Pioneering the future: A trailblazing review of the fusion of computational fluid dynamics and machine learning revolutionizing plasma catalysis and non-thermal plasma reactor design

Citation: Arshad, M.Y.; Ahmad, A.S.; Mularski, J.; Modzelewska, A.; Jackowski, M.; Pawlak-Kruczek, H.; Niedzwiecki, L. Pioneering the Future: A Trailblazing Review of the Fusion of Computational Fluid Dynamics and Machine Learning Revolutionizing Plasma Catalysis and Non-Thermal Plasma Reactor Design. Catalysts 2024, 14, 40. https://doi.org/10.3390/ catal14010040 Academic Editors: Huijuan Wang, Aurora Santos and He Guo Received: 29 September 2023 Revised: 14 December 2023 Accepted: 27 December 2023 Published: 6 January 2024 Copyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). catalysts Review Pioneering the Future: A Trailblazing Review of the Fusion of Computational Fluid Dynamics and Machine Learning Revolutionizing Plasma Catalysis and Non-Thermal Plasma Reactor Design Muhammad Yousaf Arshad 1,2,* , Anam Suhail Ahmad 3, Jakub Mularski 4, Aleksandra Modzelewska 5, Mateusz Jackowski 5, Halina Pawlak-Kruczek 4and Lukasz Niedzwiecki 4,6,* 1Corporate Sustainability and Digital Chemical Management, Interloop Limited, Faisalabad 38000, Pakistan 2School of Chemical Engineering, University of Adelaide, Adelaide, SA 5005, Australia 3Halliburton Worldwide, Houston, TX 77032-3219, USA; [email protected] 4Department of Energy Conversion Engineering, Wrocław University of Science and Technology, Wyb. Wyspia´nskiego 27, 50-370 Wrocław, Poland; [email protected] (J.M.); [email protected] (H.P.-K.) 5 Department of Micro, Nano and Bioprocess Engineering, Faculty of Chemistry, Wroclaw University of Science and Technology, Norwida 4/6, 50-373 Wroclaw, Poland; [email protected] (A.M.); [email protected] (M.J.) 6Energy Research Centre, Centre for Energy and Environmental Technologies, VŠB—Technical University of Ostrava, 17. Listopadu 2172/15, 708-00 Ostrava, Czech Republic *Correspondence: [email protected] (M.Y.A.); [email protected] (L.N.) Abstract: The advancement of plasma technology is intricately linked with the utilization of computational fluid dynamics (CFD) models, which play a pivotal role in the design and optimization of industrial-scale plasma reactors. This comprehensive compilation encapsulates the evolving landscape of plasma reactor design, encompassing fluid dynamics, chemical kinetics, heat transfer, and radiation energy. By employing diverse tools such as FLUENT, Python, MATLAB, and Abaqus, CFD techniques unravel the complexities of turbulence, multiphase flow, and species transport. The spectrum of plasma behavior equations, including ion and electron densities, electric fields, and recombination reactions, is presented in a holistic manner. The modeling of non-thermal plasma reactors, underpinned by precise mathematical formulations and computational strategies, is further empowered by the integration of machine learning algorithms for predictive modeling and optimization. From biomass gasification to intricate chemical reactions, this work underscores the versatile potential of plasma hybrid modeling in reshaping various industrial processes. Within the sphere of plasma catalysis, modeling and simulation methodologies have paved the way for transformative progress. Encompassing reactor configurations, kinetic pathways, hydrogen production, waste valorization, and beyond, this compilation offers a panoramic view of the multifaceted dimensions of plasma catalysis. Microkinetic modeling and catalyst design emerge as focal points for optimizing CO 2 conversion, while the intricate interplay between plasma and catalysts illuminates insights into ammonia synthesis, methane reforming, and hydrocarbon conversion. Leveraging neural networks and advanced modeling techniques enables predictive prowess in the optimization of plasma-catalytic processes. The integration of plasma and catalysts for diverse applications, from waste valorization to syngas production and direct CO 2 /CH 4 conversion, exemplifies the widereaching potential of plasma catalysis in sustainable practices. Ultimately, this anthology underscores the transformative influence of modeling and simulation in shaping the forefront of plasma-catalytic processes, fostering innovation and sustainable applications. Keywords: plasma; non-thermal plasma reactors; plasma catalysis Catalysts 2024,14, 40. https://doi.org/10.3390/catal14010040 https://www.mdpi.com/journal/catalysts Catalysts 2024,14, 40 2 of 24 1. Introduction Recent developments in terms of building sustainable energy systems have led to a significant increase in the installed power of intermittent renewable energy sources (solar and wind). This has led to a mismatch between supply and demand. Therefore, notable effort has recently been directed to improving the flexibility of power systems, which includes both increasing the flexibility of controllable power sources [ 1 , 2 ] and demandside management. Capacity markets have been created in recent years in many countries, including the Scandinavian countries [ 3 , 4 ], Great Britain [ 5 , 6 ], and Poland [ 7 ]. The flexibility of power units can be increased through the use of plasma to support combustion in power plant boilers [ 8 – 10 ]. However, an equally good measure to improve the flexibility and uptake of energy systems is increasing demand relative to supply from intermittent sources, which is called demand-side management. Within demand-side management, technologies called power-to-X are of particular interest [ 11 , 12 ] since they are capable of utilizing excess electricity during low-demand periods and converting it to marketable products. The X in power-to-X stands for a product, and it could indicate hydrogen (PtH) [ 12 ], methanol (PtM) [ 12 ], or ammonia (PtA) [ 12 ]. The advantage of this approach is not only that it results in a new product produced by using electricity purchased at a marginal price but also that it avoids the curtailment of intermittent renewable energy sources at a time of high supply and low demand. In the long-term, changing the energy grid in this direction will result in higher shares of renewable energy in the power mix. In recent years, increased interest has been shown in the application of plasma for hydrogen production [ 13 , 14 ], the catalytic synthesis of ammonia [ 15 – 18 ], the methanation of CO 2 to synthetic natural gas [ 19 , 20 ], the conversion of CO 2 to alcohols [ 21 ], and the production of nanomaterials [ 22 ], to name but a few applications. Since plasma is typically characterized by its fast startup [ 23 – 26 ], plasma-based technologies are of particular interest with respect to their increased use in the demand-side management of power systems. Achieving this requires industrial-scale power-to-X plasma solutions [11]. Non-thermal plasma (NTP), which is in a non-equilibrium state with different temperatures of electrons, ions, and neutrons [ 27 ], has been proven effective in many different applications. Clothiaux, Koropchak, and Moore demonstrated the possibility of the decomposition of the vapor of phosphonofluoridic acid methyl-l,2,2-trimethylpropyl ester using a silent discharge plasma [ 28 ]. Dobslaw et al. [ 29 ] confirmed the feasibility of using non-thermal plasma for the removal of volatile organic compounds and odor abatement in organic waste treatment plants. In another work, Dobslaw et al. [ 30 ] showed a synergy between a non-thermal plasma (dielectric barrier discharge) and the subsequent biotrickling filter. Andersen et al. [ 31 ] proved that NTP systems could be effectively used for the abatement of odor nuisances. NTP could also be applied for water purification [ 32 ] as well as for disinfection and sterilization purposes [ 32 , 33 ]. Apart from decontamination, NTP can also be effectively used in wastewater treatment plants [34], providing a feasible means of dealing with hazardous chemicals [ 35 ]. Moreover, Kvam et al. [ 36 ] reported that NTP is capable of inducing damage to the cell surface, which is critical in terms of medical applications, such as disinfection of air to remove multidrug-resistant microbes. Dobslaw and Glocker [ 37 ] reported the 100% degradation of CF 4 -contaminated air at an industrial scale using NTP. Helbich et al. [ 38 ] demonstrated the successful use of NTP for the treatment of emissions of styrene and secondary emissions of germs formed through biological processes, with the efficiencies of removal of volatile organic compounds, germs, and styrene ranging between 96 and 98%. Byeon et al. [ 39 ] investigated the removal of gaseous toluene and submicron aerosol particles using a dielectric barrier discharge reactor and reported toluene removal efficiencies ranging between 29% and 46%, depending on the applied voltage, frequency, upstream toluene concentration, and residence time. Nonetheless, caution is needed in such applications since the conversion of toluene in NTP could generate heavy aromatic byproducts, such as phenylethyne, indene, naphthalene, and acenaphthylene, as shown by Wnukowski and Moro´n for microwave plasma [ 40 ]. NTP plasma has been used for various applications related to gasification technologies, Catalysts 2024,14, 40 3 of 24 especially concerning gasification of waste [ 41 ] and removal of tars from producer gas [ 42 ], which is critical from the point of view of the maintenance and uninterrupted operation of gasifiers [ 43 , 44 ]. Wnukowski et al. [ 45 ] reported tar conversion efficiencies ranging between 19 and 100% for atmospheric microwave plasma processing of producer gas from gasification of sewage sludge. Kwon and Im [ 46 ] investigated the feasibility of non-thermal plasma gasification for a waste-to-energy power plant with an integrated plasma gasification combined cycle. Performed exergy analysis has shown that in such systems, the plasma gasification unit is the one that contributes the most to the total exergy loss [ 46 ]. Since plasma reactors exert such a profound effect on the overall systemic energetic and exergetic efficiency, it is crucial to have the right toolset for efficient design of such reactors. The march of industrial-scale plasma reactors into new frontiers hinges upon the seamless integration of intricate computational fluid dynamics (CFD) models, which have emerged as indispensable tools for augmenting