Kinetic Modeling Approach Research Articles

The Effect of Hydrogen Peroxide on Biogas and Methane Produced from Batch Mesophilic Anaerobic Digestion of Spent Coffee Grounds

This paper aims to explore both experimental and modeling anaerobic digestion (AD) processes as innovative methods for managing the substantial quantities of spent coffee grounds (SCG) generated in Algeria, transforming them into valuable renewable energy sources (biogas/methane). AD of SCG, while promising, is hindered by its complex lignocellulosic structure, which poses a significant challenge. This study investigates the efficacy of hydrogen peroxide (H2O2) pretreatment in addressing this issue, with a particular focus on enhancing biogas and methane production. The AD of SCG was conducted over a 46-day period, and the impact of H2O2 pretreatment was evaluated using laboratory-scale batch anaerobic reactors. Four different concentrations of H2O2 (0.5, 1, 2, and 4% H2O2 w/w) were studied in mesophilic conditions (37 ± 2) for 24 h at room temperature, providing basic data on biogas and methane production. The results showed a significant increase in soluble oxygen demand (SCOD) and total sugar solubilization in the range of 555.96–713.02% and 748.48–817.75%, respectively. The optimal pretreatment was found to be 4% H2O2 w/w resulting in 16.28% and 16.93% improvements in biogas and methane yield over the untreated SCG. Further, while previous research has established oxidative pretreatment efficacy, this study uniquely combines the empirical analysis of H2O2 pretreatment with a detailed kinetic modeling approach using the modified Gompertz (MG) and logistic function (LF) models to estimate kinetic parameters and determine the accuracy of fit. The MG model showed the most accurate prediction, thus making the present investigation a contribution to understanding the performance of the AD system under oxidative pretreatment and designing and scaling up new systems with predictability. These findings highlight the potential of H2O2-pretreated SCG as a more efficient and readily available resource for sustainable waste management and renewable energy production.

Comparative photocatalytic degradation of cationic rhodamine B and anionic bromocresol green using reduced ZnO: A detailed kinetic modeling approach.

The photocatalytic degradation of rhodamine B (RhB), a cationic dye, and bromocresol green (BCG), an anionic dye, was investigated using oxygen vacancy-enriched ZnO as the catalyst. These dyes were selected due to their differing charges and molecular structures, allowing for a deeper exploration of how these characteristics impact the degradation process. The catalyst was prepared by reducing ZnO with 10% H2/Ar gas at 500°C, and the introduction of oxygen vacancies was confirmed using various characterization techniques. A detailed kinetic model was developed to track dye degradation, accounting for adsorption and photocatalytic degradation simultaneously, both in solution and on the catalyst surface. The model incorporated the effect of pH on adsorption by considering the dissociation behavior of the dyes and their respective pKa values. The study revealed that degradation primarily occurs on the catalyst surface at acidic pH, while at basic pH, degradation is more pronounced in the solution. DFT calculations supported these findings, showing that the electrostatic potential of the dyes shifts depending on pH, influencing their interaction with hydroxyl radicals or the catalyst surface. Quantum yield calculations indicate peak values of 6.32 10-5 molecules per photon for RhB at pH 11, and 4.20 10-5 for BCG at pH 3.

Kinetic Modelling and Machine Learning Approaches on the Conductance Transients of Zn0.5Ni0.5Fe2O4 Sensors for Addressing Cross-sensitivity towards Ethanol and Acetone Vapors.

The accurate discrimination among various volatile organic compounds, especially ethanol and acetone possess a serious concern for metal oxide based chemiresistive sensors. The work presents a systematic approach to address the issue by utilizing superior sensing potentiality of Zn0.5Ni0.5Fe2O4 coupled with efficient machine learning (ML) techniques. The work provides a thorough understanding on the synthesis, characterization of Zn0.5Ni0.5Fe2O4 nanoparticles and evaluating their sensing performance towards ethanol and acetone vapors. The optimized sensor performance recorded under varying concentrations (100-1000 ppm) of analytes across the range of temperature (225-300 °C) provides lesser selectivity between acetone and ethanol. TheLangmuir-Hinshelwoodreaction mechanism was invoked further to address the selectivity issue by modeling the response transients of the sensor to get an insight into sensing interaction at microscopic level. The estimated activation energy values of ethanol (0.26 eV) have been found to be smaller compared to that of acetone (0.34 eV), explaining little higher response of the sensor towards ethanol. Moreover, some efficient ML algorithms were employed to predictively analyze the acquired sensing data for achieving more precise discriminations between these two analytes.

