Role of experimental, modeling, and simulation studies of plasma in sustainable green energy

Citation: Arshad, M.Y.; Saeed, M.A.; Tahir, M.W.; Raza, A.; Ahmad, A.S.; Tahir, F.; Borkowski, B.; M ˛aczka, T.; Niedzwiecki, L. Role of Experimental, Modeling, and Simulation Studies of Plasma in Sustainable Green Energy. Sustainability 2023,15, 14193. https://doi.org/10.3390/ su151914193 Received: 17 July 2023 Revised: 14 September 2023 Accepted: 21 September 2023 Published: 26 September 2023 Copyright: © 2023 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/). sustainability Review Role of Experimental, Modeling, and Simulation Studies of Plasma in Sustainable Green Energy Muhammad Yousaf Arshad 1,2,* , Muhammad Azam Saeed 2, Muhammad Wasim Tahir 2, Ahsan Raza 3, Anam Suhail Ahmad 4, Fasiha Tahir 2, Bartłomiej Borkowski 5, Tadeusz M ˛aczka 5and Lukasz Niedzwiecki 6,7,* 1Corporate Sustainability and Digital Chemical Management, Interloop Limited, Faisalabad 37630, Punjab, Pakistan 2 Chemical Engineering Department, Lahore Campus, University of Engineering and Technology, Lahore 54890, Punjab, Pakistan; [email protected] (M.A.S.); [email protected] (M.W.T.); [email protected] (F.T.) 3Aziz Fatimah Medical and Dental College, Faisalabad 38000, Punjab, Pakistan; [email protected] 4Halliburton Worldwide, Sonora, TX 76950, USA; [email protected] 5Institute of Power Systems Automation, ul. Wystawowa 1, 51-618 Wrocław, Poland; [email protected] (B.B.); [email protected]oc.pl (T.M.) 6Department of Energy Conversion Engineering, Wrocław University of Science and Technology, Wyb. Wyspia´nskiego 27, 50-370 Wrocław, Poland 7Energy 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: This comprehensive review paper offers a multifaceted examination of non-thermal plasma applications in addressing the complex challenge of tar removal within biomass-oriented technologies. It begins with a concise introduction to the research background, setting the context for our exploration. The research framework is then unveiled, providing a structured foundation for understanding the intricate dynamics of plasma–tar interactions. As we delve deeper into the subject, we elucidate the reactivity of tar compounds and the transformation of alkali metals through plasma-based methodologies, essential factors in enhancing product gas quality. Through an array of empirical studies, we investigated the nuanced interactions between plasma and diverse materials, yielding crucial insights into plasma kinetics, modeling techniques, and the optimization of plasma reactors and processes. Our critical review also underscores the indispensable role of kinetic modeling and simulation in advancing sustainable green energy technologies. By harnessing these analytical tools, researchers can elevate system efficiency, reduce emissions, and diversify the spectrum of available renewable energy sources. Furthermore, we delve into the intricate realm of modeling plasma behavior and its intricate interplay with various constituents, illuminating a path toward innovative plasma-driven solutions. This comprehensive review highlights the significance of holistic research efforts that encompass empirical investigations and intricate theoretical modeling, collectively advancing the frontiers of plasma-based technologies within the dynamic landscape of sustainable energy. The insights gained from this review contribute to the overall understanding of plasma technologies and their role in achieving a greener energy landscape. Keywords: tars; kinetic modeling and simulation; non-thermal plasma; green energy; reactor performance 1. Introduction Biomass is considered a viable and proven energy source, and it appears promising in terms of achieving carbon neutrality in the future [ 1 – 4 ]. In principle, biomass uses CO 2 from the atmosphere as a substrate during its growth [ 5 , 6 ]. Valorised biomass can be used in combination with fossil fuels, providing an opportunity for gradual replacement of the latter [ 7 – 9 ]. Moreover, the use of biomass in combination with CCS could result in Sustainability 2023,15, 14193. https://doi.org/10.3390/su151914193 https://www.mdpi.com/journal/sustainability Sustainability 2023,15, 14193 2 of 35 achieving negative emissions of CO 2 [ 10 – 13 ]. Gasification is a process capable of turning solid biomass into a mixture of combustible gases [ 14 – 16 ]. The gas from gasification, also called producer gas or syngas, could be combusted in an engine or turbine in order to produce electricity and heat. Moreover, such gas can be used as a substrate for chemical syntheses (thus the name syngas), including producing liquid fuels in the Fischer–Tropsch process [ 17 ]. Solid residues from gasification can be considered as biochar [ 18 , 19 ], and apart from applying to the soil, such porous carbon material [ 20 ] can also be used as a sorbent [ 21 , 22 ]. Furthermore, gasification using CO 2 is an interesting way of using captured CO 2 for energy purposes [ 23 ]. However, one of the most significant problems of gasification is related to byproducts of gasification—tars [ 24 – 26 ]. Tars are typically defined as a complex mixture of hydrocarbons with a molecular weight greater than the molecular weight of benzene [ 9 ]. Tars are deposited on cooler surfaces in many cases, significantly increasing the risk of fouling downstream process equipment. Thus, extensive research has been dedicated so far towards minimizing the production of tars during gasification, including in situ depletion at formation stage and its post gasification removal [27,28]. Plasma, an intriguing state of matter, offers tremendous potential for converting renewable energy sources into usable forms. One such application is the stabilization of ignition and extending the flammability limits [ 29 , 30 ], owing to both radicals as well as the thermal effect [ 31 , 32 ]. The non-thermal plasma can stabilize the combustion and even maintain the flame for an equivalence ratio of 0.95 [ 33 ]. Moreover, the propagation of a turbulent flame can be enhanced by using nanosecond repetitively pulsed plasma discharges [ 34 ]. The thermal effect is extremely important at the scale of a power plant boiler, as reported by Messerle et al. [ 35 ]. The minimum attainable power of solid fuel-fired power plant boilers can be significantly reduced by the use of plasma technologies [ 36 ]. Over 27 boilers in Kazakhstan, Ukraine, Mongolia, China, and South Korea have been used in successful trials of using plasma torches to support combustion [ 37 ]. A suite of tests performed on BKZ-420 power boiler (Almaty Power Plant, Kazakhstan) proved a successful startup operation using only a system of plasma torches without using heavy oil as a supplementary fuel [ 38 ]. The trials were carried out using Ekibastuz coal with a high ash content (40% when dry) and a low calorific value (16.6 MJ/kg). The combustion of pulverized coal with a relatively low volatile matter content could be sustained, with the power of plasma torches being 2.5% of the burner’s power, according to Karpenko et al. [ 37 ]. The economic feasibility of such solutions has been proven by M ˛aczka et al. [ 34 ]. The application of non-thermal plasma has also been considered as a way to abate the emissions in the energy sector and is readily being used in formulation of bio-based feedstocks for energy utilization rather than material transformation to supplementary chemicals and bio-composites [39–41]. Another environmentally friendly application of plasma technologies can be found in the field of gasification [ 42 , 43 ]. Plasma gasification has been successfully used for the gasification of organic waste [ 44 ], spent lubricants [ 45 ], or refuse-derived fuel [ 46 ]. An important advantage of plasma gasification with respect to different types of waste is its ability to perform vitrification [ 47 ]. The use of plasma for modification of the surface of different materials [ 48 ], including hydrochars, has also been reported [ 49 – 53 ]. Some authors also reported the use of plasma for the degradation of organic pollutants [54]. Looking at the Scopus database, significant growth in papers published annually with keywords such as plasma and modeling can be noted in recent years (see Figure 1) due to increased interest in plasma processes. There are existing reviews on plasma processes as well as on their modeling, including works on process modeling for the synthesis of chemicals [ 55 – 57 ] and plasma gasification [ 58 ]. However, there is a lack of reviews on the modeling of non-thermal plasma for the decomposition of tars, which is one of the key applications of plasma processes in the sustainable green energy sector. Sustainability 2023,15, 14193 3 of 35 Sustainability 2023, 15, x FOR PEER REVIEW 3 of 38 Figure 1. Number of papers published each year between 1966 and 2022 with keywords plasma AND modeling, shown by Scopus. This review aims to close this gap by exploring the wide range of applications of plasma-based technologies in sustainable green energy, with a specific focus on the modeling of non-thermal plasma decomposition of tars. These technologies not only provide eco-friendly solutions for waste disposal but also generate clean energy, making them vital components of a sustainable energy landscape [41,51,59]. One significant challenge in biomass-oriented technologies is the efficient removal of tar, a complex byproduct of gasification. Understanding the reactivity of tar and the transformation of alkali metals using plasma technologies is crucial for enhancing the quality of product gas and advancing the commercialization of biomass processes [41,59]. Experimental studies investigating the interactions between plasma and various materials yield valuable insights into plasma kinetics, modeling, and the optimization of plasma reactors and processes [53,60]. Moreover, understanding novel environmentally friendly technologies is important from the point of view of regulatory bodies, as effective environmental regulation tools can strengthen the stakeholder relationship between the government and enterprises and thus create the right environment for the implementation of green technological innovation [61]. Stable policies are key since changes between economic cycles influence energy intensity, especially in emerging economies, which in turn influences the adoption of eco-innovative technologies by industrial stakeholders [62]. This review emphasizes the pivotal role of kinetic modeling and simulation techniques in contributing to sustainable green energy technologies. By employing these tools, researchers can improve system efficiency, reduce emissions, and expand the utilization of renewable energy sources. Modeling plasma behavior and its interactions with different components facilitates the development of innovative plasma-based solutions [52]. This paper provides a