Abstract: Plasma catalysis is a rapidly growing field, often employing a packed-bed dielectric barrier discharge plasma reactor. Such dielectric barrier discharges are complex, especially when a packing material (e.g., a catalyst) is introduced in the discharge volume. Catalysts are known to affect the plasma discharge, though the underlying mechanisms influencing the plasma physics are not fully understood. Moreover, the effect of the catalysts on the plasma discharge and its subsequent effect on the overall performance is often overlooked. In this work, we deliberately design and synthesize catalysts to affect the plasma discharge in different ways. These Ni or Co alumina-based catalysts are used in plasma-catalytic dry reforming of methane and ammonia synthesis. Our work shows that introducing a metal to the dielectric packing can affect the plasma discharge, and that the distribution of the metal is crucial in this regard. Further, the altered discharge can greatly influence the overall performance. In an atmospheric pressure dielectric barrier discharge reactor, this apparently more uniform plasma yields a significantly better performance for ammonia synthesis compared to the more conventional filamentary discharge, while it underperforms in dry reforming of methane. This study stresses the importance of analyzing the plasma discharge in plasma catalysis experiments. We hope this work encourages a more critical view on the plasma discharge characteristics when studying various catalysts in a plasma reactor.

Abstract: This study investigates the dry reformation of methane (DRM) over Ni/Al2O3 catalysts in a dielectric barrier discharge (DBD) non-thermal plasma reactor. A novel hybrid machine learning (ML) model is developed to optimize the plasma-catalytic DRM reaction with limited experimental data. To address the non-linear and complex nature of the plasma-catalytic DRM process, the hybrid ML model integrates three well-established algorithms: regression trees, support vector regression, and artificial neural networks. A genetic algorithm (GA) is then used to optimize the hyperparameters of each algorithm within the hybrid ML model. The ML model achieved excellent agreement with the experimental data, demonstrating its efficacy in accurately predicting and optimizing the DRM process. The model was subsequently used to investigate the impact of various operating parameters on the plasma-catalytic DRM performance. We found that the optimal discharge power (20 W), CO2/CH4 molar ratio (1.5), and Ni loading (7.8 wt%) resulted in the maximum energy yield at a total flow rate of
51 mL/min. Furthermore, we investigated the relative significance of each operating parameter on the performance of the plasmacatalytic DRM process. The results show that the total flow rate had the greatest influence on the conversion, with a significance exceeding 35% for each output, while the Ni loading had the least impact on the overall reaction performance. This hybrid model demonstrates a remarkable ability to extract valuable insights from limited datasets, enabling the development and optimization of more efficient and selective plasma-catalytic chemical processes.

Abstract: A combination of a gliding arc plasmatron (GAP) reactor and a newly designed tubular catalyst bed (N-bed) was applied to investigate the post-plasma catalytic (PPC) effect for dry reforming of methane (DRM). As comparison, a traditional plasma catalyst bed (T-bed) was also utilized. The post-plasma catalytic effect of a Ni-based mixed oxide (Ni/MO) catalyst with a thermal catalytic performance of 77% CO2 and 86% CH4 conversion at 700 ℃ was studied. Although applying the T-bed had little effect on plasma based CO2 and CH4 conversion, an increase in selectivity to H2 was obtained with a maximum value of 89% at a distance of 2 cm. However, even when only α-Al2O3 packing material was used in the N-bed configuration, compared to the plasma alone and the T-bed, an increase of the CO2 and CH4 conversion from 53% and 53% to 69% and 69% to 83% was achieved. Addition of the Ni/MO catalyst further enhanced the DRM reaction, resulting in conversions of 79% for CO2 and 91% for
CH4. Hence, although no insulation nor external heating was applied to the N-bed post plasma, it provides a slightly better conversion than the thermal catalytic performance with the same catalyst, while being fully electrically driven. In addition, an enhanced CO selectivity to 96% was obtained and the energy cost was reduced from ~ 6 kJ/L (plasma alone) to 4.3 kJ/L. To our knowledge, it is the first time that a post-plasma catalytic system achieves this excellent catalytic performance for DRM without extra external heating or insulation.

Abstract: Computer simulations have been performed to describe the effect of small admixtures of hydrogen to an argon glow discharge in the Grimm-type configuration. The two-dimensional density profiles of the various plasma species (i.e., electrons, Ar+, ArH+, H+, H2+ and H3+ ions, H atoms and H2 molecules, Ar metastable atoms and sputtered Cu atoms) are presented for 1% H2 added to the argon glow discharge, and the effect of different H2 additions (varying between 0.1 and 10%) on the species densities, the hydrogen dissociation degree, and the sputtering process, are investigated. Finally, the relative contributions of various production and loss processes for the different plasma species are calculated.