Algorithm for Searching the Optimal Regulator Supply Mode in the Process of Manufacturing Polymer Products

Introduction. The high demand for polymer products creates the need for constant mo­dernization of the technological aspects of their production, increasing the efficiency of which is impossible without a model description and solving problems of optimization of its main technological stages. The current needs for manufacturing the products with a specified structure and properties make the issue of creating tools for solving optimization problems very relevant. One of the tools for controlling the product molecular weight is using the fractional mode to supply a regulator, the composition and dosage of which are often selected empirically.Aim of the Study. The aim of the study is to develop methods and algorithms to determine the mode for multipoint supplying a regulator in the continuous-running manufacturing of polymer products with specified molecular characteristics.Materials and Methods. To choose the optimal regulator supply mode, there is used a heuristic method represented by a genetic optimization algorithm. This algorithm is based on the mechanism for creating a population of potential solutions that are subjected to the operations of crossing, mutation and selection imitating the processes of inheritance and evolution in nature. To assess the molecular characteristics of the copolymerization pro­duct, there is applied a kinetic modeling approach based on the use of molecular weight distribution moments. For a mathematical description of continuous-running production, there are used recurrent relations characterizing the transfer of the reaction mass between ideally mixed reactors.Results. According to the conditions for organizing continuous-running manufacturing, it is possible to add a regulator at the beginning of the process in the third and sixth poly­merizers along the battery. In order to determine the regulator supply mode, the optimization criterion was developed in the form of a functional reflecting the absolute difference between the calculated and specified values of the number-average and mass-average molecular weights. The software implementation of the developed method and optimization algorithm, and the computational tests carried out made it possible to identify a number of solutions, each of which contributes to manufacturing a product with specified molecular characteristics. Visualization of some resulting solutions demonstrates different molecular weight dynamics throughout the process.Discussion and Conclusion. Through using the developed method and algorithm, there has been solved the problem of identifying the three-point molecular weight control regime for the continuous-running process of producing styrene-butadiene copolymer. The choice of a genetic algorithm for the study and optimization of complex multifactorial physicochemical systems is justified by the fact that it allows searching for one or more system parameters in both a discrete and continuous set of variables and contributes to finding a global optimum due to the random nature in the search for solutions. The variety of solutions obtained for the problem makes it possible to control the process of polymer synthesis in the case of constant monitoring of the physicochemical characteristics of the product.

Techno‐Economic Analysis of Glycerol Steam Reforming with Amine‐Based Carbon Capture for Blue Hydrogen Production: A Rate‐Based Kinetic Model Approach

ABSTRACTThis study outlines a comprehensive process design utilising glycerol‐steam reforming for an H2‐enriched gas stream, coupled with carbon dioxide removal via a chemical absorption system, followed by a techno‐economic analysis. The Aspen Plus economic analyser assesses the developed model, incorporating simulation results and literature data. Initially, the CO2 capture unit was planned with a standalone absorber and stripper, later integrated for solvent makeup calculation. Findings reveal that as catalyst loading increased from 5 to 50 kg, glycerol conversion and product molar fraction improved. For a targeted H2 production of 10 t/day, optimal reactor dimensions are 3.2 m diameter and 30 m length, corresponding to a reactant flow of 105 t/day and a 2.52 MW heat duty at stoichiometry conditions. To capture 95% CO2 from the reformed product stream, absorber and stripper packing heights of 12 and 7 m, respectively, with column diameters of 1.25 and 2.71 m are necessary. The production cost of H2 is determined to be $3.8 per kg, as revealed by the techno‐economic analysis. Calculated values for net present value, discounted payback period, and internal rate of return stand at $30 million, 5 years, and 25.0%, respectively. © 2024 Society of Chemical Industry and John Wiley & Sons, Ltd.

