation process. For instance, Fe2O3 and MnO are known for their catalytic properties, which enhance the generation of reactive oxygen species necessary for the degradation of organic pollutants like BZF. The presence of CaO and MgO can contribute to the overall stability and reactivity of the catalyst, while SiO2 provides structural support.
The preservation of these phases post-ozonation indicates that the active sites responsible for catalysis are not significantly altered or consumed during the reaction, thereby retaining their catalytic efficiency. This detailed understanding of the composition and structural stability of BFS underscores their potential as robust and effective catalysts in the treatment of pharmaceutical contaminants in wastewater through catalytic ozonation processes. The findings support the continued investigation and application of BFS in environmental remediation technologies, particularly for the sustainable treatment of emerging contaminants.

4. Conclusions

Blast furnace slag (BFS) has demonstrated high efficiency as a catalyst in the ozonation process for degrading bezafibrate (BZF). The presence of metallic oxides in BFS, such as FeO, CaO, and MnO contributes significantly to its catalytic properties. This study confirms that BFS enhances the degradation efficiency of BZF, achieving a degradation rate 4.12 times higher at pH 5.0 and 1.08 times higher at pH 10.0 compared to simple ozonation, as verified by BOD5 analysis and the accumulation of oxalic acid.
X-ray diffraction (XRD) analysis revealed that the crystalline structure of BFS remained stable during the catalytic ozonation process, with no significant changes observed before and after treatment. The most important crystalline phases identified were Fe2O3, Al2O3, CaO, MgO, MnO, and SiO2. This stability indicates the robustness of BFS as a catalyst and its potential for repeated use without structural degradation.
Utilizing BFS as a catalyst in ozonation presents a viable and cost-effective method for revalorizing industrial waste and developing efficient wastewater treatment processes. This study provides a comprehensive kinetic characterization using non-parametric modeling via differential neural networks, offering a more detailed understanding of the reaction dynamics. The findings demonstrate significant progress in the field, highlighting BFS’s potential as a sustainable and effective catalyst for advanced oxidation processes.
Future research should focus on optimizing the operational parameters for continuous reaction systems, exploring the reusability and long-term stability of BFS in catalytic ozonation, and investigating the applicability of this method to a broader range of emerging contaminants. Additionally, scaling up the process and integrating it into existing wastewater treatment facilities could further validate its practical viability and environmental benefits.

Author Contributions

Conceptualization, M.A.-P., I.C. and A.P.-M.; Methodology, A.G.-L., E.R. and A.P.-M.; software, M.A.-P. and I.C.; validation, I.C. and A.P.-M.; formal analysis, I.C.; investigation, A.G.-L., E.R. and A.P.-M.; resources, M.A.-P. and A.P.-M.; data curation, I.C.; writing—original draft preparation, A.G.-L., E.R. and A.P.-M.; writing—review and editing, M.A.-P., I.C. and A.P.-M.; visualization, M.A.-P., I.C. and A.P.-M.; supervision, M.A.-P., I.C. and A.P.-M.; project administration, M.A.-P. and A.P.-M.; funding acquisition, M.A.-P. and A.P.-M. All authors have read and agreed to the published version of the manuscript.

Funding

This research was financially funded by Universidad La Salle Mexico project ID IMC-18-21 and the Tecnologico de Monterrey Challenge-Based Research Program projects ID IJXT070-22TE60001 and IJXT070-23EG60002. All the authors appreciate the donations of BFS made by Exiros, especially those samples obtained from plants located at Puebla, Mexico.

Data Availability Statement

Data used in this study will be available upon the appropriate request to the corresponding author.

Acknowledgments

The authors thank the catalysis and materials laboratory of ESIQIE-IPN for carrying out the XRD analyzes. All authors thanks Adriana Benitez Rico from Universidad La Salle México for support in the interpretation of the XRD analysis.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
AOPAdvanced oxidation processes
BFSBlast furnace slag
BZFBezafibrate
BOD5Biological oxygen demand at 5th day
DNNDifferential neural networks
WWTWater and wastewater treatment