reactor design and optimization [ 47 , 48 ]. Within the multifaceted arena of plasma generation and its manifold applications, a confluence of variables forms, encapsulating the intricacies of fluid dynamics, chemical kinetics, heat transfer, and radiation energy [ 49 , 50 ]. This introduction serves as a beacon, illuminating the profound significance of computational simulations in decoding the labyrinthine physical and chemical orchestration within plasma reactors. Esteemed software frameworks, including FLUENT, Python, MATLAB, and Abaqus, serve as the very scaffold upon which intricate numerical fluid models are woven, facilitating meticulous scrutiny of phenomena as diverse as turbulence, multiphase flow, and the intricate migration of species. Researchers, through relentless inquiry, unveil the pivotal role of microkinetic modeling and the optimization of catalysts in propelling the engines of efficient CO 2 conversion [51,52]. Against this backdrop, modeling methodologies, such as the intricate weave of neural networks, emerge as indispensable allies, forecasting and refining the efficacy of plasmacatalytic endeavors. The fusion of plasma and catalysts to birth novel valorization pathways, orchestrate syngas symphonies, and affect the metamorphosis of CO 2 and CH 4 stands as a testament to the diverse potential of plasma catalysis within the annals of sustainable practices. The aim of this review is to summarize the works on modeling plasma and its various chemical engineering applications, thus providing an overview of the approaches used for modeling catalytic plasma processes. 2. Modeling and Simulation in Plasma Catalysis: Insights, Strategies, and Applications Recent investigations underscore the pivotal significance of modeling and simulation in propelling the frontier of plasma catalysis research. Numerous scholarly articles delve into the application of fluid models within the realm of plasma catalysis, meticulously emphasizing their indispensable role in elucidating phenomena such as streamer propagation, plasma dynamics, and the intricate interplay between plasma and catalyst [ 53 – 55 ]. Strategically optimizing plasma parameters and adeptly managing the fluxes of reactive species stand out as pivotal facets in the realm of plasma catalysis. Research endeavors leverage sophisticated modeling techniques to scrutinize and proffer strategic approaches for fine-tuning reactive species within plasma catalytic systems [56,57]. Innovatively navigating the landscape of modeling approaches, this review meticulously unveils a transformative realm within the artificial neural network (ANN) models and microfluidic chip-based plasma flow chemistry techniques. Beyond conventional bounds, it delves into cutting-edge investigations on surface-induced effects and plasmacatalytic methane reforming. Employing sophisticated fluid modeling techniques, these dedicated inquiries yield profound insights into the intricate dynamics of gas-phase redistribution effects and the nuanced influence exerted by catalyst surfaces. Cumulatively, this scholarly synthesis accentuates the novel contributions that transcend traditional boundaries, underscoring the paramount significance of modeling and simulation. Specifically harnessed through fluid models, this research not only unravels the intricacies of plasma Catalysts 2024,14, 40 4 of 24 behavior but also steers the precise control of reactive species, presenting a pioneering blueprint for fine-tuning plasma-catalytic reactors across diverse applications within the expansive domain of plasma catalysis [58–61]. 2.1. Exploring Modeling and Simulation in Plasma Catalysis for Ammonia Synthesis: Insights and Innovations Research in plasma catalysis for ammonia synthesis has been heavily influenced by various modeling and simulation techniques, which have played a vital role in gaining insights and optimizing the process. A significant portion of the literature focuses on microkinetic modeling and fluid dynamics, seeking to explore the contributions of Eley–Rideal reactions in ammonia synthesis under plasma conditions [ 62 – 64 ]. Moreover, researchers have combined kinetic modeling with experimental studies in dielectric barrier discharge reactors to better understand the intricate reaction mechanisms [64,65]. Another key area of research in plasma-catalytic ammonia synthesis is the investigation of plasma effects and reaction mechanisms. Studies have delved into the influence of the material dielectric constant on plasma generation within catalyst pores, revealing important considerations for plasma–catalyst interactions [ 55 ]. Additionally, the enhancement of reaction rates beyond the thermodynamic equilibrium in plasma-catalyzed ammonia synthesis has been studied, opening up new possibilities for more efficient ammonia production [66]. Furthermore, researchers have explored the direct conversion of CO 2 and CH 4 into liquid chemicals using plasma catalysis. Experimental studies and simulations have shown promising results in converting CO 2 and CH 4 into valuable products [ 67 ]. Meanwhile, other works have emphasized the importance of modeling plasma chemistry and reactor design to optimize the efficiency of CO2conversion through plasma catalysis [68]. In the realm of modeling techniques, artificial neural networks (ANNs) and deep learning have been harnessed to enhance plasma catalysis. ANNs have proven useful in predicting the performance of ammonia synthesis in plasma catalysis, enabling better control and optimization of the processing [ 69 ]. Similarly, deep learning techniques have been explored in modeling pulsed discharge plasma catalysis, offering novel perspectives for understanding and improving plasma-catalytic reactions [70]. The collective body of work presented in these articles showcases the indispensable significance of modeling and simulation techniques, encompassing microkinetic modeling, fluid dynamics, and artificial neural networks, in advancing plasma catalysis for ammonia synthesis. The studies shed light on plasma effects, reaction mechanisms, and the conversion of CO 2 and CH 4 , offering valuable insights into the behavior of plasma-catalytic systems and their potential applications in sustainable ammonia production. As the field of plasma catalysis continues to evolve, these modeling approaches will undoubtedly play a central role in unlocking new horizons for efficient and environmentally friendly ammonia synthesis processes. 2.2. Advancing Ammonia Synthesis through Plasma Catalysis: Unveiling Insights and Innovations via Modeling and Simulation Research in plasma catalysis has made remarkable progress in comprehending catalyst surfaces, microkinetic modeling, and reaction mechanisms. The first group of articles places emphasis on the significance of catalyst surfaces and microkinetic modeling in plasma catalysis. Wang et al. [ 71 ] investigate the role of a Ni/Al 2 O 3 catalyst surface in ammonia synthesis using plasma-enhanced catalysis at near-room temperature. Their utilization of density functional theory (DFT) microkinetic modeling yields valuable insights into reaction mechanisms, highlighting the catalyst surface’s importance in achieving efficient ammonia synthesis. Sun et al. [ 72 ] explore plasma-catalytic nitrogen fixation and its correlation with catalyst microanalysis. Through chemical kinetics modeling, they gain valuable insights into reaction mechanisms and performance, focusing on the role of a TiO2@5% GO catalyst. The second group of articles delves into the complexities of plasma-catalytic reactions, with a particular focus on plasma characteristics and their influence on reaction mechanisms. Catalysts 2024,14, 40 5 of 24 Rouwenhorst et al. [ 73 ] discuss the use of plasma-driven catalysis for green ammonia synthesis using intermittent electricity. The study explores the intricate nature of plasmacatalytic reactions, their effects on plasma characteristics, and flow patterns, contributing to the development of sustainable ammonia synthesis methods. Loenders et al. [ 74 ] present a microkinetic model for the partial oxidation of methane on Pt (111) surfaces under plasma catalysis. By investigating the influence of different plasma species on reaction kinetics and mechanisms, this study deepens our understanding of plasma-catalytic processes. Collectively, these articles underscore the importance of catalyst surfaces, microkinetic modeling, and the complexities of plasma-catalytic reactions in advancing the field of plasma catalysis. The application of DFT microkinetic modeling provides valuable insights into reaction mechanisms and catalyst performance, while investigations into plasma characteristics shed light on the intricacies of plasma-catalytic processes. Such research plays a critical role in optimizing plasma catalysis and expanding its applications in sustainable synthesis and chemical transformations. 2.3. Unraveling Complexities: Modeling Approaches in Advancing Plasma Catalysis Understanding and Efficiency The advancements in plasma catalysis have been fueled by the use of modeling approaches to unravel the intricate interactions between plasma and catalysts. Zhang et al. [ 75 ] employ a 2D implicit PIC/MCC model to study plasma streamer propagation within catalyst pores, emphasizing the significance of surface charging during this process. By comparing their results with fluid modeling predictions, the study sheds light on the complexities of plasma–catalyst interactions. Similarly, Michielsen et al. [ 76 ] use model calculations to enhance our understanding of plasma catalysis, specifically CO 2 dissociation in a packed-bed DBD reactor. Their investigation of the influence of operating parameters, particularly in helium plasma, offers us a better grasp of the underlying mechanisms. Pan et al. [ 77 ] conduct numerical simulations and calculations based on global and fluid models, as well as density functional theory (DFT), to investigate nanosecond-pulsed DBD plasma-catalytic CH 4 dry reforming. Their study highlights the synergistic effects between plasma and catalysts, providing valuable insights into the reaction dynamics and efficiency of this reforming process. On the other hand, Dou et al. [ 78 ] delve into the synergistic plasma-catalytic conversion of CO 2 and CH 4 into oxygenates. By investigating the role of metallic cobalt sites and oxygen vacancies in this process and demonstrating the production of CH 3 COOH through recombination in a plasma simulation, the study uncovers potential pathways for sustainable oxygenate production. Collectively, these articles demonstrate the significance of modeling approaches in understanding plasma–catalyst interactions and specific plasma-catalytic processes. The studies shed light on the intricate mechanisms and synergistic effects that occur during plasma catalysis, paving the way for its more efficient and sustainable applications. 