comprehensive overview of the key subtopics in plasma-based technologies for sustainable green energy. It covers areas such as tar characteristics in gasification processes, mechanisms and reactivity of tar cracking and reforming through thermal, catalytic, and plasma-assisted decomposition, plasma modeling and chemical kinetic modeling for understanding plasma processes, and the relationship between reactor geometry, energy yield, and mass transfer in plasma reactors. Figure 1. Number of papers published each year between 1966 and 2022 with keywords plasma AND modeling, shown by Scopus. This review aims to close this gap by exploring the wide range of applications of plasma-based technologies in sustainable green energy, with a specific focus on the modeling of non-thermal plasma decomposition of tars. These technologies not only provide eco-friendly solutions for waste disposal but also generate clean energy, making them vital components of a sustainable energy landscape [ 41 , 51 , 59 ]. One significant challenge in biomass-oriented technologies is the efficient removal of tar, a complex byproduct of gasification. Understanding the reactivity of tar and the transformation of alkali metals using plasma technologies is crucial for enhancing the quality of product gas and advancing the commercialization of biomass processes [ 41 , 59 ]. Experimental studies investigating the interactions between plasma and various materials yield valuable insights into plasma kinetics, modeling, and the optimization of plasma reactors and processes [ 53 , 60 ]. Moreover, understanding novel environmentally friendly technologies is important from the point of view of regulatory bodies, as effective environmental regulation tools can strengthen the stakeholder relationship between the government and enterprises and thus create the right environment for the implementation of green technological innovation [ 61 ]. Stable policies are key since changes between economic cycles influence energy intensity, especially in emerging economies, which in turn influences the adoption of eco-innovative technologies by industrial stakeholders [62]. This review emphasizes the pivotal role of kinetic modeling and simulation techniques in contributing to sustainable green energy technologies. By employing these tools, researchers can improve system efficiency, reduce emissions, and expand the utilization of renewable energy sources. Modeling plasma behavior and its interactions with different components facilitates the development of innovative plasma-based solutions [52]. This paper provides a comprehensive overview of the key subtopics in plasma-based technologies for sustainable green energy. It covers areas such as tar characteristics in gasification processes, mechanisms and reactivity of tar cracking and reforming through thermal, catalytic, and plasma-assisted decomposition, plasma modeling and chemical kinetic modeling for understanding plasma processes, and the relationship between reactor geometry, energy yield, and mass transfer in plasma reactors. Furthermore, it explores the simplification of removal kinetics modeling through direct electron collision and its relation to reactor configuration and scaling-up studies. The use Sustainability 2023,15, 14193 4 of 35 of lumped parameters and kinetic models for tar removal in synthetic gas mixtures using a pulsed corona discharge reactor is also discussed, along with the thermodynamics in kinetic models for tar removal considering moisture content and equivalent ratio. The development and validation of kinetic models for hydrocarbon conversion in non-thermal plasma reactors, the role of energy density and efficiency in plasma discharge kinetics modeling for decomposition reactions, and the application of kinetic modeling for tar decomposition in plasma reactors with varying energy density and carrier gases are examined. The efficiency of tar removal in pulse discharge reactors using NIST-based kinetic modeling and numerical simulation, as well as the effect of temperature and gas composition on tar removal efficiency in non-thermal plasma and pulsed corona discharge reactors using kinetic modeling and statistical analysis, are also addressed. This comprehensive review consolidates the current knowledge on plasma-based technologies for sustainable green energy. The insights gained from this review contribute to the overall understanding of plasma technologies and their role in achieving a greener energy landscape. By harnessing the unique properties of plasma, we can pave the way for a more sustainable and environmentally friendly future for biomass-based green energy. 2. Characteristics of Tar in Gasification Processes: Classification, Reactivity, Composition, and Environmental Impact Different classifications of tars exist, with the most common being the ECN classification. According to this classification all tars undetectable by GC (heaviest tars) belong to class 1, heterocyclic compounds (e.g., phenol, pyridine, and cresol) belong to class 2, aromatic compounds with one ring (e.g., xylene, styrene, and toluene) belong to class 3, light polyaromatic hydrocarbons with 2–3 rings (e.g., naphthalene, biphenyl, acenaphtylene, phenanthrene, and anthracene) belong to class 4, whereas heavy polyaromatic hydrocarbons with more than three rings (e.g., fluoranthene, pyrene, and chrysene) belong to class 5 [ 63 ]. Some of the recent works suggested classifying tars depending on the melting point of each compound, thus enabling the possibility of making a tar deposition diagram ( Figure 2 ), which is a useful tool for quantitative prediction of tar deposition issues downstream of a gasifier. Tar can be polar or non-polar, reactive or unreactive, and hydrophobic or hydrophilic based on temperature variation in the gasifier and feedstock. Feedstock degradation has a temperature range of about 300–400 ◦ C, and it forms water vapor. Primary, vapor formation has a higher tar yield with an increase in temperature during cracking increases. At temperatures higher than 800 ◦ C, poly-aromatic hydrocarbons yield higher tar [ 64 ]. A thermochemical conversion of combustible gases at temperatures 800–1000 ◦ C in an air/oxygen mixture usually leads to partial oxidation reactions, typical for gasification. A mixture of steam/oxygen can be considered auto-thermal depending on the equivalence ratio and ratio of oxygen to steam. Producer gas composition depends on the gasifying agent (O 2 , steam, etc.) and gasifier configuration, i.e., if it is a fluidized bed, fixed bed, or entrained flow gasifier [ 63 , 65 ]. Figure 3provides a representation of the diverse product composition in different types of gasifiers, highlighting the importance of understanding the variations in gasifier performance and product yields. Sustainability 2023,15, 14193 5 of 35 Sustainability 2023, 15, x FOR PEER REVIEW 5 of 38 Figure 2. An example of the tar deposition diagram for a fixed bed gasifier with a simple gas cooler with an inlet water temperature of 15 °C (Roman numerals indicate the class of the particular compound, based on the ECN classification)—reprinted from [66] with permission from Elsevier. Figure 3. Different types of gasifier product composition—based on [67]. Scientific research activities are being conducted extensively to enhance the understanding towards tar removal. Tar is used in any form of gasification process except in boiler operations. Tar is one of the reasons for particulate matter formation at downstream operations. The content and composition of produced tars depend on the design of a particular gasifier, e.g., a typical downdraft fixed gasifier produces a tar within the range of 500 g/cm 3 to 1000 g/cm 3 . Municipal waste is another source of energy production through gasification and also produces a good quantity of syngas. The process produces enough activity for an environmental impact and reduces waste deposits in landfills [64,68]. Moreover, many compounds contend among tars, are considered pollutants [69] and are subject to control, as shown in Figure 4, which illustrates the maximum permissible limits of emissive pollutants. From this perspective, any leaks from gasification installations could increase concentrations of the mentioned pollutants in the atmosphere. Tar reactivity research is the core step for the development and commercialization of biomass-oriented technologies as it initially removes tar and increases producer gas quality. For the commercialization of a gasification-based energy stream, a two-way methodology is proposed, initially by parametrically optimizing gasifier operations through tar Figure 2. An example of the tar deposition diagram for a fixed bed gasifier with a simple gas cooler with an inlet water temperature of 15 ◦ C (Roman numerals indicate the class of the particular compound, based on the ECN classification)—reprinted from [66] with permission from Elsevier. Sustainability 2023, 15, x FOR PEER REVIEW 5 of 38 Figure 2. An example of the tar deposition diagram for a fixed bed gasifier with a simple gas cooler with an inlet water temperature of 15 °C (Roman numerals indicate the class of the particular compound, based on the ECN classification)—reprinted from [66] with permission from Elsevier. Figure 3. Different types of gasifier product composition—based on [67]. Scientific research activities are being conducted extensively to enhance the understanding towards tar removal. Tar is used in any form of gasification process except in boiler operations. Tar is one of the reasons for particulate matter formation at downstream operations. The content and composition of produced tars depend on the design of a particular gasifier, e.g., a typical downdraft fixed gasifier produces a tar within the range of 500 g/cm 3 to 1000 g/cm 3 . Municipal waste is another source of energy production through gasification and also produces a good quantity of syngas. The process produces enough activity for an environmental impact and reduces waste deposits in landfills [64,68]. Moreover, many compounds contend among tars, are considered pollutants [69] and are subject to control, as shown in Figure 4, which illustrates the maximum permissible limits of emissive pollutants. From this perspective, any leaks from gasification installations could increase concentrations of the mentioned pollutants in the atmosphere. Tar reactivity research is the core step for the development and commercialization of biomass-oriented technologies as it initially removes tar and increases producer gas quality. For the commercialization of a gasification-based energy stream, a two-way methodology is proposed, initially by parametrically optimizing gasifier operations through tar Figure 3. Different types of gasifier product composition—based on [67]. Scientific research activities are being conducted extensively to enhance the understanding towards