Insight into the generation network of reactive oxygen species in H2O under nonthermal atmospheric-pressure plasma irradiation using a kinetic modeling approach

A kinetic model incorporating the dominant generation networks of reactive oxygen species (ROS) in H2O under nonthermal atmospheric-pressure plasma irradiation was developed in this study. In the proposed model, the minimum necessary elementary reactions involving ROS were considered based on the behaviors of the dissolved O2 and H2O2 observed in plasma-irradiated H2O. The ROS included not only OH radicals but also other ROSs such as OOH, O, and these anionic radicals. The model agrees well with the experimental results for various initial dissolved O2 concentrations, pH values, and input power conditions. Further, the model could predict specific behavior under extrapolated conditions, which are in the presence of H2O2 at the initial conditions. In addition, the reaction rates of elementary reactions involving dissolved O2, which are difficult to measure in the experiments, were simulated by the model. It was found that the reaction rates between the consumption and production reactions of dissolved O2 were balanced at later stages of nonthermal atmospheric-pressure plasma irradiation. The model is expected to provide fundamental guidelines for the effective utilization of nonthermal atmospheric-pressure plasma irradiation.

Optimization of heterogeneous continuous flow hydrogenation using FTIR inline analysis: a comparative study of multi-objective Bayesian optimization and kinetic modeling

Heterogeneous continuous flow hydrogenation is important in the chemical industry, yet the simultaneous optimization of yield and productivity has historically been complex. This study introduces a heterogeneous continuous flow hydrogenation system specifically designed for preparing 2-amino-3-methylbenzoic acid (AMA), employing FTIR inline analysis coupled with an artificial neural network model for monitoring. We explored two distinct reaction optimization strategies: multi-objective Bayesian optimization (MOBO) and mechanism-based intrinsic kinetic modeling, executed in parallel to optimize reaction conditions. Remarkably, the MOBO approach achieved an optimal AMA yield (99%) and productivity (0.64 g/hour) within a limited number of iterations. In comparison, despite requiring extensive experimental data collection and equation fitting, the intrinsic kinetic modeling approach yielded a similar optimal result. Thus, while MOBO offers a more efficient route with fewer required experiments, kinetic modeling provides deeper insights into the optimization landscape but may be impacted by non-chemical kinetic phenomena and requires significant time and resources.

Magnetic sludge-derived biochar for Cu (II) removal: RSM-based preparation and BP artificial neural network adsorption modeling

Magnetic sludge-derived biochar (MSDB) was synthesized using response surface methodology (RSM) optimization, with sludge as the raw material and sodium pyrophosphate as the modifier. The structure and surface properties of MSDB were characterized using scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), vibrating sample magnetometer (VSM), and Brunauer-Emmett-Teller (BET) analysis. These analyses revealed that MSDB is rich in functional groups and exhibits magnetic properties, facilitating the adsorption of Cu(II) via complexation and easy separation of MSDB through magnetic means. Batch adsorption experiments showed that under conditions of 25 °C and pH 3.0, with an MSDB dosage of 0.75 g L−1, a Cu(II) removal efficiency of 95.67 % was achieved. Thermodynamic studies demonstrated maximum adsorption capacities of 173.69 mg g−1 at 25 °C, 183.26 mg g−1 at 35 °C, and 197.96 mg g−1 at 45 °C. Additionally, the BP neural network model showed a high coefficient of determination (R2 = 0.9396) and a low mean squared error (MSE = 0.0268) in simulating the MSDB-Cu(II) adsorption process, confirming the robustness of the model in both data simulation and predictive accuracy. This method offers a promising approach for kinetic modeling and predicting adsorption behavior in related fields of study.