2.4. Exploring Mechanisms, Modeling, and Applications in the Realm of Plasma Catalysis The field of plasma catalysis is a realm of exploration encompassing mechanisms, modeling approaches, and diverse applications. Researchers have made significant contributions to uncovering the mechanisms and developing modeling aspects of plasma catalysis. Mei et al. [ 79 ] delve into the reforming of CH 4 with CO 2 using a nanosecondpulsed DBD plasma, shedding light on the role of the CH 4 /CO 2 ratio and total flow in product distribution and the intricate interaction between reactive plasma species and catalysts. Chawdhury et al. [ 80 ] investigate a promising plasma-catalytic approach for single-step methane conversion to oxygenates at room temperature, utilizing plasma chemical kinetic modeling to highlight the catalytic facilitation of the conversion process. Specific applications and advancements in plasma catalysis are also subjects of focused research. Van’t Veer et al. [ 81 ] explore spatially and temporally non-uniform plasmas, particularly micro-discharges in a packed bed plasma reactor, to gain insights into plasmacatalytic ammonia synthesis. The study incorporates flow simulations and plasma kinetics Catalysts 2024,14, 40 6 of 24 modeling, deepening our understanding of micro-discharges’ behavior and their role in ammonia synthesis. Ong et al. [ 82 ] discuss the application of microwave plasma technology for converting CO 2 into high-value products, emphasizing fluid models and kinetic models to understand plasma behavior and highlighting the potential of microwave plasma catalysis in value-added chemical production. Collectively, these articles contribute to the advancement of our understanding of plasma catalysis. By providing valuable insights into the underlying mechanisms, modeling techniques, and potential applications of plasma-catalytic reactions, these studies pave the way for optimizing and progressing plasma catalysis for various purposes. The investigation of specific reactions, operating parameters, and spatial–temporal characteristics adds to our knowledge base of plasma catalysis, enabling its potential implementation in sustainable and innovative chemical processes. 2.5. Exploring the Landscape of Plasma Catalysis: Models, Applications, and Challenges The field of plasma catalysis is enriched by diverse research, encompassing models, applications, and challenges. In the realm of modeling and simulation, researchers have made significant contributions. Cheng et al. [ 83 ] present a model for nanosecond-pulsed plasma dry reforming of natural gas, incorporating flow and mixing effects. Their work sheds light on the complexities of plasma-catalytic reactions in this context. Cheng et al. [ 84 ] introduce a novel plasma synergistic catalysis model based on computational fluid dynamics (CFD), to examine CO production pathways and the influence of vibrationally excited species, providing valuable insights into reaction mechanisms under plasma conditions. Oskooei et al. [ 85 ] utilize a CFD model to simulate the plasma-assisted catalytic reduction of NOx, CO, and HC in diesel engine exhaust, contributing to our understanding of emission control technologies. Specific applications and challenges in plasma catalysis have also been a focal point of research. Li et al. [ 57 ] investigate methane-to-methanol conversion using both heterogeneous catalysis and plasma catalysis, emphasizing continuous flow reactors and advanced simulation techniques, offering potential routes for efficient methanol production. Zhang et al. [ 86 ] delve into the formation of micro-discharges inside a mesoporous catalyst in dielectric barrier discharge (DBD) plasmas using a 2D fluid model, uncovering intricate plasma–catalyst interactions in confined spaces. Fu et al. [ 87 ] critically review viral disinfection using non-thermal plasma and discuss the potential of the plasma catalysis system, showcasing the diverse applications of plasma catalysis in different domains. Another crucial aspect of research involves catalyst characterization and process optimization in plasma catalysis. Zhang et al. [ 88 ] explore plasma-enhanced catalytic activation of CO 2 in a modified gliding arc reactor, emphasizing the gas flow field and the need for further plasma modeling studies to optimize the process. Kaliyappan et al. [ 89 ] investigate the impact of material properties on the plasma-catalytic dissociation of CO 2 using a packed-bed DBD plasma reactor, contributing to the development of efficient catalysts for CO 2 conversion. Zhu et al. [ 90 ] study the hybrid plasma-catalytic removal of acetone over CuO/ γ -Al 2 O 3 catalysts using a response surface method, offering insights into optimizing the process conditions for efficient pollutant removal. Addressing challenges in catalytic modeling within plasma environments is also a key focus. Viladegut et al. [ 91 ] discuss the characterization of catalytic processes in plasma wind tunnels and highlight discrepancies between current catalytic models used in computational fluid dynamics (CFD) and actual catalytic processes. Their work sheds light on the complexities of catalytic processes in plasma environments and highlights the need for improved modeling techniques. Together, these articles contribute significantly to our understanding and advancement of plasma catalysis. They explore diverse modeling approaches, specific applications in different domains, and the challenges faced in catalytic modeling within plasma environments. This comprehensive research offers valuable insights that can pave the way for future advancements in the field of plasma catalysis. Catalysts 2024,14, 40 7 of 24 2.6. Unveiling the Multifaceted Landscape of Advancements in Plasma Catalysis for Sustainable Processes Research in the field of advancements in plasma catalysis for sustainable processes covers a wide range of topics, including modeling, insights into reactions, system advancements, and kinetic pathways. Researchers have emphasized the significance of modeling and designing plasma-catalytic systems, with a focus on microkinetic modeling and catalyst design for efficient CO 2 conversion [ 92 ]. Another area of interest is exploring energy-efficient pathways for pulsed-plasma-activated ammonia synthesis, taking into account reactor structure, plasma dynamics, and mass flow control [ 93 ]. Additionally, studies have presented microkinetic models to investigate the non-oxidative coupling of methane over specific catalysts in non-thermal plasma reactors, providing valuable insights into catalyst design for efficient plasma-catalytic processes [94]. Insights into specific plasma-catalytic reactions have been a focus of research, as well. Some articles combine experimental measurements and plasma kinetic modeling to enhance our understanding of ammonia decomposition in specific plasma environments [ 95 ]. Other studies explore the effects of specific promoters on plasma-assisted dry reforming of methane over specific catalysts [96]. Advancements in plasma-catalytic systems have been discussed in various articles. These include exploring the application of dielectric barrier discharge non-thermal plasma in the abatement of volatile organic compounds (VOCs), considering catalysts, plasma– catalyst synergy, and fluid modeling [ 97 ]. Characterizing the plasma catalytic decomposition of methane, focusing on the role of atomic oxygen, and proposing conceptual models for thermal catalysis and plasma-induced reactions have also been key areas of interest [ 98 ]. Moreover, some studies have reviewed the removal of specific pollutants from exhaust gases using non-thermal plasma combined with catalysts, highlighting different plasma–catalyst systems and the use of numerical modeling [ 99 ]. Modeling and kinetic pathways in plasma-catalytic reactions have been thoroughly explored, as well. Some articles discuss the modeling of excited species and their role in the kinetic pathways of specific reactions using zero-dimensional flow reactor models [ 100 ]. The optimization of plasma catalysts is a critical endeavor in enhancing the efficiency of chemical processes, involving a nuanced adjustment of key parameters to maximize desired outcomes. For instance, the modulation of plasma power and frequency is meticulously calibrated to influence the energy levels and reactive species generated during reactions, directly impacting catalytic activity. Similarly, careful control over gas composition and flow rate is essential, as these factors dictate reaction pathways and selectivity. The surface characteristics of the catalyst, including composition and morphology, significantly influence its catalytic performance. For example, tailoring the morphology of a catalyst to enhance its surface area can lead to increased active sites and improved catalytic activity. Additionally, temperature control, facilitated by effective cooling systems, is crucial in preventing undesired side reactions and maintaining an environment conducive to optimal catalytic performance [93,94,97]. Collectively, these articles contribute to the advancement of plasma catalysis for sustainable processes by providing valuable insights into modeling approaches, reaction mechanisms, catalyst design, and the optimization of plasma-catalytic systems. The diverse range of research highlights the multifaceted nature of plasma catalysis and its potential for various applications in sustainable processes. 