tar removal. Tar is used in any form of gasification process except in boiler operations. Tar is one of the reasons for particulate matter formation at downstream operations. The content and composition of produced tars depend on the design of a particular gasifier, e.g., a typical downdraft fixed gasifier produces a tar within the range of 500 g/cm 3 to 1000 g/cm 3 . Municipal waste is another source of energy production through gasification and also produces a good quantity of syngas. The process produces enough activity for an environmental impact and reduces waste deposits in landfills [ 64 , 68 ]. Moreover, many compounds contend among tars, are considered pollutants [ 69 ] and are subject to control, as shown in Figure 4, which illustrates the maximum permissible limits of emissive pollutants. From this perspective, any leaks from gasification installations could increase concentrations of the mentioned pollutants in the atmosphere. Tar reactivity research is the core step for the development and commercialization of biomass-oriented technologies as it initially removes tar and increases producer gas quality. For the commercialization of a gasification-based energy stream, a two-way methodology is proposed, initially by parametrically optimizing gasifier operations through tar removal using primary and secondary methods [ 63 , 70 ]. Primary methods are not absolutely efficient for gas cleaning downstream but are comparatively economical. Process parameters, i.e., Sustainability 2023,15, 14193 6 of 35 temperature, equivalence ratio, compositions, reactor residence time, pressure, etc., are vital for enhanced tar reduction and syngas quality. The primary tar removal method might decrease the chemical energy and overall system efficiency [ 65 , 71 – 73 ]. Figure 5illustrates the different commercial primary and secondary tar removal methods used in the industry. Sustainability 2023, 15, x FOR PEER REVIEW 6 of 38 through tar removal using primary and secondary methods [63,70]. Primary methods are not absolutely efficient for gas cleaning downstream but are comparatively economical. Process parameters, i.e., temperature, equivalence ratio, compositions, reactor residence time, pressure, etc., are vital for enhanced tar reduction and syngas quality. The primary tar removal method might decrease the chemical energy and overall system efficiency [65,71,72,73]. Figure 5 illustrates the different commercial primary and secondary tar removal methods used in the industry. Figure 4. Maximum permissible limits of emissive pollutants—based on [69]. Figure 4. Maximum permissible limits of emissive pollutants—based on [69]. Sustainability 2023, 15, x FOR PEER REVIEW 7 of 38 Figure 5. Commercial tar removal primary and secondary methods for tar removal—based on [63,70]. 3. Tar Decomposition Mechanisms and Kinetics in Thermal and Plasma Environments Plasma-based technologies hold immense potential in converting renewable energy sources into usable forms and play a critical role in sustainable green energy applications. The focus on plasma combustion and plasma gasification offers eco-friendly waste disposal solutions and clean energy generation, making them integral to a sustainable energy landscape. The removal of tar using plasma technologies is a key area of interest, requiring a deeper understanding of tar reactivity and the transformation of alkali metals. Experimental studies on plasma–material interactions provide valuable insights into the kinetics, modeling, and optimization of plasma reactors and processes. The decomposition of tar and its constituent compound family using conventional thermal, catalytic, and advanced plasma routes allows a further in-depth understanding of tar reactivity. At elevated temperatures, tar undergoes a series of reactions with definite residence time at ambient conditions and exposure to air. Mainly, the reactions are symmetric and categorized as tar polymerization and tar decomposition. Initial reactions are decomposition reactions, while intermediate stages are polymerization reactions which feature condensed tar reactions and inert environment tar decomposition between heterogeneous (liquid and gaseous phase) compounds and subsequent char or ash. Final reactions occur between gaseous species and tar compounds. The rate of radical formation Figure 5. Commercial tar removal primary and secondary methods for tar removal—based on [ 63 , 70 ]. Sustainability 2023,15, 14193 7 of 35 3. Tar Decomposition Mechanisms and Kinetics in Thermal and Plasma Environments Plasma-based technologies hold immense potential in converting renewable energy sources into usable forms and play a critical role in sustainable green energy applications. The focus on plasma combustion and plasma gasification offers eco-friendly waste disposal solutions and clean energy generation, making them integral to a sustainable energy landscape. The removal of tar using plasma technologies is a key area of interest, requiring a deeper understanding of tar reactivity and the transformation of alkali metals. Experimental studies on plasma–material interactions provide valuable insights into the kinetics, modeling, and optimization of plasma reactors and processes. The decomposition of tar and its constituent compound family using conventional thermal, catalytic, and advanced plasma routes allows a further in-depth understanding of tar reactivity. At elevated temperatures, tar undergoes a series of reactions with definite residence time at ambient conditions and exposure to air. Mainly, the reactions are symmetric and categorized as tar polymerization and tar decomposition. Initial reactions are decomposition reactions, while intermediate stages are polymerization reactions which feature condensed tar reactions and inert environment tar decomposition between heterogeneous (liquid and gaseous phase) compounds and subsequent char or ash. Final reactions occur between gaseous species and tar compounds. The rate of radical formation determines cracking reactions and is dependent on the size, number, and least bond strength of the model compound in thermal and non-thermal settings [63,74,75]. Tar cracking is divided into four categories, partial oxidation, steam reforming, dry reforming, and hydrocracking and thermal cracking, which are mainly dependent on temperature, an increase in process temperature, and a change or sudden decrease in the activation energy. Plasma cracking and reforming complement the above two in the most sophisticated manner using a catalyst [ 64 ]. In catalytic cracking, fragmentation and carbon deposition decreases operational cost [ 76 ]. Kai studied an efficient route of plasma catalytic reforming for tar conversion into syngas and superior gas cleaning activity simultaneously. Toluene, being a model compound, decomposes in Ni/SiO 2 catalyst for syngas production. According to Wnukowski and Moro´n, the conversion of toluene in a microwave plasma generates heavy aromatic by-products, such as phenylethyne, indene, naphthalene, and acenaphthylene [ 26 ]. Moreover, tar removal from a real producer gas has also been attempted using microwave atmospheric plasma [77]. A comparison between thermally induced direct decomposition and plasma-supported decomposition shows that plasma induction just before catalyst beds increases the conversion rate towards syngas formation. A comparison of the aforementioned methods with catalytic reforming and plasma-assisted decomposition shows that plasma catalytic reforming shows the conversion rate is the maximum and the direct decomposition rate is the minimum. The sequence is as follows: plasma enhanced reforming > plasma decomposition > catalytic reforming > direct decomposition [ 78 , 79 ]. The tar reforming mechanism is depicted in Figure 6, and reactions are shown in Equations (1)–(3) [64,65,80]. CnHm+n 2O2−−−−−−−−→ nCO +m 2H2(1) CnHm+nH2O−−−−−−→m 2+nH2+nCO (2) CnHm+nCO2−−−−−−→2nCO +m 2H2(3) Close relationships between gasification reactor parameters give rise to in situ strategies that are usually complex in nature. An increase in gasification temperature decreases the yield of gasifier tar with a change in gas composition. An increase in gasification rate could be achieved by an increase in the temperature. Equilibrium reactions and reduction in available tar are affected by gasifier operational parameters. This limits the first gasification stages to maximize the conversion of total carbon and minimize tar reduction in the Sustainability 2023,15, 14193 8 of 35 second stage. The required process should not only decrease tar contents but also provide higher syngas quality, with minimum by-products and an increase in the dew point and soot formation. A plasma gasifier takes its heat content from auto-thermal reaction and oxidation processes. The high temperature in plasma gasifiers destroys toxic and dangerous molecules. Thermal plasma units are also used as secondary treatment units. Usually, in hot gas thermal plasma, gas is heated instead of the feedstock. It reduces tar and carbon soot but is extensively heat-dependent [64,81,82]. Sustainability 2023, 15, x FOR PEER REVIEW 9 of 38 Figure 6. Tar Reforming Reaction Mechanism main steps. Tar model compounds naphthalene, toluene, and benzene are thermally converted in the presence of hydrogen and steam to understand the mechanism and kinetics of the reaction as a function of temperature in a definite time function of 0.5 s. The order to reactivity is toluene > naphthalene > benzene as the activation energy of benzene is highest while toluene has lowest. The activation energy of toluene, naphthalene, and benzene are 247.1 kJ/mol, 350.2 kJ/mol, and 443.6 kJ/mol, respectively [72]. In thermal decomposition, mainly benzene is produced. Benzene has the highest activation energy, along with soot and aromatic hydrocarbons, as a principal compound from higher hydrocarbon molecules such as naphthalene. The reduction method should not incorporate an effect on gaseous products such as CO, H 2 , CH 4 , and CO 2 [63,80,83,84]. In the research of Nair [51], the author constituted the utilization of pulse discharge for the removal and cleaning of product gas for industrial applications. Experiment conditions were 200 °C for heavy hydrocarbon in an oxidation reaction process [51]. In pulse corona discharges, an energy transfer from the power source to the gaseous mixture is initiated for radical production, which defines efficiency. Streamer corona discharges are relative to reaction rates and termination reactions for achieving a removal efficiency of 90% [85–87]. Tar composition is mainly dependent on the varying equilibrium conditions in different gasifiers. On the other hand, combustion reactions are at equilibrium and do not produce tars [40]. 