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Figure 1. Laboratory ozonation process: (a) oxygen tank, (b) ozone generator, (c) BFS packed glass reactor exposed to Visible light (495–405 nm), (d) gaseous phase ozone sensor, (e) data acquisition card, and (f) computer.
Figure 1. Laboratory ozonation process: (a) oxygen tank, (b) ozone generator, (c) BFS packed glass reactor exposed to Visible light (495–405 nm), (d) gaseous phase ozone sensor, (e) data acquisition card, and (f) computer.
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Figure 2. Temporal variation of ozone concentration for the studied systems: conventional ozonation at pH = 5.0 (black line), conventional ozonation at pH = 10.0 (red line), catalytic ozonation at pH = 5.0 (blue line), and catalytic ozonation at pH = 10.0 (green line).
Figure 2. Temporal variation of ozone concentration for the studied systems: conventional ozonation at pH = 5.0 (black line), conventional ozonation at pH = 10.0 (red line), catalytic ozonation at pH = 5.0 (blue line), and catalytic ozonation at pH = 10.0 (green line).
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Figure 3. Temporal variation of BZF decomposition for the studied systems.
Figure 3. Temporal variation of BZF decomposition for the studied systems.
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Figure 4. Comparison of the degradation efficiency of BZF and the variation of BOD5 for the studied ozonation cases with adjustment of the initial pH and the presence of BFS.
Figure 4. Comparison of the degradation efficiency of BZF and the variation of BOD5 for the studied ozonation cases with adjustment of the initial pH and the presence of BFS.
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Figure 5. Comparison of the accumulated oxalic acid for the studied ozonation cases with adjustment of the initial pH and the presence of BFS.
Figure 5. Comparison of the accumulated oxalic acid for the studied ozonation cases with adjustment of the initial pH and the presence of BFS.
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Figure 6. CNN forecasting (dashed orange line) for experimental data (blue solid line) of pH = 5 without metal slag.
Figure 6. CNN forecasting (dashed orange line) for experimental data (blue solid line) of pH = 5 without metal slag.
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Figure 7. CNN forecasting (dashed orange line) for experimental data (blue solid line) of pH = 10 without metal slag.
Figure 7. CNN forecasting (dashed orange line) for experimental data (blue solid line) of pH = 10 without metal slag.
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Figure 8. CNN forecasting (dashed orange line) for experimental data (blue solid line) of pH = 5 with metal slag.
Figure 8. CNN forecasting (dashed orange line) for experimental data (blue solid line) of pH = 5 with metal slag.
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Figure 9. CNN forecasting (dashed orange line) for experimental data (blue solid line) of pH = 10 with metal slag.
Figure 9. CNN forecasting (dashed orange line) for experimental data (blue solid line) of pH = 10 with metal slag.
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Figure 10. X-ray diffraction (XRD) of before and after the catalytic ozonation process, which indicates the stability of the crystalline structure. The identified crystalline phases are shown in the figure with their ICDS number.
Figure 10. X-ray diffraction (XRD) of before and after the catalytic ozonation process, which indicates the stability of the crystalline structure. The identified crystalline phases are shown in the figure with their ICDS number.
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Table 1. Recent contributions on metallurgic slags used as contributors to wastewater treatment.
Table 1. Recent contributions on metallurgic slags used as contributors to wastewater treatment.
ReferenceType of SlagPollutantOperating ConditionsMain Results
(Song, 2020) [25]Composite material of hydrotalcite-like loaded TiO2 (TiO2@ Mg-Al, LDH), prepared from Ti-bearing blast furnace slag (Ti-BFS)Tetracycline (TC)TC concentration: 50 mg/L, Ultraviolet lamp 0–5000 WTi-bearing composites mass 0.10 mg TiO2@ Mg-Al LDH was layered double hydroxide loading TiO2 structure and doped with metal elements like Fe, Mn, etc., which confers more HO•− h+ density on the surface of TiO2@Mg-Al LDH, and aromatic ring of TC can be attacked more effectively. The TC efficacy removal was up to 90% within 120 min, reaching up to 72% of mineralization degree.
(Rangappa, 2024) [26]Ground granulated blast furnace slag (GGBS)Tetracycline (TC)TC adsorption in GGBS, TCe concentration 20–100 mg/L, 50 mL of aqueous solution at 25 ± 3 °C room temperature, 6000 rpm/10 min, Contact time: 180 minThe higher removal of TC (68%) at an optimum adsorbent and pollutant dosage of 50 mg and 20 ppm.