2.7. Exploring Modeling, Reactions, Systems, and Kinetics in Plasma Catalysis for Sustainable Processes Research in the field of advancements in plasma catalysis for sustainable processes covers a diverse range of topics, including modeling, insights into reactions, system advancements, and kinetic pathways. The first group of articles emphasizes the significance of modeling and design in plasma-catalytic systems. Bogaerts and Centi [ 92 ] underscore the importance of microkinetic modeling and catalyst design for efficient CO 2 conversion. Pourali et al. [ 94 ] contribute with their microkinetic model by studying the non- Catalysts 2024,14, 40 8 of 24 oxidative coupling of methane over a Cu catalyst in a non-thermal plasma reactor, providing valuable insights into optimal modeling approaches and catalyst design for efficient plasma-catalytic processes. Andersen et al. [ 64 ] combine experimental measurements with plasma kinetic modeling to elucidate ammonia decomposition in a dielectric barrier discharge plasma. Tu and Whitehead [ 101 ] investigate the synergistic effect between plasma and catalysts in atmospheric dielectric barrier discharge dry reforming of methane. Diao et al. [ 96 ] explore the effects of a β -Mo 2 C promoter on plasma-assisted dry reforming of methane over Mo 2 CNi/Al 2 O 3 catalysts, further contributing to our understanding of reaction mechanisms and the pivotal role of catalysts in plasma-catalytic reactions. Li et al. [ 97 ] explore the application of dielectric barrier discharge non-thermal plasma in VOC abatement, considering catalysts, plasma–catalyst synergy, and fluid modeling. Li et al. [ 98 ] provide insights by characterizing the plasma catalytic decomposition of methane, focusing on the role of atomic oxygen and proposing a conceptual model for thermal catalysis and plasma-induced reactions. Zhu et al. [ 102 ] review the removal of CH 4 and NOx from marine LNG engine exhaust using non-thermal plasma combined with catalyst, highlighting different plasma–catalyst systems and the use of numerical modeling, showcasing advancements in plasma-catalytic systems for diverse applications. Maitre et al. [ 100 ] discuss the modeling of excited species and their role in the kinetic pathways of non-oxidative coupling of methane by dielectric barrier discharge, presenting a zero-dimensional flow reactor model, providing deeper insights into reaction mechanisms, and outlining the influence of excited species on plasma-catalytic processes. 2.8. Advancing Plasma-Assisted Catalysis: Exploring Reactors, Kinetics, Hydrogen Production, and Computational Analyses Research in the field of plasma-assisted catalysis has witnessed remarkable advancements in various aspects, spanning reactor configurations, kinetics modeling, hydrogen production, comparative studies, unknowns, and computational analyses with model validation. Scholars have explored different plasma reactor configurations to achieve the selective generation of oxygenates from CO 2 and CH 4 , utilizing theoretical modeling to comprehend the underlying processes. Additionally, researchers have investigated the influence of process parameters and packing materials on chemical equilibrium and kinetics in plasma-based CO 2 conversion, shedding light on factors affecting reaction efficiency. Moreover, they have employed modeling techniques to unveil the reaction mechanisms of different plasma sources in CH4conversion while also analyzing the impact of flow rates. The focus then shifted to plasma-catalytic technologies for hydrogen production. Comprehensive models have been developed to understand hydrogen production from alcohol reforming using plasma and plasma-catalytic technologies, investigating the effects of various process parameters on efficiency. Non-thermal atmospheric plasma reactors for hydrogen production from low-density polyethylene have been studied, emphasizing thermal–fluid models to understand the fluid flow and thermal behavior within the system. Furthermore, comparative studies and exploration of unknown factors in plasma catalysis have been carried out. Scholars have conducted comparative studies of non-thermal plasma-assisted reforming technologies, simulating the catalytic effects of plasma, thus contributing to our understanding of the advantages and limitations of different plasmaassisted processes. Moreover, they have analyzed known knowns, known unknowns, and unknown unknowns in plasma catalysis, utilizing simplified kinetic models and generalized models for specific catalysts, underlining the importance of further research in these areas. In the realm of computational studies and model validation in plasma-assisted catalysis, researchers have investigated plasma-based dry reforming across different time scales, validating their models by comparing simulation results with experimental data, ensuring accuracy and reliability. In addition, studies on enzyme-catalyzed processes of drug extrac- Catalysts 2024,14, 40 9 of 24 tion have utilized plasma-limited flow models to calculate critical parameters, providing insights into drug metabolism and extraction kinetics. The field of plasma-assisted catalysis has seen substantial growth across multiple dimensions. The comprehensive research on reactor configurations, kinetics modeling, hydrogen production, comparative studies, unknowns, and computational analyses has significantly enriched our understanding of plasma-catalytic processes, paving the way for their optimization and application in various industries and sustainable technologies. 2.9. Advancing the Frontiers of Plasma-Catalytic Processes: Insights across Syngas Production, Reforming, Waste Valorization, and Beyond This section critically engages with a discernible gap in the existing literature pertaining to the deficiency in methane degradation via NTP, offering a meticulous examination of this underexplored aspect. Positioned within the broader context of plasma-catalytic processes, the research extends its focus across an array of domains, encompassing syngas production, methane reforming, tar evolution, waste valorization, hydrocarbon reforming, carbon dioxide dissociation, plasma-catalytic NOx production, neural network modeling, ammonia synthesis, and CO2/CH4conversion. Examining syngas production from methane oxidation using non-thermal plasma reactors, this review critically appraises the catalytic role of surfaces and uniform packings in augmenting the efficiency of plasma-based reforming processes. Furthermore, our review encompasses scrutiny of tar evolution in plasma gasification processes, placing emphasis on the catalytic potential of thermal plasma for waste valorization. Integral to the manuscript’s critical examination is the role of modeling and simulation as imperative tools for understanding plasma-assisted reactions, particularly in catalytic partial oxidation and NOx production. This review assesses various influencing factors, including plasma characteristics, flow patterns, and catalyst interactions. Expanding its scope, this review delves into plasma catalysis in hydrocarbon reforming, carbon dioxide dissociation, and the direct conversion of CO2and CH4into valuable products. The integration of neural network modeling emerges as a promising yet scrutinized avenue, providing predictive insights into the performance of plasma-catalytic processes. The manuscript also critically evaluates investigations into ammonia synthesis using gliding arc plasma and the strategic use of K-promoters in CO2/CH4conversion. By critically addressing the deficiency in methane degradation by NTP and subjecting various facets of plasma catalysis to rigorous scrutiny, this manuscript serves as a specialized and pioneering contribution. Its critical analysis not only contributes to our comprehensive understanding of plasma-catalytic processes but also challenges and refines prevailing perspectives, paving the way for more nuanced and impactful advancements in sustainable and efficient chemical transformations within this dynamic field. 3. Development and Application of Computational Fluid Dynamics Models for Optimization of Plasma Reactors on an Industrial Scale Computational fluid dynamics studies are important for advancing reactor design and optimization at the industrial scale. Plasma generation and utilization consist of physical and chemical phenomena extensively involving transport phenomena for fluids inside the plasma reactors. Turbulence, heat transfer, radiation energy, multiphase flow, fluid interactions, and homogenous and heterogeneous phase reactions are of significant importance in the domain of CFD studies. Based on the key experimental results, a numerical fluid model is usually developed for CFD studies. Often, software like FLUENT, Python, MATLAB, and Abaqus are used for CFD coding and simulation. In steady states, the Navier–Stokes equation is applied in incompressible fluid conditions and physical sub-models. Eddy dissipation for species transport is also studied in colloquium with kinetic models for gas-phase reaction studies. Sometimes, in the CFD models, we utilize built-in functions, while in erroneous design, and evaluate models for user-defined functions [65,103]. Catalysts 2024,14, 40 16 of 24 Catalysts 2024, 14, x FOR PEER REVIEW 17 of 26 uni-electrode dielectric coating, mainly thermionic emissions, increases the electron temperatures in comparison to uncoated electrodes [104]. Figure 3. Different modeling approaches for modeling of plasma reactors (based on [130]). Graph results show the simulated behavior of NTP DBD systems for unconsidered atmospheric gas using kinetic theory with multiple reactions involving mainly important charged species for the evolution of charged species in COMSOL and BOLTZMAN Solver. The temperature for ion-kinetic reactions mostly depends on the autoionization process. Spatial–temporal variation in the plasma volume for recombinative air kinetic reactions leads to more autoionization for saturation of ion-kinetic temperature. In one-dimensional conditions, radiation and heat transfers can be neglected, but a greater-dimensional model incorporates the respective species densities based on the aforementioned. Ultimate Model •Hierarchical Modeling for Studies Inter-atomistic Potential •Baseline for MD Simulations & Adsorbate-Catalyst Systems •Usually, variables are a function of time and length •Modeling Without Catalysis & Adiabatic Reactors •Plug & Laminar Flow •1-D & 2-D Models •Atomic Scale & Mesoscale Models •No Side Reactions & Surface Reactions Density Functional Theory •Studies activation energy barriers, intermediates, and transition states •Small-time systems