Abstract
This study focuses on the numerical modelling of the decomposition process of carbon tetrafluoride (CF4, Freon R-14), the most stable perfluorinated gas with a significant global warming potential, as experimentally carried out using a thermal argon/steam plasma in a plasma-chemical reactor. The numerical modelling approach incorporates advanced computational fluid dynamics (CFD) techniques and the kinetics of a complex chemical reaction mechanism. A two-dimensional axisymmetric model, incorporating the conservation of mass, momentum, energy and chemical balances, has been developed to simulate the species distribution under the assumption of steady flow, including turbulence effects. In order to compare the species distributions and destruction efficiencies, two chemical reaction mechanism models of different complexity were implemented: a simplified model A with 196 reactions and a model B with 258 reactions. The simulations were performed for plasma generated by steam and argon stabilized DC plasma torch under three operating conditions for arc currents of 120, 150 and 200 A. The destruction and removal efficiency (DRE) of CF4 decomposition at a high feed rate was computed by both models, with an error of less than 6% compared to the experimental data. It can be concluded that both models accurately predict the residual CF4 concentration trends depending on the feed per input torch power. While model A overestimates process efficiency, model B, with a more balanced reaction mechanism, more closely aligns with experimental results. Model B provides an accurate prediction of the DRE and captures trends as a function of input parameters. Importantly and innovatively, the model presented in this study, unlike previous models, allows for the first time the inclusion of both high concentrations and a sufficiently large reaction mechanism for CF4 abatement. This model presents a novel opportunity to perform trend simulations of operating characteristics for the CF4 abatement problem at a quantitative level, allowing experimental design to be evaluated and optimised.
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