Deep insights on the Co-pyrolysis of tea stem and polyethylene terephthalate (PET): Unveiling synergistic effects and detailed kinetic modeling

This study investigates the co-pyrolysis of tea stem (TS) and polyethylene terephthalate (PET), with an emphasis on their interactions during the pyrolysis process. The effects of heating rate and TS/PET ratio on synergy of TS with PET pyrolysis was examined using thermogravimetric analysis (TGA) and Fourier transform infrared spectroscopy (FT-IR) techniques. The experimental runs were conducted at three mixing ratios (i.e., 3:1, 1:1, and 1:3) of TS to PET and four heating rates (i.e., 10, 15, 20, and 25 °C/min). The results show that the TS/PET ratio is the main factor determining the synergy of the two components. In particular, the mixture of 25 % TS and 75 % PET exhibited the least antagonistic effects, while the mixture of 75 % TS and 25 % PET had strong synergistic positive and negative effects. Moreover, the FT-IR analysis of the co-pyrolysis process suggested the presence of oxygenated compounds, including hydroxyl and carbonyl groups, indicating the potential formation of aromatic hydrocarbons in the co-pyrolysis process. A new kinetic modeling approach including seven independent parallel reactions (IPR), was developed to illustrate the complex decomposition behavior of the TS-PET mixture. The outcome of kinetic modeling using nonlinear multivariate optimization resulted in a quality of fit ranging from 1.38 % to 3.55 %. These results accurately depict the experimental evidence and offer useful understanding of the reaction kinetics and the overall influence of the mixing ratio on the kinetic triplets.

A kinetic modelling approach to explore mechanism of Cr(VI) detoxification by a novel strain Pseudochrobactrum saccharolyticum NBRI-CRB 13 using response surface methodology.

A novel Pseudochrobactrum saccharolyticum strain NBRI-CRB 13, isolated from tannery sludge, was studied to grow up to 500 mgL-1 of Cr(VI) and showed Cr(VI) detoxification by reducing > 90% of Cr(VI) at different concentrations 25, 50 and 100 mgL-1. Kinetic studies showed that first-order models were fitted (R2 = 0.998) to the time-dependent Cr(VI) reduction with degradation rate constant (k) (1.03-0.429h-1). Cr(VI) detoxification was primarily related to the extracellular fraction of microbial cells, which showed a maximum extracellular reductase enzyme activity led to 94.6% reduction of Cr(VI). Moreover, the strain showed maximum extracellular polymeric substances (EPS) production at 100 mgL-1 Cr(VI), which is presumably the reason for Cr(VI) removal as EPS serves as the metal binding site for Cr(VI) ions. Further, an optimization study using Box-Behnken design was conducted considering parameters viz., pH, temperature, and initial concentration of Cr(VI). The maximum percent reduction of Cr(VI) was obtained at pH 6.5, temperature 30°C with 62.5 mgL-1Cr(VI) concentration. Further, the Cr(VI) reduction and adsorption ability of strain P. saccharolyticum NBRI-CRB13 were confirmed by SEM-EDS, FTIR, and XRD analyses. FTIR analysis confirmed the presence of functional groups (-OH, -COOH, -PO4) on bacterial cell walls, which were more likely to interact with positively charged chromium ions. The study elucidated the reduction of Cr(VI) by the novel bacterium within 24h using the response surface methodology approach and advocated its application in real-time situations.

Study of heavy crude oil upgrading in supercritical water using diverse kinetic approaches

Heavy crude oil upgrading under a supercritical water environment has demonstrated to have diverse advantages. For commercial applications, it is necessary to develop mathematical models that can predict the reactive behavior of the different components of crude oil under these operating conditions. However, the consideration of different experimental and modeling approaches can significantly influence the determination and evaluation of reaction rates, which complicates providing essential insights into the reaction system and thus generates numerical simulations and strategies that allow for increasing the efficiency of these processes. Therefore, this study analyzes the effect of the supercritical water atmosphere on the diverse reactions among oil components as well as different kinetic modeling approaches reported in the literature to predict the yields of reactive compounds. Complex kinetic models were developed using proposed reaction schemes and experimental data obtained during the upgrading of heavy crude oil under supercritical water conditions. The predictions with the models show good agreement concerning the experimental data. In addition, it was shown that the conversion of asphaltenes to resins and gas compounds under supercritical conditions presents a significant increase in reaction rates compared with experiments under non-catalytic and catalytic thermal cracking conditions.