(Chen, 2019) [27]Manganese slagSalicylic acidThree-dimensional electrode reactor (TDE)
Cell voltages: 5, 10, 15, 20, 25 y 30 V
pH: 1.00, 3.00, 5.00, 7.00, 9.00, 11.00
Salicylic acid concentration: 0.01, 0.05, 0.1, 0.15, 0.2, 0.3, 0.5 and 0.70 mg/L
The manganese slag as particle electrodes had been successfully loaded on the Cu/Fe and applied on the TDE to degrade Salicylic acid, reaching 76.9% of rate removal. Acetic acid was the main final product obtained.
(Song, 2022) [28]Hydrotalcite-like photocatalytic material (denoted CeTL) was prepared from titanium-bearing blast furnace slag (Ti-BFS)Tetracycline (TC)Tetracycline (TC) was used to evaluate the photochemical catalytic performance of CeTL under a 300 W Xenon lamp.
The catalyst of 20 mg was accurately weighed and added to the TC aqueous solution (20 ppm).
The degradation rate of (TC) by CeTL reached 92.8% after 90 min of illumination. The results of XRD, BET, UV-DRS, VB-XPS, and active species capture indicated that this might be the synergistic effect of good adsorption capacity and enhanced photocatalysis.
(Fasce, 2023) [29]Electric arc furnace slag (EAFS)Bisphenol A (BPA)BPA concentration: 20 mg/L
Temperature of room: 23–24 °C
3 h/1 L
Ozone gas flow: 700 mL/min
Ozone concentration: 10 mL/L EAFS improved the mineralization degree of BPA at acidic and alkaline conditions for those achieved in single ozonation processes. The high TOC conversion reached at alkaline pH (80%) was due to the generation of HO•− promoted by OH combined with precipitation reactions caused by Ca oxides. The improvement in the mineralization level at pH 3 (63%) was attributed to the activity of leached species, mostly Fe and Mn cations.
(Arzate-Salgado, 2016) [30]Two metallurgical wastes: one from the copper (COB) and other from the steel (MIT) industries, as Fenton-type photocatalystsDiclofenac (DCF)Xe arc lamp at 300–800 nm, and an air-cooled Xe lamp system with 5–6% photon emissions between 290 and 400 nm.
Diclofenac concentration of 500 mg/L
Stirrer agitated at 250 rpm at 35 °C
Based on the degradation rate constants of DCF, the COB/H2O2/simulated sunlight system showed a better performance than the COB/simulated sunlight system due to the contribution of hydrogen peroxide in the OH radical production.
Complete depletion of DCF was obtained after 90 and 150 min of reaction for the initial concentrations of 30 and 120 mg/L, respectively. The highest mineralization (87%) of this drug was achieved after 300 min of reaction time.
Table 2. Studied BZF systems for ozonation with and without catalyst.
Table 2. Studied BZF systems for ozonation with and without catalyst.
SystemInitial pH
Simple Ozonation Process (SOP)5.5
Simple Ozonation Process (SOP)10
BFS + Ozonation Process5.5
BFS + Ozonation Process10
Table 3. Physical and chemical BFS properties.
Table 3. Physical and chemical BFS properties.
PhysicChemical
SolidFeO: 40%
Color grayCaO: 22%
Density: 1.67 kg/m3SiO2 14%
Hardness: 7 in Mohs scaleMgO: 8.5%
Particle size: 3/8–¾ inAl2O3: 5.3%
MnO2: 1.6%
Table 4. Ozone consumed in each system studied.
Table 4. Ozone consumed in each system studied.
SystemOzone Consumed (mg/L)
SOP pH 5.575.64
SOP pH 1085.27
Ozonation pH 5.5 + BFS93.63
Ozonation pH 10 + BFS68.12
Table 5. Estimated reaction rate constant using the DNN estimated dynamics.
Table 5. Estimated reaction rate constant using the DNN estimated dynamics.
Reaction SystemReaction Rate Constant, L·mol·s−1
Oz pH 5.5145.67
Oz pH 10.01489.98
Oz pH 5.5 + BFS645.67
Oz pH 10.0 + BFS1689.34
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Galina-Licea, A.; Alfaro-Ponce, M.; Chairez, I.; Reyes, E.; Perez-Martínez, A. Assessment of Blast Furnace Slags as a Potential Catalyst in Ozonation to Degrade Bezafibrate: Degradation Study and Kinetic Study via Non-Parametric Modeling. Processes 2024, 12, 1998. https://doi.org/10.3390/pr12091998

AMA Style

Galina-Licea A, Alfaro-Ponce M, Chairez I, Reyes E, Perez-Martínez A. Assessment of Blast Furnace Slags as a Potential Catalyst in Ozonation to Degrade Bezafibrate: Degradation Study and Kinetic Study via Non-Parametric Modeling. Processes. 2024; 12(9):1998. https://doi.org/10.3390/pr12091998

Chicago/Turabian Style

Galina-Licea, Alexandra, Mariel Alfaro-Ponce, Isaac Chairez, Elizabeth Reyes, and Arizbeth Perez-Martínez. 2024. "Assessment of Blast Furnace Slags as a Potential Catalyst in Ozonation to Degrade Bezafibrate: Degradation Study and Kinetic Study via Non-Parametric Modeling" Processes 12, no. 9: 1998. https://doi.org/10.3390/pr12091998

APA Style

Galina-Licea, A., Alfaro-Ponce, M., Chairez, I., Reyes, E., & Perez-Martínez, A. (2024). Assessment of Blast Furnace Slags as a Potential Catalyst in Ozonation to Degrade Bezafibrate: Degradation Study and Kinetic Study via Non-Parametric Modeling. Processes, 12(9), 1998. https://doi.org/10.3390/pr12091998

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