4. Plasma Chemical Kinetic Modeling: Relationship of Reactor Geometry, Energy Yield, and Mass Transfer in Plasma Reactor for Hydrocarbons and Volatile Organic Carbon Conversion Kinetic modeling and simulation techniques play a pivotal role in advancing sustainable green energy technologies. By utilizing these tools, researchers can enhance system efficiency, reduce emissions, and expand the range of renewable energy sources available for use. Comprehensive research efforts, encompassing both experimental investigations and theoretical modeling, are necessary to drive the development of innovative plasmabased solutions. 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 [88]. Usual plasma process modeling problems initially have the flow of carrier/background gas, breakdown, and transport existing in the field of fluid dynamics. The creation and subsequent destruction exist in the realm of plasma chemistry, and chain reactions within species are kinetic modeling. Chemical species integration decomposition ensures the behavior of process development according to operating conditions and analytical models such as zero-dimensional reaction kinetics chemical model (0-D) and simulation. Kinetic modeling and simulations are simplified with either diffusion consideration in special cases or only computation of reaction rates Figure 6. Tar Reforming Reaction Mechanism main steps. Tar model compounds naphthalene, toluene, and benzene are thermally converted in the presence of hydrogen and steam to understand the mechanism and kinetics of the reaction as a function of temperature in a definite time function of 0.5 s. The order to reactivity is toluene > naphthalene > benzene as the activation energy of benzene is highest while toluene has lowest. The activation energy of toluene, naphthalene, and benzene are 247.1 kJ/mol, 350.2 kJ/mol, and 443.6 kJ/mol, respectively [ 72 ]. In thermal decomposition, mainly benzene is produced. Benzene has the highest activation energy, along with soot and aromatic hydrocarbons, as a principal compound from higher hydrocarbon molecules such as naphthalene. The reduction method should not incorporate an effect on gaseous products such as CO, H 2 , CH 4 , and CO 2 [ 63 , 80 , 83 , 84 ]. In the research of Nair [ 51 ], the author constituted the utilization of pulse discharge for the removal and cleaning of product gas for industrial applications. Experiment conditions were 200 ◦ C for heavy hydrocarbon in an oxidation reaction process [ 51 ]. In pulse corona discharges, an energy transfer from the power source to the gaseous mixture is initiated for radical production, which defines efficiency. Streamer corona discharges are relative to reaction rates and termination reactions for achieving a removal efficiency of 90% [ 85 – 87 ]. Tar composition is mainly dependent on the varying equilibrium conditions in different gasifiers. On the other hand, combustion reactions are at equilibrium and do not produce tars [40]. 4. Plasma Chemical Kinetic Modeling: Relationship of Reactor Geometry, Energy Yield, and Mass Transfer in Plasma Reactor for Hydrocarbons and Volatile Organic Carbon Conversion Kinetic modeling and simulation techniques play a pivotal role in advancing sustainable green energy technologies. By utilizing these tools, researchers can enhance system efficiency, reduce emissions, and expand the range of renewable energy sources available for use. Comprehensive research efforts, encompassing both experimental investigations and theoretical modeling, are necessary to drive the development of innovative plasmabased solutions. 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 [ 88 ]. Usual plasma process modeling problems initially have the flow of carrier/background gas, breakdown, and transport existing in Sustainability 2023,15, 14193 9 of 35 the field of fluid dynamics. The creation and subsequent destruction exist in the realm of plasma chemistry, and chain reactions within species are kinetic modeling. Chemical species integration decomposition ensures the behavior of process development according to operating conditions and analytical models such as zero-dimensional reaction kinetics chemical model (0-D) and simulation. Kinetic modeling and simulations are simplified with either diffusion consideration in special cases or only computation of reaction rates in general. Diffusion terms often consider atomistic calculations [ 89 ]. A detailed consideration for ion-to-neutral species shows an effective channeling for the removal process within a strong electric field. Comparison between two approaches for modeling tar removal ionmolecule infinite fast reactions is used for G-value calculations. The secondary approach designates a kinetic based on ions and electrons. G-value based on the primary approach of ion–neural interaction is more comparable [ 27 , 28 ]. The study incorporates Figure 7, which demonstrates the NTP plasma decomposition and microscopic kinetic timeline of the complex reaction pathways involved in tar decomposition. For a range of up to 240 mm benzene initial concentration, a silent discharge plasma reactor has been investigated. The relationship has been developed for understanding reactor geometry, energy yield, and rate of conversion in benzene with quartz-type material inner electrode and varying diameter. An additional parameter, space velocity, has also been studied. At optimum conditions, an energy yield of 255 J/L, space velocity of 188.1 h −1 at 60% conversion rate with 6 mm and 1333 quartz and bolt type electrodes for applied high voltage. Destruction of benzene has been identified in GC-Ms, which shows pentanoic acid, phenol, hydroquinone, and 4-phenoxy phenol is obtained. Destruction of benzene in NTP is usually very complex in reaction nature. The products usually formed are CO 2 , CO, and H 2 O with electron and radical impact dissociation. The two paths, i.e., electron impact or radicals-based dissociation, play an active role in destruction or decomposition [90]. In NTP reactors, in addition to kinetic modeling, mass transfer is an important feature. Mass transfer and kinetic reactor rates are synergistically effective for novel continuous reactions and show a greater selectivity of heavy hydrocarbon and volatile organic carbons, such as benzene and naphthalene, using photocatalysis. The hybrid modeling approach allows an agreement between kinetic modeling and empirical results of the mass transfer of complex nature in plasma reactions. Synergistic effects and hybrid models allow a simulation of NTP with photocatalysis without knowing the reaction’s plausible pathways and kinetics in-depth with information on mass transfer in relation to total modeling terms [91]. Sustainability 2023, 15, x FOR PEER REVIEW 10 of 38 in general. Diffusion terms often consider atomistic calculations [89]. A detailed consideration for ion-to-neutral species shows an effective channeling for the removal process within a strong electric field. Comparison between two approaches for modeling tar removal ion-molecule infinite fast reactions is used for G-value calculations. The secondary approach designates a kinetic based on ions and electrons. G-value based on the primary approach of ion–neural interaction is more comparable [27,28]. The study incorporates Figure 7, which demonstrates the NTP plasma decomposition and microscopic kinetic timeline of the complex reaction pathways involved in tar decomposition. For a range of up to 240 mm benzene initial concentration, a silent discharge plasma reactor has been investigated. The relationship has been developed for understanding reactor geometry, energy yield, and rate of conversion in benzene with quartz-type material inner electrode and varying diameter. An additional parameter, space velocity, has also been studied. At optimum conditions, an energy yield of 255 J/L, space velocity of 188.1 h −1 at 60% conversion rate with 6 mm and 1333 quartz and bolt type electrodes for applied high voltage. Destruction of benzene has been identified in GC-Ms, which shows pentanoic acid, phenol, hydroquinone, and 4-phenoxy phenol is obtained. Destruction of benzene in NTP is usually very complex in reaction nature. The products usually formed are CO 2 , CO, and H 2 O with electron and radical impact dissociation. The two paths, i.e., electron impact or radicals-based dissociation, play an active role in destruction or decomposition [90]. In NTP reactors, in addition to kinetic modeling, mass transfer is an important feature. Mass transfer and kinetic reactor rates are synergistically effective for novel continuous reactions and show a greater selectivity of heavy hydrocarbon and volatile organic carbons, such as benzene and naphthalene, using photocatalysis. The hybrid modeling approach allows an agreement between kinetic modeling and empirical results of the mass transfer of complex nature in plasma reactions. Synergistic effects and hybrid models allow a simulation of NTP with photocatalysis without knowing the reaction’s plausible pathways and kinetics in-depth with information on mass transfer in relation to total modeling terms [91]. Figure 7. NTP plasma decomposition and microscopic kinetic timeline—based on [90,92]. Plasma discharges are divided mainly into two modes: homogeneous and streamer discharge mode. In plasma modeling, we can include a 1-D or 2-D streamer model for discharge propagation. The phases of modeling are segmented into an avalanche, streamer/discharge, and finally, decay phase. The barrier is also affected by the frequent streamer or discharges, and its surface starts to lose charges. This eventually increases the Figure 7. NTP plasma decomposition and microscopic kinetic timeline—based on [90,92]. Sustainability 2023,15, 14193 16 of 35 Yis the dependent variable, β is the co-efficient, and xis the independent variable, either quantitatively or qualitatively. ε is the residual term. Based on the predictive model, a data analysis graph shows a synergy between EY actual and EY predicted . The predictive models presented in the literature provide a valuable tool for designing and optimizing plasma reactors to achieve high conversion rates and cleaner gas products. However, further research and development are required to explore more complex reactions and improve the performance of plasma-based tar removal systems for practical applications. Energy Yield (EY)g KWh =[(Inlet Concnetration (Cin)−Outlet Concnetration (Cout) ] (Speci f ic Input Energy (SIE)) (10) Sustainability 2023, 15, x FOR PEER REVIEW 17 of 38 Acetone (800 ppm) 300.00 285.00 83.00 Pulsed Corona Reactor ChloroBenzene (244 ppm) 180.00 102.00 97.00 Silent discharge Plasma Reactor Styrene (407 ppm) 300.00 53.60 30.60 Silent discharge Plasma Reactor Trichloroethylene (500 ppm) 25.00 12.40 5.80 Silent discharge Plasma Reactor Styrene (814 ppm) 300.00 115.00 32.80 Silent discharge Plasma Reactor Styrene (1627 ppm) 300.00 299.00 42.70 Silent discharge Plasma Reactor Styrene (2441 ppm) 300.00 461.00 43.80 Silent discharge Plasma Reactor Benzene (80 ppm) 25.00 277.00 83.00 Pulsed Corona Reactor Toluene (100 ppm) 180.00 500.00 32.80 Pulsed Corona Reactor Styrene (200 ppm) 300.00 1660.00 2159.00 Silent discharge Plasma Reactor Figure 11. Benzene reaction generalized decomposition pathway—reprinted from [82] with permission from Elsevier. Researchers have recently focused their work on qualitative rather than quantitative measures. The authors of [95] highlighted the linear regression based on the available data Figure 11. Benzene reaction generalized decomposition pathway—reprinted from [ 82 ] with permission from Elsevier. 