Heparosan biosynthesis in recombinant Bacillus megaterium: Influence of N-acetylglucosamine supplementation and kinetic modeling.

Heparosan, an unsulfated polysaccharide, plays a pivotal role as a primary precursor in the biosynthesis of heparin-an influential anticoagulant with diverse therapeutic applications. To enhance heparosan production, the utilization of metabolic engineering in nonpathogenic microbial strains is emerging as a secure and promising strategy. In the investigation of heparosan production by recombinant Bacillus megaterium, a kinetic modeling approach was employed to explore the impact of initial substrate concentration and the supplementation of precursor sugars. The adapted logistic model was utilized to thoroughly analyze three vital parameters: the B. megaterium growth dynamics, sucrose utilization, and heparosan formation. It was noted that at an initial sucrose concentration of 30gL-1 (S1), it caused an inhibitory effect on both cell growth and substrate utilization. Intriguingly, the inclusion of N-acetylglucosamine (S2) resulted in a significant 1.6-fold enhancement in heparosan concentration. In addressing the complexities of the dual substrate system involving S1 and S2, a multi-substrate kinetic models, specifically the double Andrew's model was employed. This approach not only delved into the intricacies of dual substrate kinetics but also effectively described the relationships among the primary state variables. Consequently, these models not only provide a nuanced understanding of the system's behavior but also serve as a roadmap for optimizing the design and management of the heparosan production method.

Kinetic modeling of a Sznajd opinion model on social networks

In this paper, we study a kinetic modeling of opinion formation on social networks in which the distribution function depends on both the opinion and the connectivity of the agents. The opinion exchange process is governed by a Sznajd-type model with three opinions, [Formula: see text], 0, and the social network is represented statistically with connectivity denoting the number of contacts of a given individual. It is commonly accepted that, in social networks, the opinion of the agents with a higher connectivity, i.e. a larger number of followers, is more convincing than that of the agents with a lower number of followers. By the kinetic modeling approach, we derived the asymptotic mean opinion of a social network in terms of the initial opinion and the connectivity of the agents. The present work extends the result in [N. Loy, M. Raviola and A. Tosin, Opinion polarisation in social networks, Phil. Trans. R. Soc. A 380, 20210158 (2021)] where only binary opinions, [Formula: see text], were considered, that indecisive people who cannot make a clear choice between [Formula: see text] are allowed in this study.

Research on the strengthening mechanism of Nb–Ti microalloyed ultra low carbon IF steel

Interstitial free (IF) steels are characterized by their interstitial free ferritic matrix due to addition of micro-alloying elements that draw carbon and nitrogen out of matrix to form carbonitrides. The IF steels typically have excellent deep drawing ability, certain mechanical strength, and high plastic deformation performance. In this paper, the microstructure evolution and carbides precipitation behavior during the hot rolling, cold rolling and annealing processes have been studied using both experimental and thermodynamic and kinetic modeling approaches. The strengthening mechanism of Nb–Ti microalloyed IF steel has been investigated. The results of the thermodynamic simulation indicates that the addition of Nb into Ti bearing IF steel promotes recrystallization at 900 °C, leading to fine recrystallized grains. Due to the recrystallization of austenite during the final rolling process, Nb–Ti microalloyed hot rolled plates exhibit finer grain size, fewer substructures, and lower dislocation density compared to the hot rolled plates containing only Ti. Building upon the initial fine and uniform structure, Nb–Ti microalloyed steel maintains a refined structure after undergoing cold rolling and annealing. The study of the carbides precipitation behavior suggests that TiN precipitates at high temperature above 1100 °C, while a substantial amount of (Nb,Ti)C and TiC precipitate at about 900 °C in Nb–Ti and Ti bearing steels, respectively. There is no significantly difference between the amount of carbides in steels hot deformed at 900 °C and in the hot rolled plates, indicating that the carbides mainly precipitate during the hot rolling process. There are negligible carbides precipitated during the coiling process at 600 °C. In Nb–Ti containing steel, Nb and Ti in solid solution are significantly reduced due to the large amount precipitation of (Nb,Ti)C during the final rolling process. The reduction of solid solution Nb effectively promotes the recrystallization of austenite. Therefore, the grains of hot-rolled, cold-rolled, and annealed strips are refined. In addition, there are more small size carbides in Nb–Ti bearing steel than that in Ti bearing steel. Therefore, the higher strength of Nb–Ti steel is mainly from the strengthening effect of grain refinement and carbides precipitation.