usually nanoseconds to picoseconds •Usually more than 100 species involved •Atomic scale Kinetic Monte Carlo Equations •Extends timescale •for higher length and magnitude of collisions as compared to Classical MD •Usually derived rate constant •Reaction data is collected from a database. •Atomic Scale •KMC is usually cheaper than classical MD and DFT Classical MD simulations •Hold larger systems of species than DFT systems •Usually include computational fluid dynamics •Atomic Scale Artifical Neural Network •Usually for modeling and optimizing studies Studies product distribution Increases selectivity with relative calculations Macroscale Plasma •Usually for 0-D models •For surface reactions •Synergistic effects •Reduced formation of coke •Hotspots in the reaction and discharge areas Figure 3. Different modeling approaches for modeling of plasma reactors (based on [130]). A hybrid multi-model approach has been used in the literature for chemical kinetics, which studies the degradation rates using a process intensification strategy of rotation with conversion (in COMSOL), the geometry of the electrode, electric field strength, electrostatic conditions, electrode surface area, and velocity calculations in a plasma reactor. The T&T reactor utilizes Gibbs energy with variable parameters such as temperature and pressure. The Navier Stokes equation and mass conservation equation can be used for multiple species and the generation of a 3D model in COMSOL. The software uses a builtin comprehensive nominal residual method for iteration and convergence of the model and validation with experimental results. The degradation rate of gasifier product gas highlights a strong relationship between the decomposition rate with the electrode tip and the electric field intensity in rotational intensified plasma reactors [131,132]. 3.6. Hybrid Modeling of Plasma Transport and Chemical Kinetics: Combining PIC and Monte Carlo Approaches with Density Functional Theory and Mechanistic Modeling Combining Monte Carlo and particle in cell (PIC) for transport and chemical kinetics offers a hybrid method for modeling. The 1D model is easy to compute due to the low computational effort required and limited species, and since the reaction under study has a uniform composition with no consideration of transport phenomena. In fluid modeling, Catalysts 2024,14, 40 17 of 24 mass, energy, and momentum balance are combined in the Boltzmann transport equation, yet more computational time is required than in kinetic modeling. If pressure conditions are taken into consideration, i.e., low-pressure conditions, the Monte Carlo equation with fluid models is utilized to study the detailed electron behavior in the plasma region, bringing forth a hybrid fluid model, and boundary conditions, a sticking coefficient, and wall reaction probabilities must be considered for surface reactions in dielectric plasma reactors. A new concept of the qualitative approach, mechanistic modeling, considers activation energies and a reaction rate coefficient based on first principal modeling using ab initio methods or MD simulations. In addition to considering multi-phase physical and chemistry calculations, density functional theory is proposed. Due to the complex nature of plasma and multiple by-products, this incorporates different body energy calculations and configurations to find appropriate and probable reaction mechanisms [ 120 , 133 , 134 ]. Tar components (naphthalene and phenol) are subjected to degradation similar to the degradation of double carbon bonds and aromatic ring compounds. Double bonds are destroyed by ozone mainly at 1,2 positions as the ethyl and hydroxyl activate further destruction. Naphthalene breakdown at 2,3 positions as the middle deployment increases the destruction rate. Package QCISSTD(T)/63-11 G (d, p) determines the activation energy for toluene, phenol, and naphthalene as 49 kJ/mol, 48.84 kJ/mol, and 28.06 kJ/ mol, while the same package gives the required Arrhenius activation energy expression levels as k = 4.17 × 10 10 exp ( − 5476.4/T), 1.22 × 10 12 exp ( − 5634.7/T), and finally 2.24 ×1012 exp (−3742.8/T) (cm3·mole−1·s−1) in succession. The ozone degradation rate constants of biomass compounds, in sequence, are the naphthalene molecule > phenol molecule > toluene molecule [135]. 3.7. Use of Machine Learning Algorithms for Optimization and Prediction of Plasma Reactor Performance in Plasma Hybrid Modeling Just like traditional plasma hybrid modeling, machine learning algorithms are used in combination for optimization and prediction studies of reactor performance. Naphthalene is subjected to reforming in a gliding arc plasma reactor. The results of reforming are analyzed in the a three-combination model comprising SVM, DT, and AAN for a complex procedural understanding and hyper-parameter tuning mainly with genetic algorithms. The results show great symmetry between experimental and modeling work. Steamto-carbon S/C and discharge power are the most influential factors for conversion and energy efficiency, respectively. Variables are coupled for performance calculations. Machine learning modeling shows an optimized tar conversion of 67.2% for an energy efficiency of 7.8 g/kWh [135–137]. Predictions are sometimes not feasible for large carbon and are currently not available in the literature for plasma technologies’ advancement. Chang’s ANN model was developed a four-experimental-parameter study, underscoring the effect on toluene removal. The parameters are the discharge power, initial concentration, flow rate, and relative humidity. ML algorithms are widely available for prediction studies in the chemical plasma process, with limitations and drawbacks. The most appropriate ML algorithms are supervised learning algorithms for regression classification and prediction problems. ANN methods are self-adapted, autonomously configured, and continuously learning. The only drawback is the large data set requirement for the ANN method’s executions and a challenge is avoiding overfitting. Usually, in plasma, chemical process data sets are small and confined. This eventually leads to uncertainty in the anticipated results. SVR is designated for non-linear and higher-order regressions [ 138 ]. It has a rare chance of overfitting and so consumes greater training time. In the originated data set, usually containing noise, DT algorithm transformation represents a tree visualization for robust calculations and decisions. Data set confirmation and properties do not affect DT calculations, and no preprocessing is required. Only normalized distribution and standardization are conducted. The results are usually overfitted due to generalization. The only advantage is the processing of large data sets. Catalysts 2024,14, 40 18 of 24 Kinetic studies are not feasible for a multi-length-scale complex plasma process, so a powerful machine learning tool is required [ 138 ]. ML is required for the prediction and optimization of large chemical processes. In experimentation, there are usually long cycles, complicated procedures, and susceptibility to environmental interference, all of which cost us time and resources in seeking to obtain reliable results. In addition, experimental complexity scales exponentially with the number of variables, restricting the number of experiments and narrowing the range of process parameters. Plasma modeling is a helpful method for understanding the plasma process and building a chemical kinetic model for predicting key reaction data, i.e., rate constant, conversion, and cross-section. The genetic algorithm finds the optimal hyperparameter for each ML algorithm involved, to enhance adaptability and predictive accuracy. KPI-based understanding has been developed for energy efficiency and the conversion of naphthalene [110,139,140]. Determining the reaction key performance (P) for tar plasma involves a casual combination of three algorithms, where the equation is given as P=PANN X W1+W2XPSVR +W3X PDT (0≤W1,≤W2≤W3≤1)(8) The algorithm’s weighted parameters are W 1 ,W 2 , and W 3 for DT, SVM, and ANN, while Pis the predicted value’s results. The mean square error evaluation formula is used to evaluate the performance of the ML model in terms of optimization for the relative weights using a predefined exhaustion method [136,141] MSE = n ∑ i=1 (Pi−RI)2(9) At four total inlet concentration sets of 1.1 g/Nm 3 , 1.4 g/Nm 3 , 1.7 g/Nm 3 , and 2.0 g/Nm3 , model predicted and experimental values are calculated for the conversion (%). The conversion rate varies at 87%, 72%, and 70% at the four designated rates. At lower initial concentrations, models and prediction values are greatly related, while an increase in the concentration creates a significant deviation between the two values. In the case of discharge power (W) values from 32 W to 77 W, the conversion increases from 64% to 73% at 1.7 g/Nm 3 concentration. A great coherence exists between predicted and experimental values for S/C = 0 [114,136,141]. 4. Conclusions In conclusion, the synthesis of these observations underscores the substantial advancements in plasma reactor design and catalytic processes. The integration of modeling and simulation has played a pivotal role in unraveling the intricacies of these domains. The intricate interplay between plasma and catalysts has been meticulously examined, providing profound insights into critical reactions such as ammonia synthesis, methane reforming, and hydrocarbon conversion. Microkinetic modeling and catalyst optimization have emerged as instrumental components, highlighting their pivotal role in driving efficient CO 2 conversion and fostering sustainable chemical processes. The ongoing evolution introduces innovative modeling techniques, including neural networks, with predictive capabilities that significantly enhance the precision of plasma-catalytic processes. The convergence of plasma and catalysts is expanding in its application scope to encompass areas such as waste utilization and syngas production. In this context, plasma catalysis stands out as a promising approach, spearheading environmentally conscious solutions and transformative industrial applications. As this trajectory unfolds, modeling and simulation will persist as invaluable tools, guiding us toward the development of efficient, sustainable, and innovative plasma-catalytic processes, thereby contributing to a more environmentally friendly future grounded in scientific rigor and technological innovation. Author Contributions: Conceptualization, M.Y.A.; methodology, M.Y.A., A.S.A. and M.J.; software, M.Y.A., A.S.A. and J.M.; formal analysis, M.Y.A., A.M., A.S.A. and M.J.; investigation, M.Y.A., A.S.A. Catalysts 2024,14, 40 19 of 24 and J.M.; resources, M.Y.A. and A.S.A.; writing—original draft preparation, M.Y.A., A.S.A., J.M. and L.N.; writing—review and editing, M.Y.A., A.S.A., M.J., A.M., L.N. and H.P.