7. Development and Validation of Kinetic Models for Hydrocarbon Conversion in Non-Thermal Plasma Reactors In NTP reactions, the chemistry is of a very complex nature. Various plasma reactor types, including pulsed corona and silent discharge plasma reactors, have been investigated for the removal of volatile organic compounds (VOCs) such as benzene, toluene, and styrene. The kinetic models developed for these reactions have been validated against experimental data, and they provide valuable tools for predicting conversion rates and product yields under specific conditions. Usually, the focus is on the conversion of hydrocarbons, typically sighted by Nair as the “plasma CH 4 conversion race”. The model combines experimental results into a sensitivity analysis required for the formation of oxygen at a temperature lower than 200 ◦ C. Selectivity increases the total conversion, which in the model shows greater energy efficiency and lesser energy density. Hydrogen dissociation is complemented by water molecules in the reforming reaction within DBD reactors by Sustainability 2023,15, 14193 17 of 35 radical dissociation. The simple kinetic model involves perfect discharge assumption with no axial and radial direction reactions. A numerical model calculation incorporated the rate constant having a mixed phase and reaction phase. A pseudo-homogenous model is helpful for discharge chemistry. Thermal decomposition happens at the initial temperature point of T = 130–200 ◦ C mainly due to gas composition. The model gives an energy density of 200 kJ/mol. Reaction model conversion and experimental conversion are closely related, and a model is 95% validated [ 86 ]. The kinetic model consists of parameters defining the decomposition of toluene and available soot reduction. The model is the best fit for the irradiation behavior that increases the decomposition rate, and an average error is almost 5%. The kinetic model rate constant is calculated by integral methods based on the experimental data along with Arrhenius equations. The model has been extrapolated for the prediction of toluene conversion and total product yield. Activation energy lowers by 12 times while the resultant heating requirement for conversion as compared to the thermal reactor is six times less. The kinetic model is favorable for the non-reforming process conversion [ 109 ]. Figure 12 illustrates the reaction rate constants for step-by-step plasma kinetic modeling, providing a visual representation of the comprehensive analysis conducted in the study. Sustainability 2023, 15, x FOR PEER REVIEW 19 of 38 Figure 12. Reaction rate constant for step by step plasma kinetic modeling—based on [110]. NTP reactor schematics can be found in the work of Mei and Tu [111]. Abatement of benzene in the DBD reactor is carried out for conversion and by-product formation. Heterogenous phase products are analyzed using GC-MS at optimal specific energy densities. Benzonitriles, phenols, and amides are the main products that depend on SEDs and wt.% of carbon increase or decrease as its functions. At higher SED, products and VOCs are converted to CO 2 . Solid-state products are morphologically studied under different SEDs [112]. 𝐸𝑌=3.6 × 𝑀 24.4 × 𝑐 −𝑐 𝑆𝐸𝐷 (11) M is the benzene molecular weight g/mol, 3.6 is the standard conversion factor, and the standard volume is 24.4 (L/mol) under normal conditions. The author describes a plasma gas phase reaction for benzene removal in which two benzene molecules are collided by excited electrons to form an intermediate derivative phenyl radical. Intermediate, unstable radicals undergo a brisk ring rupture that leads to a variety of radicals. Mainly, secondary benzene accommodates the gaseous pollutant with available collided electrons for the production of H, CO, O 3 , NO 2 , etc. VOC reacts with species for the finalized formation of CO 2 and H 2 O [112]. 8. Role of Energy Density and Efficiency in Plasma Discharge Kinetics Modeling and for Decomposition Reactions The kinetic models have shed light on the reaction rates, intermediate species formation, and the role of active species in the plasma discharge. Advanced numerical simulations and software tools have been employed to simulate and validate the experimental results, providing a deeper understanding of the reaction mechanisms and the factors affecting tar removal efficiency. Moreover, statistical modeling techniques have been used to optimize reactor parameters and energy requirements, leading to improved performance and reduced pollutant emissions. The integration of experimental studies, theoretical kinetic models, and numerical simulations has facilitated the development of efficient and environmentally friendly plasma-based tar removal technologies. These advancements have significant implications for various applications, including gasification processes, combustion systems, and waste treatment. The role of energy density and efficiency in plasma discharge kinetics modeling for decomposition reactions is highly important. Higher specific energy densities (SEDs) lead to increased decomposition efficiency, and various reaction pathways have been proposed to explain the breakdown of VOCs in different plasma reactors. The use of non-thermal plasma reactors has shown significant enhancement in the different gas mixture quality and increased VOC degradation. Plasma discharge accelerates the excited electrons for a unique range of 1 to 10 eV. In the literature, the C=C and C-C bond Figure 12. Reaction rate constant for step by step plasma kinetic modeling—based on [110]. NTP reactor schematics can be found in the work of Mei and Tu [ 111 ]. Abatement of benzene in the DBD reactor is carried out for conversion and by-product formation. Heterogenous phase products are analyzed using GC-MS at optimal specific energy densities. Benzonitriles, phenols, and amides are the main products that depend on SEDs and wt.% of carbon increase or decrease as its functions. At higher SED, products and VOCs are converted to CO 2 . Solid-state products are morphologically studied under different SEDs [112]. EY =3.6 ×M 24.4 ×cin −cout SED (11) Mis the benzene molecular weight g/mol, 3.6 is the standard conversion factor, and the standard volume is 24.4 (L/mol) under normal conditions. The author describes a plasma gas phase reaction for benzene removal in which two benzene molecules are collided by excited electrons to form an intermediate derivative phenyl radical. Intermediate, unstable radicals undergo a brisk ring rupture that leads to a variety of radicals. Mainly, secondary benzene accommodates the gaseous pollutant with available collided electrons for the production of H, CO, O 3 , NO 2 , etc. VOC reacts with species for the finalized formation of CO2and H2O [112]. Sustainability 2023,15, 14193 18 of 35 8. Role of Energy Density and Efficiency in Plasma Discharge Kinetics Modeling and for Decomposition Reactions The kinetic models have shed light on the reaction rates, intermediate species formation, and the role of active species in the plasma discharge. Advanced numerical simulations and software tools have been employed to simulate and validate the experimental results, providing a deeper understanding of the reaction mechanisms and the factors affecting tar removal efficiency. Moreover, statistical modeling techniques have been used to optimize reactor parameters and energy requirements, leading to improved performance and reduced pollutant emissions. The integration of experimental studies, theoretical kinetic models, and numerical simulations has facilitated the development of efficient and environmentally friendly plasma-based tar removal technologies. These advancements have significant implications for various applications, including gasification processes, combustion systems, and waste treatment. The role of energy density and efficiency in plasma discharge kinetics modeling for decomposition reactions is highly important. Higher specific energy densities (SEDs) lead to increased decomposition efficiency, and various reaction pathways have been proposed to explain the breakdown of VOCs in different plasma reactors. The use of nonthermal plasma reactors has shown significant enhancement in the different gas mixture quality and increased VOC degradation. Plasma discharge accelerates the excited electrons for a unique range of 1 to 10 eV. In the literature, the C=C and C-C bond energies are 5.4 eV and correspond to 4.6 eV for the latter. Hence, an energy limit of 5.6 eV is required for the benzene ring dissociation, leading to breakdown, but somehow direct collision leads to different reactions, sometimes intermediate. SEDs increase from a value of 1.54 to 3.79 kJ/L for the required maximum decomposition efficiency of 59.98% to 96%. Unconverted liquid components such as benzene, ethylene, and benzonitrile are detected. Converted benzene results in CO 2 . The solid phase contains phenols and esters, and their granularity is strongly dependent on SED increase, and a reduction in C wt.