A kinetic theory approach to modeling prey–predator ecosystems with expertise levels: analysis, simulations and stability considerations

In this paper a kinetic modeling approach for an ecological system is proposed, based on the prey–predator structure in terms of individuals experience. Specifically, a system of nonlinear kinetic equations is formulated using standard tools. Both conservative and nonconservative events are considered since proliferative/destructive rates and an external force field occur. These results are compared to the ones of the classical theory of dynamical system to show their consistency. In addition to a first local analytical result, some numerical simulations are performed. The results thus obtained ensure that the kinetic model behaves in a way consistent with other similar ecological models formulated by dynamical systems, involving ordinary differential equations with lumped dependent variables. For each simulation, stationary solutions are shown. Moreover, oscillations appear for some values of the parameters of the ecological system, and this suggests that bifurcations may appear.

Experimentally implemented dynamic optogenetic optimization of ATPase expression using knowledge-based and Gaussian-process-supported models

Optogenetic modulation of adenosine triphosphatase (ATPase) expression represents a novel approach to maximize bioprocess efficiency by leveraging enforced adenosine triphosphate (ATP) turnover. In this study, we experimentally implement a model-based open-loop optimization scheme for optogenetic modulation of the expression of ATPase. Increasing the intracellular concentration of ATPase, and thus the level of ATP turnover, in bioprocesses with product synthesis coupled with ATP generation, can lead to increased substrate uptrake and product formation. Previous simulation studies formulated optimal control problems using dynamic constraint-based models to find optimal light inputs in fermentations with optogenetically mediated ATPase expression. However, using these models poses challenges due to resulting bilevel optimizations and complex parameterization. Here, we outline a simplified unsegregated and quasi-unstructured kinetic modeling approach that reduces the number of dynamic states and leads to single-level optimizations. The models can be augmented with Gaussian processes to compensate for model uncertainties. We implement optimal control constrained by knowledge-based and hybrid models for optogenetic ATPase expression in Escherichia coli with lactate as the main product. To do so, we genetically engineer E. coli to obtain optogenetic expression of ATPase using the CcaS/CcaR system. This represents the first experimental implementation of model-based optimization of ATPase expression in bioprocesses.

Investigating the effects of drying on the physical properties of Kombucha Bacterial Cellulose: Kinetic study and modeling approach

Kombucha bacterial cellulose (KBC), obtained as a by-product of tea fermentation, has potential applications in diverse fields. The high water content and presence of post-fermentation residues necessitate the implementation of suitable drying conditions that can significantly affect the final characteristics of this versatile biopolymer. Thus, to study the effect of different drying methods on its properties, KBC was subjected to microwave drying (180–900 W), hot air oven drying (30–70 °C), and shade drying (25 °C). Additionally, the acquired data were fitted into ten different models to study the drying kinetics of KBC. The dried sheets were then analyzed to study the changes in water absorption and holding capacity, rehydration, surface color, and other physicomechanical properties (UTM, XRD, and contact angle analysis). Hence, optimization of the drying conditions will help reduce the time and cost without compromising any of the intrinsic properties that allow it to be processed into various value-added products.

The Environmental Oxidation of Acetaminophen in Aqueous Media as an Emerging Pharmaceutical Pollutant Using a Chitosan Waste-Based Magnetite Nanocomposite