-K.; visualization, M.Y.A., A.S.A. and A.M.; supervision, M.Y.A., J.M. and H.P.-K. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Data Availability Statement: No new data were created. Conflicts of Interest: The authors declare no conflicts of interest. References 1. Pawlak-Kruczek, H.; Nied´zwiecki, Ł.; Ostrycharczyk, M.; Czerep, M.; Plutecki, Z. Potential and Methods for Increasing the Flexibility and Efficiency of the Lignite Fired Power Unit, Using Integrated Lignite Drying. Energy 2019,181, 1142–1151. [CrossRef] 2. M ˛aczka, T.; Pawlak-Kruczek, H.; Niedzwiecki, L.; Ziaja, E.; Chor ˛a˙ zyczewski, A. Plasma Assisted Combustion as a Cost-Effective Way for Balancing of Intermittent Sources: Techno-Economic Assessment for 200 MWel Power Unit. Energies 2020,13, 5056. [CrossRef] 3. Forsström, J.; Koreneff, G.; Similä, L. Electricity Market Designs and Flexibility; VTT Technical Research Centre of Finland: Espoo, Finland, 2016. 4. Nordic Council of Ministers. Demand Side Flexibility in the Nordic Electricity Market; Nordic Energy Research: Oslo, Norway, 2017; ISBN 9789289352383. 5. OFGEM. Consolidated Version of the Capacity Market Rules; Ofgem: London, UK, 2016. 6. Benalcazar, P.; Nalepka, P. The Polish Capacity Market Proposal vs the British Model. Polityka Energ. Energy Policy J. 2017,20, 59–72. 7. Chojnacki, I. Na 2021 Rok w Rynku Mocy Zakontraktowane Ponad 22 Tys. MW—Energetyka. Available online: https://energetyka.wnp.pl/na-2021-rok-w-rynku-mocy-zakontraktowane-ponad-22-tys-mw,335173_1_0_0.html (accessed on 1 December 2018). 8. Messerle, V.E.; Karpenko, E.I.; Ustimenko, A.B.; Lavrichshev, O.A. Plasma Preparation of Coal to Combustion in Power Boilers. Fuel Process. Technol. 2013,107, 93–98. [CrossRef] 9. Messerle, V.E.; Askarova, A.S.; Bolegenova, S.A.; Yu Maximov, V.; Nugymanova, A.O. 3D-Modelling of Kazakhstan Low-Grade Coal Burning in Power Boilers of Thermal Power Plant with Application of Plasma Gasification and Stabilization Technologies. J. Phys. Conf. Ser. 2019,1261, 012022. [CrossRef] 10. Karpenko, E.I.; Messerle, V.E.; Ustimenko, A.B. Plasma-Aided Solid Fuel Combustion. Proc. Combust. Inst. 2007,31, 3353–3360. [CrossRef] 11. Wulf, C.; Zapp, P.; Schreiber, A. Review of Power-to-X Demonstration Projects in Europe. Front. Energy Res. 2020,8, 191. [CrossRef] 12. Palys, M.J.; Daoutidis, P. Power-to-X: A Review and Perspective. Comput. Chem. Eng. 2022,165, 107948. [CrossRef] 13. Wang, W.; Ma, Y.; Chen, G.; Quan, C.; Yanik, J.; Gao, N.; Tu, X. Enhanced Hydrogen Production Using a Tandem Biomass Pyrolysis and Plasma Reforming Process. Fuel Process. Technol. 2022,234, 107333. [CrossRef] 14. Messerle, V.E.; Ustimenko, A.B. Hydrogen Production by Thermal Plasma Pyrolysis of Hydrocarbon Gases. IEEE Trans. Plasma Sci. 2023, 1–5. [CrossRef] 15. Liu, J.; Zhu, X.; Jiang, S.; Zhang, H.; Hong, Y.; Chen, G.; Tu, X. Plasma-Catalytic Synthesis of Ammonia over Ru/BaTiO 3 -Based Bimetallic Catalysts: Synergistic Effect from Dual-Metal Active Sites. Fuel Process. Technol. 2023,250, 107851. [CrossRef] 16. Akay, G. Hydrogen, Ammonia and Symbiotic/Smart Fertilizer Production Using Renewable Feedstock and CO 2 Utilization through Catalytic Processes and Nonthermal Plasma with Novel Catalysts and In Situ Reactive Separation: A Roadmap for Sustainable and Innovation-Base. Catalysts 2023,13, 1287. [CrossRef] 17. Gorbanev, Y.; Engelmann, Y.; van’t Veer, K.; Vlasov, E.; Ndayirinde, C.; Yi, Y.; Bals, S.; Bogaerts, A. Al 2 O 3 -Supported Transition Metals for Plasma-Catalytic NH 3 Synthesis in a DBD Plasma: Metal Activity and Insights into Mechanisms. Catalysts 2021, 11, 1230. [CrossRef] 18. Rouwenhorst, K.H.R.; Lefferts, L. Feasibility Study of Plasma-Catalytic Ammonia Synthesis for Energy Storage Applications. Catalysts 2020,10, 999. [CrossRef] 19. Salden, A.; Budde, M.; Garcia-Soto, C.A.; Biondo, O.; Barauna, J.; Faedda, M.; Musig, B.; Fromentin, C.; Nguyen-Quang, M.; Philpott, H.; et al. Meta-Analysis of CO 2 Conversion, Energy Efficiency, and Other Performance Data of Plasma-Catalysis Reactors with the Open Access PIONEER Database. J. Energy Chem. 2023,86, 318–342. [CrossRef] 20. Schaaf, T.; Grünig, J.; Schuster, M.R.; Rothenfluh, T.; Orth, A. Methanation of CO 2 —Storage of Renewable Energy in a Gas Distribution System. Energy Sustain. Soc. 2014,4, 2. [CrossRef] 21. Wang, L.; Wang, Y.; Fan, L.; Xu, H.; Liu, B.; Zhang, J.; Zhu, Y.; Tu, X. Direct Conversion of CH 4 and CO 2 to Alcohols Using Plasma Catalysis over Cu/Al(OH)3Catalysts. Chem. Eng. J. 2023,466, 143347. [CrossRef] Catalysts 2024,14, 40 20 of 24 22. Yu, X.; Chen, G.; Widenmeyer, M.; Kinski, I.; Liu, X.; Kunz, U.; Schüpfer, D.; Molina-Luna, L.; Tu, X.; Homm, G.; et al. Catalytic Recycling of Medical Plastic Wastes over La 0.6 Ca 0.4 Co 1−x Fe x O 3−δ Pre-Catalysts for Co-Production of H 2 and High-Value Added Carbon Nanomaterials. Appl. Catal. B Environ. 2023,334, 122838. [CrossRef] 23. Mei, D.; Wang, Y.; Liu, S.; Alliati, M.; Yang, H.; Tu, X. Plasma Reforming of Biomass Gasification Tars Using Mixed Naphthalene and Toluene as Model Compounds. Energy Convers. Manag. 2019,195, 409–419. [CrossRef] 24. Messerle, V.E.; Ustimenko, A.B.; Bodykbaeva, M.K. Plasma-Chemical Waste Processing: Numerical Analysis and Experiment. Part 1: Medical-Biological Waste and Fuel Biomass. Thermophys. Aeromechanics 2023,30, 183–192. [CrossRef] 25. Messerle, V.E.; Mosse, A.L.; Ustimenko, A.B. Plasma-Chemical Waste Processing: Numerical Analysis and Experiment. Part 2. Fuels and Lubricants. Thermophys. Aeromech. 2023,30, 387–392. [CrossRef] 26. Xuan, K.; Zhu, X.; Cai, Y.; Tu, X. Plasma Oxidation of H 2 S over Non-Stoichiometric LaxMnO 3 Perovskite Catalysts in a Dielectric Barrier Discharge Reactor. Catalysts 2018,8, 317. [CrossRef] 27. Matsumoto, T.; Wang, D.; Namihira, T.; Akiyam, H. Non-Thermal Plasma Technic for Air Pollution Control. In Air Pollution A Comprehensive Perspective; InTech: London, UK, 2012; Volume 11, p. 13, ISBN 0000957720. 28. Clothiaux, E.J.; Koropchak, J.A.; Moore, R.R. Decomposition of an Organophosphorus Material in a Silent Electrical Discharge. Plasma Chem. Plasma Process. 1984,4, 15–20. [CrossRef] 29. Dobslaw, D.; Ortlinghaus, O.; Dobslaw, C. A Combined Process of Non-Thermal Plasma and a Low-Cost Mineral Adsorber for VOC Removal and Odor Abatement in Emissions of Organic Waste Treatment Plants. J. Environ. Chem. Eng. 2018,6, 2281–2289. [CrossRef] 30. Dobslaw, D.; Schulz, A.; Helbich, S.; Dobslaw, C.; Engesser, K.H. VOC Removal and Odor Abatement by a Low-Cost Plasma Enhanced Biotrickling Filter Process. J. Environ. Chem. Eng. 2017,5, 5501–5511. [CrossRef] 31. Andersen, K.B.; Feilberg, A.; Beukes, J.A. Use of Non-Thermal Plasma and UV-Light for Removal of Odour from Sludge Treatment. Water Sci. Technol. 2012,66, 1656–1662. [CrossRef] [PubMed] 32. Barjasteh, A.; Dehghani, Z.; Lamichhane, P.; Kaushik, N.; Choi, E.H.; Kaushik, N.K. Recent Progress in Applications of NonThermal Plasma for Water Purification, Bio-Sterilization, and Decontamination. Appl. Sci. 2021,11, 3372. [CrossRef] 33. Guesmi, A.; Cherif, M.M.; Baaloudj, O.; Kenfoud, H.; Badawi, A.K.; Elfalleh, W.; Hamadi, N.B.; Khezami, L.; Assadi, A.A. Disinfection of Corona and Myriad Viruses in Water by Non-Thermal Plasma: A Review. Environ. Sci. Pollut. Res. 2022,29, 55321–55335. [CrossRef] 34. Naicker, K.I.; Kaweesa, P.; Daramola, M.O.; Iwarere, S.A. Non-Thermal Plasma Review: Assessment and Improvement of Feasibility as a Retrofitted Technology in Tertiary Wastewater Purification. Appl. Sci. 2023,13, 6243. [CrossRef] 35. Todorova, Y.; Benova, E.; Marinova, P.; Yotinov, I.; Bogdanov, T.; Topalova, Y. Non-Thermal Atmospheric Plasma for Microbial Decontamination and Removal of Hazardous Chemicals: An Overview in the Circular Economy Context with Data for Test Applications of Microwave Plasma Torch. Processes 2022,10, 554. [CrossRef] 36. Kvam, E.; Davis, B.; Mondello, F.; Garner, A.L. Nonthermal Atmospheric Plasma Rapidly Disinfects Multidrug-Resistant Microbes by Inducing Cell Surface Damage. Antimicrob. Agents Chemother. 2012,56, 2028–2036. [CrossRef] 37. Dobslaw, C.; Glocker, B. Plasma Technology and Its Relevance in Waste Air and Waste Gas Treatment. Sustainability 2020,12, 8981. [CrossRef] 38. Helbich, S.; Dobslaw, D.; Schulz, A.; Engesser, K.H. Styrene and Bioaerosol Removal from Waste Air with a Combined Biotrickling Filter and DBD–Plasma System. Sustainability 2020,12, 9240. [CrossRef] 39. Byeon, J.H.; Park, J.H.; Jo, Y.S.; Yoon, K.Y.; Hwang, J. Removal of Gaseous Toluene and Submicron Aerosol Particles Using a Dielectric Barrier Discharge Reactor. J. Hazard. Mater. 2010,175, 417–422. [CrossRef] 40. Wnukowski, M.; Moro´n, W. Warm Plasma Application in Tar Conversion and Syngas Valorization: The Fate of Hydrogen Sulfide. Energies 2021,14, 7383. [CrossRef] 41. M ˛aczka, T.; ´ Sliwka, E.; Wnukowski, M. Plasma Gasification of Waste Plastics. J. Ecol. Eng. 2013,14, 33–39. [CrossRef] 42. Wnukowski, M.; Jamróz, P. Microwave Plasma Treatment of Simulated Biomass Syngas: Interactions between the Permanent Syngas Compounds and Their Influence on the Model Tar Compound Conversion. Fuel Process. Technol. 2018,173, 229–242. [CrossRef] 43. ˇ Cespiva, J.; Niedzwiecki, L.; Vereš, J.; Skˇrínský, J.; Wnukowski, M.; Borovec, K.; Ochodek, T. Evaluation of the Performance of the Cross/Updraft Type Gasification Technology with the Sliding Bed over a Circular Grate. Biomass Bioenergy 2022,167, 106639. [CrossRef] 44. ˇ Cespiva, J.; Wnukowski, M.; Niedzwiecki, L.; Skˇrínský, J.; Vereš, J.; Ochodek, T.; Pawlak-Kruczek, H.; Borovec, K. Characterization of Tars from a Novel, Pilot Scale, Biomass Gasifier Working under Low Equivalence Ratio Regime. Renew. Energy 2020,159, 775–785. [CrossRef] 45. Wnukowski, M.; Kordylewski, W.; Łuszkiewicz, D.; Le´sniewicz, A.; Ociepa, M.; Michalski, J. Sewage Sludge-Derived Producer Gas Valorization with the Use of Atmospheric Microwave Plasma. Waste Biomass Valorization 2020,11, 4289–4303. [CrossRef] 46. Kwon, S.; Im, S. kyun Feasibility of Non-Thermal Plasma Gasification for a Waste-to-Energy Power Plant. Energy Convers. Manag. 