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| Author | Wang, W.; Mei, D.; Tu, X.; Bogaerts, A. | ||||
| Title | Gliding arc plasma for CO 2 conversion: Better insights by a combined experimental and modelling approach | Type | A1 Journal article | ||
| Year | 2017 | Publication | Chemical engineering journal | Abbreviated Journal | Chem Eng J |
Volume  |
330 | Issue | Pages | 11-25 | |
| Keywords | A1 Journal article; Plasma Lab for Applications in Sustainability and Medicine – Antwerp (PLASMANT) | ||||
| Abstract | A gliding arc plasma is a potential way to convert CO2 into CO and O2, due to its non-equilibrium character, but little is known about the underlying mechanisms. In this paper, a self-consistent two-dimensional (2D) gliding arc model is developed, with a detailed non-equilibrium CO2 plasma chemistry, and validated with experiments. Our calculated values of the electron number density in the plasma, the CO2 conversion and energy efficiency show reasonable agreement with the experiments, indicating that the model can provide a realistic picture of the plasma chemistry. Comparison of the results with classical thermal conversion, as well as other plasma-based technologies for CO2 conversion reported in literature, demonstrates the non-equilibrium character of the gliding arc, and indicates that the gliding arc is a promising plasma reactor for CO2 conversion. However, some process modifications should be exploited to further improve its performance. As the model provides a realistic picture of the plasma behaviour, we use it first to investigate the plasma characteristics in a whole gliding arc cycle, which is necessary to understand the underlying mechanisms. Subsequently, we perform a chemical kinetics analysis, to investigate the different pathways for CO2 loss and formation. Based on the revealed discharge properties and the underlying CO2 plasma chemistry, the model allows us to propose solutions on how to further improve the CO2 conversion and energy efficiency by a gliding arc plasma. |
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| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Wos | 000414083300002 | Publication Date | 2017-07-22 | |
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 1385-8947 | ISBN | Additional Links | UA library record; WoS full record; WoS citing articles | |
| Impact Factor | 6.216 | Times cited | 38 | Open Access | OpenAccess |
| Notes | This research was supported by the European Marie Skłodowska- Curie Individual Fellowship “GlidArc” within Horizon 2020 (Grant No. 657304) and by the FWO project (grant G.0383.16N). The support of this experimental work by the EPSRC CO2Chem Seedcorn Grant and the FWO travel grant for study abroad (Grant K2.128.17N) is gratefully acknowledged. The calculations were performed using the Turing HPC infrastructure at the CalcUA core facility of the Universiteit Antwerpen (UAntwerpen), a division of the Flemish Supercomputer Center VSC, funded by the Hercules Foundation, the Flemish Government (department EWI) and the UAntwerpen. | Approved | Most recent IF: 6.216 | ||
| Call Number | PLASMANT @ plasmant @c:irua:145033 | Serial | 4636 | ||
| Permanent link to this record | |||||
| Author | Cai, Y.; Mei, D.; Chen, Y.; Bogaerts, A.; Tu, X. | ||||
| Title | Machine learning-driven optimization of plasma-catalytic dry reforming of methane | Type | A1 Journal Article | ||
| Year | 2024 | Publication | Journal of Energy Chemistry | Abbreviated Journal | Journal of Energy Chemistry |
| Volume |
96 | Issue | Pages | 153-163 | |
| Keywords | A1 Journal Article; Plasma catalysis Machine learning Process optimization Dry reforming of methane Syngas production; Plasma, laser ablation and surface modeling Antwerp (PLASMANT) ; | ||||
| 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. | ||||
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| Publisher | Place of Publication | Editor | |||
| Language | Wos | Publication Date | 2024-04-25 | ||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 2095-4956 | ISBN | Additional Links | ||
| Impact Factor | 13.1 | Times cited | Open Access | ||
| Notes | This project received funding from the European Union’s Hori- zon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No. 813393. | Approved | Most recent IF: 13.1; 2024 IF: 2.594 | ||
| Call Number | PLASMANT @ plasmant @ | Serial | 9124 | ||
| Permanent link to this record | |||||
| Author | Sun, S.R.; Wang, H.X.; Mei, D.H.; Tu, X.; Bogaerts, A. | ||||
| Title | CO2 conversion in a gliding arc plasma: Performance improvement based on chemical reaction modeling | Type | A1 Journal article | ||
| Year | 2017 | Publication | Journal of CO2 utilization | Abbreviated Journal | J Co2 Util |
| Volume |
17 | Issue | 17 | Pages | 220-234 |
| Keywords | A1 Journal article; Plasma Lab for Applications in Sustainability and Medicine – Antwerp (PLASMANT) | ||||
| Abstract | CO2 conversion into value-added chemicals is gaining increasing interest in recent years, and a gliding arc plasma has great potential for this purpose, because of its high energy efficiency. In this study, a chemical reaction kinetics model is presented to study the CO2 splitting in a gliding arc discharge. The calculated conversion and energy efficiency are in good agreement with experimental data in a range of different operating conditions. Therefore, this reaction kinetics model can be used to elucidate the dominant chemical reactions contributing to CO2 destruction and formation. Based on this reaction pathway analysis, the restricting factors for CO2 conversion are figured out, i.e., the reverse reactions and the small treated gas fraction. This allows us to propose some solutions in order to improve the CO2 conversion, such as decreasing the gas temperature, by using a high frequency discharge, or increasing the power density, by using a micro-scale gliding arc reactor, or by removing the reverse reactions, which could be realized in practice by adding possible scavengers for O atoms, such as CH4. Finally, we compare our results with other types of plasmas in terms of conversion and energy efficiency, and the results illustrate that gliding arc discharges are indeed quite promising for CO2 conversion, certainly when keeping in mind the possible solutions for further performance improvement. |
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| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Wos | 000393928500023 | Publication Date | 2016-12-28 | |
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 2212-9820 | ISBN | Additional Links | UA library record; WoS full record; WoS citing articles | |
| Impact Factor | 4.292 | Times cited | 41 | Open Access | Not_Open_Access |
| Notes | We acknowledge financial support from the IAP/7 (Inter- university Attraction Pole) program ‘PSI-Physical Chemistry of Plasma-Surface Interactions’ by the Belgian Federal Office for Science Policy (BELSPO) and the Fund for Scientific Research Flanders (FWO; Grant no. G.0383.16N). The calculations were carried out using the Turing HPC infrastructure at the CalcUA core facility of the Universiteit Antwerpen (UAntwerpen), a division of the Flemish Supercomputer Center VSC, funded by the Hercules Foundation, the Flemish Government (department EWI) and the UAntwerpen. This work is also supported by National Natural Science Foundation of China (grant nos. 11275021, 11575019). S R Sun thanks the financial support from the China Scholarship Council (CSC). | Approved | Most recent IF: 4.292 | ||
| Call Number | PLASMANT @ plasmant @ c:irua:138986 | Serial | 4332 | ||
| Permanent link to this record | |||||