Clean water is a precious and limited resource that plays a crucial role in supporting life on our planet. However, the industrial sector, especially the pharmaceutical industry, significantly contributes to water consumption, and this can lead to water body pollution. Fenton’s reagent was introduced in the current investigation to oxidize acetaminophen as an emerging pollutant in such effluents. Therefore, we employed a straightforward co-precipitation method to fabricate chitosan-coated magnetic iron oxide, which is referred to in this study as Chit@Fe3O4. X-ray diffraction spectroscopy (XRD), Fourier transform infrared (FTIR), diffuse reflectance spectra (DRS), scanning electron microscopy (TEM), and transmission electron microscopy (TEM) were utilized to characterize the sample. It is crucial to treat such effluents due to the rapid increase in emerging pollutants. In this study, a photo-Fenton system was introduced as a combination of a Chit@Fe3O4 catalyst augmented with hydrogen peroxide under ultraviolet (UV) illumination conditions. The results reveal that only 1 h of irradiance time is efficient in oxidizing acetaminophen molecules. Doses of 20 and 200 mg/L of Chit@Fe3O4 and H2O2, respectively, and a pH of 2.0 were recorded as the optimal operational conditions that correspondingly oxidize 20 mg/L of acetaminophen to a 95% removal rate. An increase in the reaction temperature results in a decline in the reaction rate, and this, in turn, confirms that the reaction system is exothermic in nature. The sustainability of the catalyst was verified and deemed adequate in treating and oxidizing acetaminophen, even up to the fourth cycle, achieving a 69% removal rate. A kinetic modeling approach is applied to the experimental results, and the kinetic data reveal that the oxidation system conforms to second-order kinetics, with rate constants ranging from 0.0157 to 0.0036 L/mg·min. Furthermore, an analysis of the thermodynamic parameters reveals that the reaction is exothermic and non-spontaneous, predicting an activation energy of 36.35 kJ/mol. Therefore, the proposed system can address the limitations associated with the homogeneous Fenton system.

Deciphering SO2 poisoning mechanisms for passive NOx adsorption: A kinetic modeling approach and development of a high-resistance catalyst

Passive NOx adsorption (PNA) is a promising technology aimed at reducing NOx emissions from vehicles during the cold start phase of the engine. This work investigated the SO2 poisoning mechanism of PNA through a combination of experimental research and kinetic modeling, leading to the development of a novel PNA sample with high resistance to SO2 poisoning. Pd/SSZ-13 samples were synthesized using different drying conditions, revealing that samples dried at room temperature showed lower degradation (10 %) compared to those dried at 80 °C (26 %). Investigation into the degradation revealed that ion-exchanged Pd sites with a hydroxyl group were more resistant to SO2 poisoning than other Pd sites. It is also found that SO2 aids in NOx storage on Pd sites, enhancing the PNA performance. A kinetic model was developed to describe the SO2 poisoning behavior and its influence on NOx storage. The model, which was verified under various conditions, effectively simulated the PNA behavior and SO2 poisoning of Pd/SSZ-13.

Analysis of Photochemical Characteristics and Sensitivity of Atmospheric Ozone in Nanjing in Summer

Tropospheric ozone (O3) is mainly produced through a series of photochemical reactions of nitrogen oxides (NOx) and volatile organic compounds (VOCs). The reaction process presents complex non-linear relationships. In this work, datasets of atmospheric ozone and volatile organic compounds (VOCs) observed during the summer of 2018 in Nanjing were used. Combining with the framework for 0-D atmospheric model-master chemical mechanism (F0AM-MCM), the characteristics of photochemical reactions for ozone (O3) formation in Nanjing during the O3 episode days and non-episode days were investigated. The results showed that φ(O3) and φ(TVOCs) in the O3 episode days were 47.8×10-9 and 49.0×10-9, respectively, exceeding those in the non-episode days by factors of 1.8 and 1.6. Furthermore, F0AM, the empirical kinetic modeling approach (EKMA), and relative incremental reactivity (RIR) were utilized for the calculation of ozone chemical sensitivity. It was found that O3 formation in Nanjing was attributed to both VOCs and NOx limitation. In addition, the modeled ·OH and HO2 concentrations in the O3 episode days were 1.3 and 1.8 times higher than those in the non-episode days. The higher formation and loss rates of ·OH and HO2 were also found during O3 episode days. These findings reflected that the enhancements of atmospheric oxidation capacity resulted in increased production rates of O3, providing an explanation for the enhancements of O3 concentrations in Nanjing during the O3 episode days. The findings also improved the understanding of the O3 photochemical characteristics over Nanjing in the summer during the O3 episode days.