2022,251, 114978. [CrossRef] 47. Fox, R.O. CFD Models for Analysis and Design of Chemical Reactors. Adv. Chem. Eng. 2006,31, 231–305. [CrossRef] 48. Lao, L.; Aguirre, A.; Tran, A.; Wu, Z.; Durand, H.; Christofides, P.D. CFD Modeling and Control of a Steam Methane Reforming Reactor. Chem. Eng. Sci. 2016,148, 78–92. [CrossRef] Catalysts 2024,14, 40 21 of 24 49. Xuan, J.; Leung, M.K.H.; Leung, D.Y.C.; Ni, M. Integrating Chemical Kinetics with CFD Modeling for Autothermal Reforming of Biogas. Int. J. Hydrogen Energy 2009,34, 9076–9086. [CrossRef] 50. Zettervall, N.; Fureby, C.; Nilsson, E.J.K. Evaluation of Chemical Kinetic Mechanisms for Methane Combustion: A Review from a CFD Perspective. Fuels 2021,2, 210–240. [CrossRef] 51. Rohr, B.A.; Singh, A.R.; Gauthier, J.A.; Statt, M.J.; Nørskov, J.K. Micro-Kinetic Model of Electrochemical Carbon Dioxide Reduction over Platinum in Non-Aqueous Solvents. Phys. Chem. Chem. Phys. 2020,22, 9040–9045. [CrossRef] [PubMed] 52. Sterk, E.B.; Nieuwelink, A.E.; Monai, M.; Louwen, J.N.; Vogt, E.T.C.; Filot, I.A.W.; Weckhuysen, B.M. Structure Sensitivity of CO 2 Conversion over Nickel Metal Nanoparticles Explained by Micro-Kinetics Simulations. JACS Au 2022,2, 2714–2730. [CrossRef] [PubMed] 53. Bogaerts, A.; Zhang, Q.-Z.; Zhang, Y.-R.; Van Laer, K.; Wang, W. Burning Questions of Plasma Catalysis: Answers by Modeling. Catal. Today 2019,337, 3–14. [CrossRef] 54. Wang, B.; Mikhail, M.; Cavadias, S.; Tatoulian, M.; Da Costa, P.; Ognier, S. Improvement of the Activity of CO 2 Methanation in a Hybrid Plasma-Catalytic Process in Varying Catalyst Particle Size or under Pressure. J. CO2Util. 2021,46, 101471. [CrossRef] 55. Zhang, Y.-R.; Van Laer, K.; Neyts, E.C.; Bogaerts, A. Can Plasma Be Formed in Catalyst Pores? A Modeling Investigation. Appl. Catal. B Environ. 2016,185, 56–67. [CrossRef] 56. Jiang, J.; Bruggeman, P.J. Tuning Plasma Parameters to Control Reactive Species Fluxes to Substrates in the Context of Plasma Catalysis. J. Phys. D. Appl. Phys. 2021,54, 214005. [CrossRef] 57. Li, S.; Ahmed, R.; Yi, Y.; Bogaerts, A. Methane to Methanol through Heterogeneous Catalysis and Plasma Catalysis. Catalysts 2021, 11, 590. [CrossRef] 58. Bogaerts, A.; Tu, X.; Whitehead, J.C.; Centi, G.; Lefferts, L.; Guaitella, O.; Azzolina-Jury, F.; Kim, H.-H.; Murphy, A.B.; Schneider, W.F.; et al. The 2020 Plasma Catalysis Roadmap. J. Phys. D. Appl. Phys. 2020,53, 443001. [CrossRef] 59. Maitre, P.A.; Bieniek, M.S.; Kechagiopoulos, P.N. Plasma-Enhanced Catalysis for the Upgrading of Methane: A Review of Modelling and Simulation Methods. React. Chem. Eng. 2020,5, 814–837. [CrossRef] 60. Liu, L.; Shao, G.; Ma, C.; Nikiforov, A.; De Geyter, N.; Morent, R. Plasma-Catalysis for VOCs Decomposition: A Review on Microand Macroscopic Modeling. J. Hazard. Mater. 2023,451, 131100. [CrossRef] [PubMed] 61. Zhu, L.; Lv, Z.; Huang, X.; Ran, J.; Chen, J.; Qin, C. Understanding the Role of Support Structure in Methane Dry Reforming for Syngas Production. Fuel 2022,327, 125163. [CrossRef] 62. Ben Yaala, M.; Saeedi, A.; Scherrer, D.F.; Moser, L.; Steiner, R.; Zutter, M.; Oberkofler, M.; De Temmerman, G.; Marot, L.; Meyer, E. Plasma-Assisted Catalytic Formation of Ammonia in N 2 -H 2 Plasma on a Tungsten Surface. Phys. Chem. Chem. Phys. 2019,21, 16623–16633. [CrossRef] [PubMed] 63. Rouwenhorstm, K.H.R.; Lefferts, L. Plasma-Catalytic Ammonia Synthesis via Eley-Rideal Reactions: A Kinetic Analysis. ChemCatChem 2023,15, e202300078. [CrossRef] 64. Andersen, J.A.; Holm, M.C.; van ’t Veer, K.; Christensen, J.M.; Østberg, M.; Bogaerts, A.; Jensen, A.D. Plasma-Catalytic Ammonia Synthesis in a Dielectric Barrier Discharge Reactor: A Combined Experimental Study and Kinetic Modeling. Chem. Eng. J. 2023, 457, 141294. [CrossRef] 65. Arshad, M.Y.; Saeed, M.A.; Tahir, M.W.; Pawlak-Kruczek, H.; Ahmad, A.S.; Niedzwiecki, L. Advancing Sustainable Decomposition of Biomass Tar Model Compound: Machine Learning, Kinetic Modeling, and Experimental Investigation in a Non-Thermal Plasma Dielectric Barrier Discharge Reactor. Energies 2023,16, 5835. [CrossRef] 66. Mehta, P.; Barboun, P.M.; Engelmann, Y.; Go, D.B.; Bogaerts, A.; Schneider, W.F.; Hicks, J.C. Plasma-Catalytic Ammonia Synthesis beyond the Equilibrium Limit. ACS Catal. 2020,10, 6726–6734. [CrossRef] 67. Li, D.; Rohani, V.; Fabry, F.; Parakkulam Ramaswamy, A.; Sennour, M.; Fulcheri, L. Direct Conversion of CO 2 and CH 4 into Liquid Chemicals by Plasma-Catalysis. Appl. Catal. B Environ. 2020,261, 118228. [CrossRef] 68. Bogaerts, A.; Berthelot, A.; Heijkers, S.; Kolev, S.; Snoeckx, R.; Sun, S.; Trenchev, G.; Van Laer, K.; Wang, W. CO 2 Conversion by Plasma Technology: Insights from Modeling the Plasma Chemistry and Plasma Reactor Design. Plasma Sources Sci. Technol. 2017, 26, 63001. [CrossRef] 69. Wang, X.; Du, X.; Chen, K.; Zheng, Z.; Liu, Y.; Shen, X.; Hu, C. Predicting the Ammonia Synthesis Performance of Plasma Catalysis Using an Artificial Neural Network Model. ACS Sustain. Chem. Eng. 2023,11, 4543–4554. [CrossRef] 70. Pan, J.; Liu, Y.; Zhang, S.; Hu, X.; Liu, Y.; Shao, T. Deep Learning-Assisted Pulsed Discharge Plasma Catalysis Modeling. Energy Convers. Manag. 2023,277, 116620. [CrossRef] 71. Wang, Y.; Craven, M.; Yu, X.; Ding, J.; Bryant, P.; Huang, J.; Tu, X. Plasma-Enhanced Catalytic Synthesis of Ammonia over a Ni/Al 2 O 3 Catalyst at Near-Room Temperature: Insights into the Importance of the Catalyst Surface on the Reaction Mechanism. ACS Catal. 2019,9, 10780–10793. [CrossRef] [PubMed] 72. Sun, J.; Zhang, T.; Hong, J.; Zhou, R.; Masood, H.; Zhou, R.; Murphy, A.B.; Ostrikov, K.K.; Cullen, P.J.; Lovell, E.C.; et al. Insights into Plasma-Catalytic Nitrogen Fixation from Catalyst Microanalysis and Chemical Kinetics Modelling. Chem. Eng. J. 2023, 469, 143841. [CrossRef] 73. Rouwenhorst, K.H.R.; Engelmann, Y.; Van ’T Veer, K.; Postma, R.S.; Bogaerts, A.; Lefferts, L. Plasma-Driven Catalysis: Green Ammonia Synthesis with Intermittent Electricity. Green Chem. 2020,22, 6258–6287. [CrossRef] 74. Loenders, B.; Engelmann, Y.; Bogaerts, A. Plasma-Catalytic Partial Oxidation of Methane on Pt(111): A Microkinetic Study on the Role of Different Plasma Species. J. Phys. Chem. C 2021,125, 2966–2983. [CrossRef] Catalysts 2024,14, 40 22 of 24 75. Zhang, Q.-Z.; Bogaerts, A. Propagation of a Plasma Streamer in Catalyst Pores. Plasma Sources Sci. Technol. 2018,27, 35009. [CrossRef] 76. Michielsen, I.; Uytdenhouwen, Y.; Pype, J.; Michielsen, B.; Mertens, J.; Reniers, F.; Meynen, V.; Bogaerts, A. CO 2 Dissociation in a Packed Bed DBD Reactor: First Steps towards a Better Understanding of Plasma Catalysis. Chem. Eng. J. 2017,326, 477–488. [CrossRef] 77. Pan, J.; Chen, T.; Gao, Y.; Liu, Y.; Zhang, S.; Liu, Y.; Shao, T. Numerical Modeling and Mechanism Investigation of NanosecondPulsed DBD Plasma-Catalytic CH4Dry Reforming. J. Phys. D. Appl. Phys. 2022,55, 35202. [CrossRef] 78. Dou, L.; Liu, Y.; Gao, Y.; Li, J.; Hu, X.; Zhang, S.; Ostrikov, K.; Shao, T. Disentangling Metallic Cobalt Sites and Oxygen Vacancy Effects in Synergistic Plasma-Catalytic CO 2 /CH 4 Conversion into Oxygenates. Appl. Catal. B Environ. 2022,318, 121830. [CrossRef] 79. Mei, D.; Zhang, P.; Duan, G.; Liu, S.; Zhou, Y.; Fang, Z.; Tu, X. CH4 Reforming with CO 2 Using a Nanosecond Pulsed Dielectric Barrier Discharge Plasma. J. CO2Util. 2022,62, 102073. [CrossRef] 80. Chawdhury, P.; Wang, Y.; Ray, D.; Mathieu, S.; Wang, N.; Harding, J.; Bin, F.; Tu, X.; Subrahmanyam, C. A Promising PlasmaCatalytic Approach towards Single-Step Methane Conversion to Oxygenates at Room Temperature. Appl. Catal. B Environ. 2021, 284, 119735. [CrossRef] 81. van ‘t Veer, K.; van Alphen, S.; Remy, A.; Gorbanev, Y.; De Geyter, N.; Snyders, R.; Reniers, F.; Bogaerts, A. Spatially and Temporally Non-Uniform Plasmas: Microdischarges from the Perspective of Molecules in a Packed Bed Plasma Reactor. J. Phys. D. Appl. Phys. 2021,54, 174002. [CrossRef] 82. Ong, M.Y.; Nomanbhay, S.; Kusumo, F.; Show, P.L. Application of Microwave Plasma Technology to Convert Carbon Dioxide (CO2) into High Value Products: A Review. J. Clean. Prod. 2022,336, 130447. [CrossRef] 83. Cheng, H.; Fan, J.; Zhang, Y.; Liu, D.; Ostrikov, K. (Ken) Nanosecond Pulse Plasma Dry Reforming of Natural Gas. Catal. Today 2020,351, 103–112. [CrossRef] 84. Cheng, H.; Liu, D.; Ostrikov, K. (Ken) Synergistic CO 2 Plasma Catalysis: CO Production Pathways and Effects of Vibrationally Excited Species. J. CO2Util. 2021,54, 101763. [CrossRef] 85. Oskooei, A.B.; Koohsorkhi, J.; Mehrpooya, M. Simulation of Plasma-Assisted Catalytic Reduction of NOx, CO, and HC from Diesel Engine Exhaust with COMSOL. Chem. Eng. Sci. 2019,197, 135–149. [CrossRef] 86. Zhang, Y.; Wang, H.; Zhang, Y.; Bogaerts, A. Formation of Microdischarges inside a Mesoporous Catalyst in Dielectric Barrier Discharge Plasmas. Plasma Sources Sci. Technol. 2017,26, 54002. [CrossRef] 87. Fu, J.; Xu, Y.; Arts, E.J.; Bai, Z.; Chen, Z.; Zheng, Y. Viral Disinfection Using Nonthermal Plasma: A Critical Review and Perspectives on the Plasma-Catalysis System. Chemosphere 2022,309, 136655. [CrossRef] [PubMed] 88. Zhang, S.; Li, Y.; Knoll, A.; Oehrlein, G.S. Mechanistic Aspects of Plasma-Enhanced Catalytic Methane Decomposition by Time-Resolved Operando Diffuse Reflectance Infrared Fourier Transform Spectroscopy. J. Phys. D. Appl. Phys. 2020,53, 215201. [CrossRef] 89. Kaliyappan, P.; Paulus, A.; D’Haen, J.; Samyn, P.; Uytdenhouwen, Y.; Hafezkhiabani, N.; Bogaerts, A.; Meynen, V.; Elen, K.; Hardy, A.; et al. Probing the Impact of Material Properties of Core-Shell SiO 2 @TiO 2 Spheres on the Plasma-Catalytic CO 2 Dissociation Using a Packed Bed DBD Plasma Reactor. J. CO2Util. 2021,46, 101468. [CrossRef] 90. Zhu, X.; Tu, X.; Mei, D.; Zheng, C.; Zhou, J.; Gao, X.; Luo, Z.; Ni, M.; Cen, K. Investigation of Hybrid Plasma-Catalytic Removal of Acetone over CuO/γ-Al2O3Catalysts Using Response Surface Method. Chemosphere 2016,155, 9–17. [CrossRef] [PubMed] 91. Viladegut, A.; Chazot, O. Catalytic Characterization in Plasma Wind Tunnels under the Influence of Gaseous Recombination. Phys. Fluids 2022,34, 27108. [CrossRef] 92. Bogaerts, A.; Centi, G. Plasma Technology for CO 2 Conversion: A Personal Perspective on Prospects and Gaps. Front. Energy Res. 2020,8, 1–23. [CrossRef] 93. Zeng, X.; Zhang, S.; Liu, Y.; Hu, X.; Ostrikov, K.K.; Shao, T. Energy-Efficient Pathways for Pulsed-Plasma-Activated Sustainable Ammonia Synthesis. ACS Sustain. Chem. Eng. 2023,11, 1110–1120. [CrossRef] 94. Pourali, N.; Vasilev, M.; Abiev, R.; Rebrov, E. V Development of a Microkinetic Model for Non-Oxidative Coupling of Methane over a Cu Catalyst in a Non-Thermal Plasma Reactor. J. Phys. D. Appl. Phys. 2022,55, 395204. [CrossRef] 95. Andersen, J.A.; van ’t Veer, K.; Christensen, J.M.; Østberg, M.; Bogaerts, A.; Jensen, A.D. Ammonia Decomposition in a Dielectric Barrier Discharge Plasma: Insights from Experiments and Kinetic Modeling. Chem. Eng. Sci. 2023,271, 118550. [CrossRef] 96. Diao, Y.; Zhang, X.; Liu, Y.; Chen, B.; Wu, G.; Shi, C. Plasma-Assisted Dry Reforming of Methane over Mo 2 C-Ni/Al 2 O 3 Catalysts: Effects of β-Mo2C Promoter. Appl. Catal. B Environ. 