“…The following recombination/dissociation reactions (Eqs. (9) , (10) , (11) , (12) , (13) , (14) ) may also take place to produce lower hydrocarbons [ [36] , [37] , [38] , [39] ]. Fig.…”
“…The following recombination/dissociation reactions (Eqs. (9) , (10) , (11) , (12) , (13) , (14) ) may also take place to produce lower hydrocarbons [ [36] , [37] , [38] , [39] ]. Fig.…”
“…Faisal et al [36] reveals clear trends regarding the effects of input power, concentration of the tar analogue compound, and residence time. Increasing the input power results in higher decomposition of benzene.…”
Section: Experimental Methodology and Materialsmentioning
confidence: 98%
“…In this study, a dataset was obtained from an experiment [36] using a coaxial NTP single-stage DBD reactor to decompose benzene, a tar model compound. The experimental setup involved a plasma reactor with a plasma zone between quartz tubes, operating at a frequency of 20 kHz and a voltage of 20 kV.…”
Section: Experimental Methodology and Materialsmentioning
confidence: 99%
“…Among all the VOCs, benzene is the least reactive and has a slower reaction rate constant [32]. Although current tar definition excludes benzene, it is still used as a model compound in many studies on plasma decomposition of tars [30,[33][34][35][36]. The reason for this is the aforementioned stability and low reactivity.…”
This study examines the sustainable decomposition reactions of benzene using non-thermal plasma (NTP) in a dielectric barrier discharge (DBD) reactor. The aim is to investigate the factors influencing benzene decomposition process, including input power, concentration, and residence time, through kinetic modeling, reactor performance assessment, and machine learning techniques. To further enhance the understanding and modeling of the decomposition process, the researchers determine the apparent decomposition rate constant, which is incorporated into a kinetic model using a novel theoretical plug flow reactor analogy model. The resulting reactor model is simulated using the ODE45 solver in MATLAB, with advanced machine learning algorithms and performance metrics such as RMSE, MSE, and MAE employed to improve accuracy. The analysis reveals that higher input discharge power and longer residence time result in increased tar analogue compound (TAC) decomposition. The results indicate that higher input discharge power leads to a significant improvement in the TAC decomposition rate, reaching 82.9%. The machine learning model achieved very good agreement with the experiments, showing a decomposition rate of 83.01%. The model flagged potential hotspots at 15% and 25% of the reactor’s length, which is important in terms of engineering design of scaled-up reactors.
“…Nonthermal plasma (NTP) technology offers several advantages for tar removal from gasification processes. ,,,,,,− Some of the key advantages of using nonthermal plasma for tar removal include the following: (1) high reaction rates − due to the presence of energetic electrons and other species; (2) nonselective reactivity − , due to the possibility to simultaneously break down a wide range of tar compounds in the mix unlike catalysts, a suitable feature for scenarios such as gasifier tar containing mixture of tar compounds; (3) wide operating temperature ranges; ,− (4) scalability, allowing gas cleaning in different scales of gasification operations from lab and pilot scales to industrial scale; and (5) environmental benefits ,, due to converting the tar compound rather than separating the tar compounds from the gas, which pollutes the environment upon disposal.…”
Section: Tar Destruction/removal:
Existing Technologiesmentioning
Gasification is an advanced thermochemical process that converts carbonaceous feedstock into syngas, a mixture of hydrogen, carbon monoxide, and other gases. However, the presence of tar in syngas, which is composed of higher molecular weight aromatic hydrocarbons, poses significant challenges for the downstream utilization of syngas. This Review offers a comprehensive overview of tar from gasification, encompassing gasifier chemistry and configuration that notably impact tar formation during gasification. It explores the concentration and composition of tar in the syngas and the purity of syngas required for the applications. Various tar removal methods are discussed, including mechanical, chemical/catalytic, and plasma technologies. The Review provides insights into the strengths, limitations, and challenges associated with each tar removal method. It also highlights the importance of integrating multiple techniques to enhance the tar removal efficiency and syngas quality. The selection of an appropriate tar removal strategy depends on factors such as tar composition, gasifier operating and design factors, economic considerations, and the extent of purity required at the downstream application. Future research should focus on developing cleaning strategies that consume less energy and cause a smaller environmental impact.
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