% is observed. The DBD reactor increases the mineralization phenomena for CO 2 . A two-stage mechanism is observed involving direct electron collision with parent benzene molecule conversion and radical-induced conversion supported by NO3·, OH·, O·, and H·[112]. At a high temperature, producer gas containing benzene was subjected to a microwave reactor. The work has shown that carbon dioxide and hydrogen are vital ingredients in the syngas mixture quality enhancement and benzene degradation [ 60 ]. O, OH, and H are required for significant changes in the reaction components at the conversion rate of 98% [ 60 ]. Nitrogen contribution in naphthalene decomposition is relatively less due to the high dissociation energy of 9.8 eV as compared to oxygen 5.1 eV. An energy density of 75.5 J/L is obtained. A kinetic model based on semi-empirical kinetics for reaction rate constant is based on the log–log scale of concentration change for the residence time along the reaction zone of a non-plasma reactor at P = 0.07 W and 0.138 W for [92,113]. A self-consistent numerical model was developed for gas cleaning from naphthalene within the corona discharge reactor [ 114 ]. The reactor was divided into preand postplasma chemical discharge processes. In pure nitrogen, the naphthalene decomposition rate was several folds bigger than that of the carrier gas mixture [ 114 ]. Two approaches to kinetic modeling have been proposed for pulse corona reactors. The first approach shows an infinitely fast and definite reaction occurring between available ions and molecules, so the G-value is classified for the primary neutral-charged species available. The secondary approach is uniquely based on the ions and electrons interaction for possible plasma chemical kinetics. In the case of naphthalene removal, N 2 is the prior one for removing enhancement, while H 2 O decreases the removal rate and efficiency if added with N 2 . Therefore, a charged species is utilized for cleaning process modification while the G-value is required for the primary non-charged active species [28]. Six benzene derivatives, along with an extra family formic acid, are being subjected to decomposition at ambient conditions in the plasma catalyst discharge system. GHSV range has been 11,000–55,000 h −1 . A global kinetic model shows that 6 VOCs are mainly Sustainability 2023,15, 14193 19 of 35 decomposed by SIE, while residence time, ionization potential, and GHSV have no effect on conversion. Five out of six show first-order kinetics. Only styrene behavior was found peculiar in nature. The K e energy constant has been found from the kinetic model. Results show that the by-product formed is CO 2 and CO, and major CO 2 selectivity has been shown by formic acid (100%), whereas for others, it lies in the range of 70–75%. No catalyst deactivation has been found during decomposing hazardous compounds such as benzene and toluene [ 115 ]. In kinetic modeling, a log-plot illustrates the VOC conversion as a function of the independent variable for different power inputs [ 62 ]. At the same time, a semi-log plot demonstrates the reaction order and rate deduction for benzene in terms of SIE (Specific Energy Input) versus the concentration of carrier gases as given by Equation (12). Vinlet −Vout =−Ke×SIE (12) Pemen studied the global synthesis energy of plasma-prompted chemical reactions for VOC’s pollution management. Assuming there are no critical extreme end reactions, direct collision reactions are important. Existing pollutant removal is only dependent on the plasma energy density, and the energy yield is constant. The current kinetic model removal rate demonstrates efficiency as the function of energy input density. Assuming that nonlinearity has a significant influence on plasma reactions. The removal rate is determined by the square base of the corona input energy density. These characteristics are investigated in the context of VOCs and generalized for other pollutants. Figure 13 shows a benzene decomposition semi-plot for power vs log concentration changes. Sustainability 2023, 15, x FOR PEER REVIEW 21 of 38 investigated in the context of VOCs and generalized for other pollutants. Figure 13 shows a benzene decomposition semi-plot for power vs log concentration changes. An important question is how to combine a corona existing plasma reactor with a voltage-induced pulse generator to boost absolute energy productivity. An input voltage is identified for a given reactor to match a pulse generator. The voltage rising time, the result impedance of a pulse generator, and the depleted and sway capacitance of a reactor have all been improved. For example, the research looks at a 5.0 kW nonthermal plasma framework for NOx removal from exhaust gases. A simplified kinetic model for experimental results can be either exponential, linear, or square base, highly dependent on the reactor input energy density [116]. G is the standard calculator for the corona discharge reactors. It is defined as the energy cost per molecule removed for the energy yield. β is the specific energy, which increases as the G-value decreases with the removal efficiency. Whilst energy density decreases as energy cost decreases for the removal of molecules, total energy yield increases accordingly. Increasing the corona energy density, the energy cost for each molecule X removal increases while the energy yield decreases [85,116,117]. 𝑋 𝑋 =exp (−𝐸𝛽) (13) Figure 14 presents the reactor modeling based on reactor performance in a silent discharge plasma reactor, illustrating the energy cost and beta value at different temperature zones for multiple concentrations of various pollutants. Figure 13. Benzene decomposition semi-plot for power vs log concentration changes—reprinted from [51] with permission from Elsevier; (a) ln (C/Co) vs. Power (benzene) (b) ln (C/Co) vs. Power (CH4). Figure 13. Benzene decomposition semi-plot for power vs log concentration changes—reprinted from [ 51 ] with permission from Elsevier; ( a ) ln (C/Co) vs. Power (benzene) ( b ) ln (C/C o ) vs. Power (CH4). An important question is how to combine a corona existing plasma reactor with a voltage-induced pulse generator to boost absolute energy productivity. An input voltage is identified for a given reactor to match a pulse generator. The voltage rising time, the result impedance of a pulse generator, and the depleted and sway capacitance of a reactor have all been improved. For example, the research looks at a 5.0 kW nonthermal plasma framework for NOx removal from exhaust gases. A simplified kinetic model for experimental results can be either exponential, linear, or square base, highly dependent on the reactor input energy density [ 116 ]. G is the standard calculator for the corona discharge reactors. It is defined as the energy cost per molecule removed for the energy yield. β is the specific energy, which increases as the G-value decreases with the removal efficiency. Whilst energy density decreases as energy cost decreases for the removal of molecules, total energy Sustainability 2023,15, 14193 20 of 35 yield increases accordingly. Increasing the corona energy density, the energy cost for each molecule Xremoval increases while the energy yield decreases [85,116,117]. X X0=exp−E β(13) Figure 14 presents the reactor modeling based on reactor performance in a silent discharge plasma reactor, illustrating the energy cost and beta value at different temperature zones for multiple concentrations of various pollutants. Sustainability 2023, 15, x FOR PEER REVIEW 22 of 38 Figure 14. Reactor modeling based on reactor performant in silent discharger plasma reactor for energy cost, beta value at different temperature zones for multiple concentration of various pollutants—based on [108]. 9. Kinetic Modeling and Energy Efficiency in a Non-Thermal Plasma Reactor for VOC Pollutant Removal and Management In environmental chemistry, aerosol is a major pollutant for health concerns comprising a benzene ring compound. In AC/DC stream, benzene, toluene, and naphthalene are designated for decomposition in the corona discharge reactor. The linear kinetic model removal rate is shown by toluene and benzene in the corona discharge streamer, while styrene is a slow reactive species and shows an even trend for input power. Energy density lies in 27–32 J/L with a concentration factor of 9 × 105 cm−3 for benzene and toluene and is highest for styrene. Corona discharge is cost effective and leads to fewer by-products [107]. Comparative modeling for three gas streams, i.e., engine, methane combustion, and pollutant air, has been studied in two different reactors, i.e., the corona discharge reactor and silent barrier discharge reactor. Comparative modeling predicts the conversion and estimates the discharges [118]. Toluene is removed in a bi-gaseous mixture within a DBD reactor at less than 300 °C. The total wt.% of O2 is kept at less than 10% and, at minimum, produces the overall best removal efficiency. H and O radicals increase removal rate because hydroxyl radical ions formed by recombination increase the removal rate. Three hydrocarbons, namely acetone, benzene, and ethane, when heated up to 300 °C to increase DBD reactor removal efficiency. Solid deposition has been confirmed by the plasma gas phase reaction. A simplified kinetic model of global nature is suggested by the author. Four-step reactions for available species, ions or radical R, are responsible for the pollutant removal. N2 is regarded as the breakdown of species R for the reactions at different metastable states [102,110]. In Figure 15, a global generalized kinetic model for pollutant removal in a non-thermal plasma (NTP) reactor is presented, showcasing the comprehensive understanding and predictive capabilities achieved in the field of plasmabased tar removal and pollutant abatement. Figure 14. Reactor modeling based on reactor performant in silent discharger plasma reactor for energy cost, beta value at different temperature zones for multiple concentration of various pollutants— based on [108]. 9. Kinetic Modeling and Energy Efficiency in a Non-Thermal Plasma Reactor for VOC Pollutant Removal and Management In environmental chemistry, aerosol is a major pollutant for health concerns comprising a benzene ring compound. In AC/DC stream, benzene, toluene, and naphthalene are designated for decomposition in the corona discharge reactor. The linear kinetic model removal rate is shown by toluene and benzene in the corona discharge streamer, while styrene is a slow reactive species and shows an even trend for input power. Energy density lies in 27–32 J/L with a concentration factor of 9 × 10 5 cm −3 for benzene and toluene and is highest for styrene. Corona discharge is cost effective and leads to fewer by-products [ 107 ]. Comparative modeling for three gas streams, i.e., engine, methane combustion, and pollutant air, has been studied in two different reactors, i.e., the corona discharge reactor and silent barrier discharge reactor. Comparative modeling predicts the conversion and estimates the discharges [ 118 ]. Toluene is removed in a bi-gaseous mixture within a DBD reactor at less than 300 ◦ C. The total wt.