2022,301, 120779. [CrossRef] 97. Li, S.; Dang, X.; Yu, X.; Abbas, G.; Zhang, Q.; Cao, L. The Application of Dielectric Barrier Discharge Non-Thermal Plasma in VOCs Abatement: A Review. Chem. Eng. J. 2020,388, 124275. [CrossRef] 98. Li, Y.; Jiang, J.; Hinshelwood, M.; Zhang, S.; Bruggeman, P.J.; Oehrlein, G.S. Characterization of Plasma Catalytic Decomposition of Methane: Role of Atomic O and Reaction Mechanism. J. Phys. D. Appl. Phys. 2022,55, 155204. [CrossRef] 99. Zhu, X.; Wang, T.; Serageldin, M.A.; Pan, W.-P. Removal of Volatile Organic Compounds from Coal-Fired Power Plants Using Nonthermal Plasma Generated by a Dielectric Barrier Discharge Reactor: Carbon Yield and Application Feasibility. Ind. Eng. Chem. Res. 2023,62, 15434–15448. [CrossRef] 100. Maitre, P.-A.; Bieniek, M.S.; Kechagiopoulos, P.N. Modelling Excited Species and Their Role on Kinetic Pathways in the NonOxidative Coupling of Methane by Dielectric Barrier Discharge. Chem. Eng. Sci. 2021,234, 116399. [CrossRef] Catalysts 2024,14, 40 23 of 24 101. Tu, X.; Whitehead, J.C. Plasma-Catalytic Dry Reforming of Methane in an Atmospheric Dielectric Barrier Discharge: Understanding the Synergistic Effect at Low Temperature. Appl. Catal. B Environ. 2012,125, 439–448. [CrossRef] 102. Zhu, N.; Hong, Y.; Cai, Y.; Dong, F.; Song, J. The Removal of CH 4 and NOx from Marine LNG Engine Exhaust by NTP Combined with Catalyst: A Review. Materials 2023,16, 4969. [CrossRef] 103. Mashayak, S. CFD Modeling of Plasma Thermal Reactor for Waste Treatment. Ph.D. Thesis, Purdue University, West Lafayette, IN, USA, 2022. 104. Arshak, K.; Guiney, I.; Forde, E. Investigation of Recombination Effects in Dielectric Barrier Discharges: A Model. In Proceedings of the 2007 IEEE 34th International Conference on Plasma Science (ICOPS), Warsaw, Poland, 2–6 July 2007; p. 290. 105. Ferdous, N.; Mahbub, T.; Quddus, N. Study of AC Electroosmotic Flow Developed by Coplanar Microelectrode Array in a Slit Microchannel. In Proceedings of the International Conference on Mechanical Engineering 2011 (ICME2011), Dhaka, Bangladesh, 18–20 December 2011. 106. Fazeli, S.M.; Ravari, F.; Bozorgzadeh, H.; Sadeghzadeh Ahari, J. Kinetic Model Study of Dry Reforming of Methane Using Cold Plasma. Phys. Chem. Res. 2017,5, 395–408. [CrossRef] 107. van Dijk, J.; Kroesen, G.M.W.; Bogaerts, A. Plasma Modelling and Numerical Simulation. J. Phys. D. Appl. Phys. 2009,42, 190301. [CrossRef] 108. Materazzi, M.; Lettieri, P.; Mazzei, L.; Taylor, R.; Chapman, C. Reforming of Tars and Organic Sulphur Compounds in a Plasma-Assisted Process for Waste Gasification. Fuel Process. Technol. 2015,137, 259–268. [CrossRef] 109. Mariana, F.M.; Raúl, V.A.; Régulo, L.C.; Samuel, B.D.; Rosendo, P.E.; Arturo, M.C.; Bethsabet, J.S.; Antonio, M.C. NOx Chemical Reduction in a Cylindrical DBD Reactor: Theoretical and Experimental Analysis. Conf. Fr. Electrost. 2008,3, 98. 110. Xu, S.Y.; Cai, J.S.; Li, J. Modeling and Simulation of Plasma Gas Flow Driven by a Single Nanosecond-Pulsed Dielectric Barrier Discharge. Phys. Plasmas 2016,23, 103510. [CrossRef] 111. Stryczewska, H.D. Supply Systems of Non-Thermal Plasma Reactors. Construction Review with Examples of Applications. Appl. Sci. 2020,10, 3242. [CrossRef] 112. Buentello-Montoya, D.A.; Zhang, X.; Li, J. The Use of Gasification Solid Products as Catalysts for Tar Reforming. Renew. Sustain. Energy Rev. 2019,107, 399–412. [CrossRef] 113. Buentello-Montoya, D.; Zhang, X.; Li, J.; Ranade, V.; Marques, S.; Geron, M. Performance of Biochar as a Catalyst for Tar Steam Reforming: Effect of the Porous Structure. Appl. Energy 2020,259, 114176. [CrossRef] 114. Li, Q.; Wang, Q.; Tsuboi, Y.; Zhang, J.; Liu, J. Steam Reforming of Tar Studied in Bench-Scale Experiments and Pilot-Scale Tests with Simulations. Fuel 2021,290, 120028. [CrossRef] 115. Liu, S.Y.; Mei, D.H.; Nahil, M.A.; Gadkari, S.; Gu, S.; Williams, P.T.; Tu, X. Hybrid Plasma-Catalytic Steam Reforming of Toluene as a Biomass Tar Model Compound over Ni/Al2O3Catalysts. Fuel Process. Technol. 2017,166, 269–275. [CrossRef] 116. Saleem, F.; Umer, J.; Rehman, A.; Zhang, K.; Harvey, A. Effect of Methane as an Additive in the Product Gas toward the Formation of Lower Hydrocarbons during the Decomposition of a Tar Analogue. Energy Fuels 2020,34, 1744–1749. [CrossRef] 117. Lorcet, H.; Guenadou, D.; Latge, C.; Brothier, M.; Mariaux, G.; Vardelle, A. Kinetics Modeling of Biomass Gasification under Thermal Plasma Conditions. Application to a Refractory Species: The Methane. In Proceedings of the International Symposium on Plasma Chemistry, Bochum, Germany, 26–31 July 2009. 118. Saleem, F.; Zhang, K.; Harvey, A. Temperature Dependence of Non-Thermal Plasma Assisted Hydrocracking of Toluene to Lower Hydrocarbons in a Dielectric Barrier Discharge Reactor. Chem. Eng. J. 2019,356, 1062–1069. [CrossRef] 119. Bityurin, V.; Filimonova, E.; Kerst, R.A.B.P.; Naidis, G.; Pemen, A.J.M. The Modeling of Tar Removal from Biogas Stimulated by a Pulse Corona Discharge. Vacuum 2006,2, N2. 120. Bogaerts, A.; Eckert, M.; Mao, M.; Neyts, E. Computer Modelling of the Plasma Chemistry and Plasma-Based Growth Mechanisms for Nanostructured Materials. J. Phys. D. Appl. Phys. 2011,44, 174030. [CrossRef] 121. Delikonstantis, E.; Scapinello, M.; Stefanidis, G.D. Process Modeling and Evaluation of Plasma-Assisted Ethylene Production from Methane. Processes 2019,7, 68. [CrossRef] 122. Filimonova, E.A.; Amirov, R.H.; Kim, H.T.; Park, I.H. Comparative Modelling of NO x and SO 2 Removal from Pollutant Gases Using Pulsed-Corona and Silent Discharges. J. Phys. D. Appl. Phys. 2000,33, 1716–1727. [CrossRef] 123. Font Palma, C. Modelling of Tar Formation and Evolution for Biomass Gasification: A Review. Appl. Energy 2013,111, 129–141. [CrossRef] 124. Gagliano, A.; Nocera, F.; Bruno, M.; Cardillo, G. Development of an Equilibrium-Based Model of Gasification of Biomass by Aspen Plus. Energy Procedia 2017,111, 1010–1019. [CrossRef] 125. Jamróz, P.; Kordylewski, W.; Wnukowski, M. Microwave Plasma Application in Decomposition and Steam Reforming of Model Tar Compounds. Fuel Process. Technol. 2018,169, 1–14. [CrossRef] 126. Kaiser, S.; Fahlenkamp, H. CFD Modelling of the Electrical Phenomena and the Particle Precipitation Process of Wet ESP in Coaxial Wire-Tube Configuration. Int. J. Plasma Environ. Sci. Technol. 2011,5, 103–109. 127. Robicheaux, F.; Hanson, J.D. Simulated Expansion of an Ultra-Cold, Neutral Plasma. Phys. Plasmas 2003,10, 2217–2229. [CrossRef] 128. Wang, Y.; Yang, H.; Tu, X. Plasma Reforming of Naphthalene as a Tar Model Compound of Biomass Gasification. Energy Convers. Manag. 2019,187, 593–604. [CrossRef] 129. Assadi, A.A.; Bouzaza, A.; Merabet, S.; Wolbert, D. Modeling and Simulation of VOCs Removal by Nonthermal Plasma Discharge with Photocatalysis in a Continuous Reactor: Synergetic Effect and Mass Transfer. Chem. Eng. J. 2014,258, 119–127. [CrossRef] Catalysts 2024,14, 40 24 of 24 130. Neyts, E.C.; Bogaerts, A. Understanding Plasma Catalysis through Modelling and Simulation—A Review. J. Phys. D Appl. Phys. 2014,47, 224010. [CrossRef] 131. Cassini, F.; Consoni, D.; Rangel, L.; Padoin, N.; Soares, C.; Debacher, N. Multi-Step Modeling of Rotating Gliding-Arc Plasma Reactor Applied for Carbon Black Synthesis. Int. J. Plasma Environ. Sci. Technol. 2020,14, e03001. [CrossRef] 132. Cimerman, R.; Hensel, K. Generation of Honeycomb Discharge Assisted by Micro-Hollow Surface Dielectric Barrier Discharge. Int. J. Plasma Environ. Sci. Technol. 2021,15, e01003. [CrossRef] 133. Nair, S. Corona Plasma for Tar Removal. Ph.D. Thesis, Technical University of Eindhoven, Eindhoven, The Netherlands, 2004. 134. Pemen, A.; Nair, S.; Yan, K.; Heesch, E.J.M.; Ptasinski, K.; Drinkenburg, A. Pulsed Corona Discharges for Tar Removal from Biomass Derived Fuel Gas. Plasmas Polym. 2003,8, 209–224. [CrossRef] 135. Wen, Z.; Liu, Y.; Shen, H.; Ding, N.; Li, Y.; Luo, D. Mechanism and Kinetic Study on the Degradation of Typical Biomass Tar Components (Toluene, Phenol and Naphthalene) by Ozone. Ozone Sci. Eng. 2021,43, 78–87. [CrossRef] 136. Wang, Y.; Liao, Z.; Mathieu, S.; Bin, F.; Tu, X. Prediction and Evaluation of Plasma Arc Reforming of Naphthalene Using a Hybrid Machine Learning Model. J. Hazard. Mater. 2021,404, 123965. [CrossRef] [PubMed] 137. Wang, C.; Zhu, L.; Zhao, F.; Xu, D. The Chemistry of Gaseous Benzene Degradation Using Non-Thermal Plasma. Environ. Sci. Pollut. Res. 2021,28, 1565–1573. [CrossRef] 138. Geng, J.; Gan, W.; Xu, J.; Yang, R.; Wang, S. Support Vector Machine Regression (SVR)-Based Nonlinear Modeling of Radiometric Transforming Relation for the Coarse-Resolution Data-Referenced Relative Radiometric Normalization (RRN). Geo-Spat. Inf. Sci. 2020,23, 237–247. [CrossRef] 139. Yu, H.; Hu, W.; He, J.; Ye, Z. Decomposition Efficiency and Aerosol By-Products of Toluene, Ethyl Acetate and Acetone Using Dielectric Barrier Discharge Technique. Chemosphere 2019,237, 124439. [CrossRef] 140. Zhu, F.; Zhang, H.; Yang, J.; Yan, J.; Ni, M.; Li, X. Plasma Assisted Toluene Destruction in Simulated Producer Gas. Chem. Lett. 2017,46, 1341–1343. [CrossRef] 141. Affonso Nóbrega, P.H.; Rohani, V.; Fulcheri, L. Non-Thermal Plasma Treatment of Volatile Organic Compounds: A Predictive Model Based on Experimental Data Analysis. Chem. Eng. J. 2019,364, 37–44. [CrossRef] Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.