% of O 2 is kept at less than 10% and, at minimum, produces the overall best removal efficiency. H and O radicals increase removal rate because hydroxyl radical ions formed by recombination increase the removal rate. Three hydrocarbons, namely acetone, benzene, and ethane, when heated up to 300 ◦ C to increase DBD reactor removal efficiency. Solid deposition has been confirmed by the plasma gas phase reaction. A simplified kinetic model of global nature is suggested by the author. Four-step reactions for available species, ions or radical R, are responsible for the pollutant removal. N 2 is regarded as the breakdown of species R for the reactions at different metastable states [ 102 , 110 ]. In Figure 15, a global generalized kinetic model for pollutant removal in a non-thermal plasma (NTP) reactor is presented, showcasing the comprehensive understanding and predictive capabilities achieved in the field of plasma-based tar removal and pollutant abatement. Sustainability 2023,15, 14193 21 of 35 Sustainability 2023, 15, x FOR PEER REVIEW 23 of 38 Figure 15. Global generalized kinetic model for pollutant removal in NTP reactor—based on [109]. T is the inlet and outlet concentration in ppm, v c is the required characteristic frequency. Ozone formation is comparatively less as the oxygen density is kept constant in the range of 10 14 –10 15 cm 3. [119]. The tar decomposition kinetic model in the N 2 stream is interpreted based on irradiance, soot reduction, and experimental observations, not just single factor extrapolation, so results are more authentic. The proposed model is a baseline for future reactor design and optimization studies. Pre-exponential factors and temperature are kept constant for activation energy value deduction. Toluene chain reactions lead to radical formation in the decomposition initiation phase. Radicals react with more toluene molecules and lead to further radical propagation [109]. Figure 16 presents the suggested assumptions for the kinetic model, providing valuable insights into the complex reaction pathways involved in pollutant removal. Figure 16. Suggested assumptions for kinetic model in tar removal in an NTP reactor—based on [109]. Figure 15. Global generalized kinetic model for pollutant removal in NTP reactor—based on [109]. Tis the inlet and outlet concentration in ppm, v c is the required characteristic frequency. Ozone formation is comparatively less as the oxygen density is kept constant in the range of 10 14 –10 15 cm 3 [ 119 ]. The tar decomposition kinetic model in the N 2 stream is interpreted based on irradiance, soot reduction, and experimental observations, not just single factor extrapolation, so results are more authentic. The proposed model is a baseline for future reactor design and optimization studies. Pre-exponential factors and temperature are kept constant for activation energy value deduction. Toluene chain reactions lead to radical formation in the decomposition initiation phase. Radicals react with more toluene molecules and lead to further radical propagation [ 109 ]. Figure 16 presents the suggested assumptions for the kinetic model, providing valuable insights into the complex reaction pathways involved in pollutant removal. Sustainability 2023, 15, x FOR PEER REVIEW 23 of 38 Figure 15. Global generalized kinetic model for pollutant removal in NTP reactor—based on [109]. T is the inlet and outlet concentration in ppm, v c is the required characteristic frequency. Ozone formation is comparatively less as the oxygen density is kept constant in the range of 10 14 –10 15 cm 3. [119]. The tar decomposition kinetic model in the N 2 stream is interpreted based on irradiance, soot reduction, and experimental observations, not just single factor extrapolation, so results are more authentic. The proposed model is a baseline for future reactor design and optimization studies. Pre-exponential factors and temperature are kept constant for activation energy value deduction. Toluene chain reactions lead to radical formation in the decomposition initiation phase. Radicals react with more toluene molecules and lead to further radical propagation [109]. Figure 16 presents the suggested assumptions for the kinetic model, providing valuable insights into the complex reaction pathways involved in pollutant removal. Figure 16. Suggested assumptions for kinetic model in tar removal in an NTP reactor—based on [109]. Figure 16. Suggested assumptions for kinetic model in tar removal in an NTP reactor—based on [ 109 ]. Predicted parameters verified the experimental data through the evaluation of model best fits, standard deviations, and sums of squares errors of a fitted parameter. The standard error of the estimate (SEE) or the combined residual variance is given in [95,109]. Sustainability 2023,15, 14193 22 of 35 F exp,i ,F mod,i , and n are the product yield and number of readings, respectively, by experimental and estimated. SEE =s∑n iFexp,i−Fmod,i2 n−2(14) Figure 17 illustrates the application of machine learning and numerical analysis in kinetic modeling to predict experimental outcomes. Kinetic modeling plays a crucial role in understanding and optimizing the behavior of complex reactions, such as tar decomposition in plasma reactors. However, accurately predicting the reaction rates and intermediate species formation can be challenging due to the intricate nature of the plasma discharge process. To address this challenge, researchers have integrated machine learning techniques with numerical analysis to enhance the predictive capabilities of kinetic models. Machine learning algorithms, such as neural networks and support vector machines, are trained on a vast dataset of experimental results to identify patterns and relationships between input parameters and reaction outcomes. The numerical analysis then employs these trained models to predict the behavior of the plasma reactor under different conditions. By incorporating machine learning into kinetic modeling, scientists can obtain more accurate and reliable predictions for tar decomposition and pollutant removal in plasma-based systems. This approach not only improves the understanding of reaction mechanisms but also aids in optimizing reactor configurations and energy inputs to achieve higher efficiency and cleaner gas products. Figure 18 serves as a visual representation of this innovative approach, showcasing how machine learning and numerical analysis work together to advance the field of sustainable green energy and plasma technology. Sustainability 2023, 15, x FOR PEER REVIEW 24 of 38 Predicted parameters verified the experimental data through the evaluation of model best fits, standard deviations, and sums of squares errors of a fitted parameter. The standard error of the estimate (SEE) or the combined residual variance is given in [95,109]. F exp,i , F mod,i , and n are the product yield and number of readings, respectively, by experimental and estimated. 𝑆𝐸𝐸=𝐹,−𝐹, 𝑛−2 (14) Figure 17 illustrates the application of machine learning and numerical analysis in kinetic modeling to predict experimental outcomes. Kinetic modeling plays a crucial role in understanding and optimizing the behavior of complex reactions, such as tar decomposition in plasma reactors. However, accurately predicting the reaction rates and intermediate species formation can be challenging due to the intricate nature of the plasma discharge process. To address this challenge, researchers have integrated machine learning techniques with numerical analysis to enhance the predictive capabilities of kinetic models. Machine learning algorithms, such as neural networks and support vector machines, are trained on a vast dataset of experimental results to identify patterns and relationships between input parameters and reaction outcomes. The numerical analysis then employs these trained models to predict the behavior of the plasma reactor under different conditions. By incorporating machine learning into kinetic modeling, scientists can obtain more accurate and reliable predictions for tar decomposition and pollutant removal in plasma-based systems. This approach not only improves the understanding of reaction mechanisms but also aids in optimizing reactor configurations and energy inputs to achieve higher efficiency and cleaner gas products. Figure 18 serves as a visual representation of this innovative approach, showcasing how machine learning and numerical analysis work together to advance the field of sustainable green energy and plasma technology. Figure 17. A General Numerical computation results for software calculations of kinetic model in terms of temperature and time along the reactor length. Figure 17. A General Numerical computation results for software calculations of kinetic model in terms of temperature and time along the reactor length. Sustainability 2023,15, 14193 23 of 35 Sustainability 2023, 15, x FOR PEER REVIEW 25 of 38 Figure 18. Predictive outcome for the experimental results for kinetic modeling using machine learning and numerical analysis—based on [95]. 10. Kinetic Modeling for Tar Decomposition in a Plasma Reactor with Effects of Energy Density and Carrier Gases An energy band of 7–8 eV is required for the gasifier product gas and pollutant removal in a cylindrical dielectric plasma reactor within N 2 + O 2 carrier gaseous mixture at a temperature of 350 K. In a DBD reactor, gas temperature increases substantially, thus not procuring many dangerous by-products. Reactor removal efficiency is 98%. The experimental and theoretical spectrum for an “Electron Energy Distribution Function (EEDF) model” comparison has been made, having ≤2% moisture content presence. Three stages of the chemical reduction procedure are presented [120]. • Defining chemical reactor discharge and Model Boltzmann kinetics on the basis of the main removal of analogue compound representative chemistry; • Characterization studies: rotational temperature calculation based on optical emission spectroscopy; • Average electron energy calculation for the second positive and first negative system discharge calculation for nitrogen molecules has been performed. Based on the above three chemical reduction calculations, the mechanism has been assessed for generalized studies [70,120]. Chemkin (ANSYS 2022 R1) is good software for understanding the combustion behavior in plasma conditions for hydrocarbon and air mixtures. However, as the reaction kinetics becomes tedious, a greater amount of ion, radical-based kinetics data is required for the conversion calculation. At this point, this very viable software falls short. Chemkin is readily available due to the missing calculations of electric or magnetic fields in the Figure 18. Predictive outcome for the experimental results for kinetic modeling using machine learning and numerical analysis—based on [95]. 10. Kinetic Modeling for Tar Decomposition in a Plasma Reactor with Effects of Energy Density and Carrier Gases An energy band of 7–8 eV is required for the gasifier product gas and pollutant removal in a cylindrical dielectric plasma reactor within N 2 + O 2 carrier gaseous mixture at a temperature of 350 K. In a DBD reactor, gas temperature increases substantially, thus not procuring many dangerous by-products. Reactor removal efficiency is 98%. The experimental and theoretical spectrum for an “Electron Energy Distribution Function (EEDF) model” comparison has been made, having ≤ 2% moisture content presence. Three stages of the chemical reduction procedure are presented [120]. • Defining chemical reactor discharge and Model Boltzmann kinetics on the basis of the main removal of analogue compound representative chemistry; • Characterization studies: rotational temperature calculation based on optical emission spectroscopy; • Average electron energy calculation for the second positive and first negative system discharge calculation for nitrogen molecules has been performed. Based on the above three chemical reduction calculations, the mechanism has been assessed for generalized studies [70,120]. Chemkin (ANSYS 2022 R1) is good software for understanding the combustion behavior in plasma conditions for hydrocarbon and air mixtures. However, as the reaction kinetics becomes tedious, a greater amount of ion, radical-based kinetics data is required for the conversion calculation. At this point, this very viable software falls short. Chemkin is readily available due to the missing calculations of electric or magnetic fields in the plasma calculations [ 121 ]. Figure 18 illustrates general numerical computation results for software Sustainability 2023,15, 14193 24 of 35 calculations of the kinetic model, showcasing the temperature and time variations along the length of the reactor. The second streamer limits the decomposition rate, and the temperature of the reaction process enhances the total conversion of analogue compounds. Energy consumption is effectively controlled by controlling the rate of radical propagation to minimise by-products that are usually unwanted carcinogenic. Thus, the gas distribution is increased with respect to the total flow rate of carrier gases [ 122 ]. A scheme of 57 species accounting for 308 reactions has been considered for tar removal in CO 2 carrier gas and validating reactions towards certain products. For mechanistic modeling, optical emission spectroscopy is utilized within experimentation that sheds light on reaction routes, products formed, and reaction rates based on emission spectra. Numerical kinetic modeling for the discharge process is conducted in two steps using the KINETICS program. Firstly, the kinetic equation is formed in the differential equation standard models. METAN 1.14 solves and integrates the differential equation model numerically. Data is extracted from NIST. To keep the computation easy, the system removes the C 4 greater hydrocarbon if not necessary [ 123 ]. Figure 19 illustrates the experimentation of plasma reactor and kinetic modeling, showcasing the formulation and simulation processes using open-source data, software tools, and numerical computation. Sustainability 2023, 15, x FOR PEER REVIEW 26 of 38 plasma calculations [121]. Figure 18 illustrates general numerical computation results for software calculations of the kinetic model, showcasing the temperature and time variations along the length of the reactor. The second streamer limits the decomposition rate, and the temperature of the reaction process enhances the total conversion of analogue compounds. Energy consumption is effectively controlled by controlling the rate of radical propagation to minimise byproducts that are usually unwanted carcinogenic. Thus, the gas distribution is increased with respect to the total flow rate of carrier gases [122]. A scheme of 57 species accounting for 308 reactions has been considered for tar removal in CO 2 carrier gas and validating reactions towards certain products. For mechanistic modeling, optical emission spectroscopy is utilized within experimentation that sheds light on reaction routes, products formed, and reaction rates based on emission spectra. Numerical kinetic modeling for the discharge process is conducted in two steps using the KINETICS program. Firstly, the kinetic equation is formed in the differential equation standard models. METAN 1.14 solves and integrates the differential equation model numerically. Data is extracted from NIST. To keep the computation easy, the system removes the C 4 greater hydrocarbon if not necessary [123]. Figure 19 illustrates the experimentation of plasma reactor and kinetic modeling, showcasing the formulation and simulation processes using open-source data, software tools, and numerical computation. Figure 19. Experimentation of plasma reactor and kinetic modeling for formulation and simulation using open-source data. 11. Efficiency of Tar Removal in Pulse Discharge Reactors using NIST-Based Kinetic Modeling, and Numerical Simulation Pemen proposed a synergistic resemblance of plasma with catalyst system as it usually reduces the severity of the process operational condition. NIST-based reaction data is used for deducing a model which curtails a large governance system for reaction conditioning. The streamer corona reactor has multiple frequencies of repetition for naphthalene as a tar model compound for removal efficiency studies. The simulation is bi-stage for gas cleaning. The initial stage is triggered by streamer propagation in a repetition sequence. Naphthalene is removed in carrier gaseous mixture of N 2 , CO, H 2 , and CO 2 of 50%, 20%, 12%, 17%, and 1% volumetrically [124]. The rate of streamer propagation is governed by the kinetic and transport of electrons and in photoionization leading to absorption of emitted radiation in the streamer head. The tar degradation that happens in nitrogen is similar to that in air, but photoionization in N 2 is double fold in comparison to air. The simulation proposed the G-value for the production of active species in the given mixture composition wights about partial G-values. A tubular reactor is considered, which accounts for the dead zones, and the energy deposition is given in [115,124]. Figure 19. Experimentation of plasma reactor and kinetic modeling for formulation and simulation using open-source data. 11. Efficiency of Tar Removal in Pulse Discharge Reactors using NIST-Based Kinetic Modeling, and Numerical Simulation Pemen proposed a synergistic resemblance of plasma with catalyst system as it usually reduces the severity of the process operational condition. NIST-based reaction data is used for deducing a model which curtails a large governance system for reaction conditioning. The streamer corona reactor has multiple frequencies of repetition for naphthalene as a tar model compound for removal efficiency studies. The simulation is bi-stage for gas cleaning. The initial stage is triggered by streamer propagation in a repetition sequence. Naphthalene is removed in carrier gaseous mixture of N 2 , CO, H 2 , and CO 2 of 50%, 20%, 12%, 17%, and 1% volumetrically [ 124 ]. The rate of streamer propagation is governed by the kinetic and transport of electrons and in photoionization leading to absorption of emitted radiation in the streamer head. The tar degradation that happens in nitrogen is similar to that in air, but photoionization in N 2 is double fold in comparison to air. The simulation proposed the G-value for the production of active species in the given mixture composition wights about Sustainability 2023,15, 14193 25 of 35 partial G-values. A tubular reactor is considered, which accounts for the dead zones, and the energy deposition is given in [115,124]. Wsyst =Ep Q×f(15) 340 elementary reaction contains almost 52 species in the tubular reactor, containing variables such as energy per pulse Ep Q flow rate and volume of the reactor. A specific energy influx per stream is Wst = ne/Ge (16) and the per cycle treatment is Ncv = Esys/Wsyst (17) while the temperature is kept constant. Simulation of reaction species is performed in RADICAL software 3.7. The narrow type of streamer flow channel is non-uniform in nature and distributed along space, and energy is mainly discrete during several pulses. Models consider streamer spreading mainly due to disunion and the changing composition of gas in the background. Experimental and simulation comparison show a reaction is controlled mainly due to excited nitrogen molecules. Greater addition of nitrogen composition reduces the rate of removal [115,116,124] . The work [ 114 ] studied the conversion of naphthalene along with numerical simulations to build a self-consistent model having the impact of corona discharges with repeated pulses. H 2 , CO, CO 2 , and N 2 molecules present in the form of (N2(A3 Σ )) dissociate to an excited state and decompose naphthalene for cleaning. Numerical simulation is divided into two concurrent stages based on experimentation conditions. Firstly, active species, i.e., ions, excited species, and radicals, are primarily generated within the discharge zone. In the secondary stage, a thin discharge filament plasma is generated by streamer and repeated propagations. Primary species have comparative densities and are required for the second-stage inputs for modeling studies showing chemical transformation usually induced by first-stage active species. RADICAL software incorporates tar conversion in a pulse reactor for kinetic modeling, which is a model for thin streamer input energy in the non-uniform spatial vicinity for active species inside the streamer reactor. Streamer models consider the traces and chemical compositions for modeling the sets of equations for different components n j . Nitrogen with CO, CO 2, and H 2 decreases the removal rate and plays a pivotal role in gas cleaning. Electrons impact the dissociation for the further formation of nitrogenous compounds. Excited nitrogen molecules also add on to other tar compounds in addition to naphthalene at 770 K, with temperature having a substantial effect on removal efficiency. Removal efficiency, denoted by G increase, by T that shows T varies the rate constant. Ber and Pemen studied the cracking of heavy hydrocarbon in a pulse discharge reactor, with heavy hydrocarbon generated from biogas. Portfolio has the capability of operating at high temperatures with a voltage of 100 kV and repeated pulse rate of 600–150 per second. At 1.5 kW of average power, cracked hydrocarbon tar is of a gaseous nature at 1 bar pressure and within a high temperature for a 0.25 m diameter and 3 m long stainlesssteel corona wire along the axis. Product gas thermodynamics and kinetics are strongly dependent on contained C, O, H, and N contents, as well as pressure, and temperature. Tar compounds produced concentration and composition on the equilibrium conditions, i.e., pyrene concentration equilibrium decreases as biogas temperature increases. The author proposes a first-order kinetic model for heavy hydrocarbon removal at 200 ◦ C. In N 2 presence, tar is cracked by a combined corona pulsed reactor with a range of 39 J/L to 154 J/L energy densities. Power input converts higher hydrocarbon to syngas [ 75 , 125 ]. The results of heavy and light tar removal by pulsed corona in the outlet of a real wood-fueled gasifier (BTG Almelo, Enschede, The Netherlands) are illustrated in Figure 20. The Langmuir–Hinshelwood relation plays a crucial role in understanding the nitric reaction kinetics within a minimized photocatalytic continuous microreactor. This kinetic Sustainability 2023,15, 14193 32 of 35 28. Filimonova, E.; Naidis, G. Effect of gas mixture composition on tar removal process in a pulsed corona discharge reactor. J. Phys. Conf. Ser. 2010,257, 012018. 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