| Home | << 1 >> |
|
“Machine learning-driven optimization of plasma-catalytic dry reforming of methane”. Cai Y, Mei D, Chen Y, Bogaerts A, Tu X, Journal of Energy Chemistry 96, 153 (2024). http://doi.org/10.1016/j.jechem.2024.04.022
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.
Keywords: A1 Journal Article; Plasma catalysis Machine learning Process optimization Dry reforming of methane Syngas production; Plasma, laser ablation and surface modeling Antwerp (PLASMANT) ;
Impact Factor: 13.1
DOI: 10.1016/j.jechem.2024.04.022
|
|
“Meta-analysis of CO₂, conversion, energy efficiency, and other performance data of plasma-catalysis reactors with the open access PIONEER database”. Salden A, Budde M, Garcia-Soto CA, Biondo O, Barauna J, Faedda M, Musig B, Fromentin C, Nguyen-Quang M, Philpott H, Hasrack G, Aceto D, Cai Y, Jury FA, Bogaerts A, Da Costa P, Engeln R, Galvez ME, Gans T, Garcia T, Guerra V, Henriques C, Motak M, Navarro MV, Parvulescu VI, Van Rooij G, Samojeden B, Sobota A, Tosi P, Tu X, Guaitella O, Journal of energy chemistry 86, 318 (2023). http://doi.org/10.1016/J.JECHEM.2023.07.022
Abstract: This paper brings the comparison of performances of CO2 conversion by plasma and plasma-assisted catalysis based on the data collected from literature in this field, organised in an open access online data-base. This tool is open to all users to carry out their own analyses, but also to contributors who wish to add their data to the database in order to improve the relevance of the comparisons made, and ultimately to improve the efficiency of CO2 conversion by plasma-catalysis. The creation of this database and data-base user interface is motivated by the fact that plasma-catalysis is a fast-growing field for all CO2 con-version processes, be it methanation, dry reforming of methane, methanolisation, or others. As a result of this rapid increase, there is a need for a set of standard procedures to rigorously compare performances of different systems. However, this is currently not possible because the fundamental mechanisms of plasma-catalysis are still too poorly understood to define these standard procedures. Fortunately how-ever, the accumulated data within the CO2 plasma-catalysis community has become large enough to war-rant so-called “big data” studies more familiar in the fields of medicine and the social sciences. To enable comparisons between multiple data sets and make future research more effective, this work proposes the first database on CO2 conversion performances by plasma-catalysis open to the whole community. This database has been initiated in the framework of a H2020 European project and is called the “PIONEER DataBase”. The database gathers a large amount of CO2 conversion performance data such as conversion rate, energy efficiency, and selectivity for numerous plasma sources coupled with or without a catalyst. Each data set is associated with metadata describing the gas mixture, the plasma source, the nature of the catalyst, and the form of coupling with the plasma. Beyond the database itself, a data extraction tool with direct visualisation features or advanced filtering functionalities has been developed and is available online to the public. The simple and fast visualisation of the state of the art puts new results into context, identifies literal gaps in data, and consequently points towards promising research routes. More advanced data extraction illustrates the impact that the database can have in the understanding of plasma-catalyst coupling. Lessons learned from the review of a large amount of literature during the setup of the database lead to best practice advice to increase comparability between future CO2 plasma-catalytic studies. Finally, the community is strongly encouraged to contribute to the database not only to increase the visibility of their data but also the relevance of the comparisons allowed by this tool. (c) 2023 Science Press and Dalian Institute of Chemical Physics, Chinese Academy of Sciences. Published by ELSEVIER B.V. and Science Press. This is an open access article under the CC BY license (http://creati- vecommons.org/licenses/by/4.0/).
Keywords: A1 Journal article; Engineering sciences. Technology; Plasma Lab for Applications in Sustainability and Medicine – Antwerp (PLASMANT)
Impact Factor: 13.1
DOI: 10.1016/J.JECHEM.2023.07.022
Additional Links: UA library record; WoS full record; WoS citing articles
|
|
“Engineering single crystalline Mn3O4 nano-octahedra with exposed highly active {011} facets for high performance lithium ion batteries”. Huang S-Z, Jin J, Cai Y, Li Y, Tan H-Y, Wang H-E, Van Tendeloo G, Su B-L, Nanoscale 6, 6819 (2014). http://doi.org/10.1039/c4nr01389a
Abstract: Well shaped single crystalline Mn3O4 nano-octahedra with exposed highly active {011} facets at different particle sizes have been synthesized and used as anode materials for lithium ion batteries. The electrochemical results show that the smallest sized Mn3O4 nano-octahedra show the best cycling performance with a high initial charge capacity of 907 mA h g−1 and a 50th charge capacity of 500 mA h g−1 at a current density of 50 mA g−1 and the best rate capability with a charge capacity of 350 mA h g−1 when cycled at 500 mA g−1. In particular, the nano-octahedra samples demonstrate a much better electrochemical performance in comparison with irregular shaped Mn3O4 nanoparticles. The best electrochemical properties of the smallest Mn3O4 nano-octahedra are ascribed to the lower charge transfer resistance due to the exposed highly active {011} facets, which can facilitate the conversion reaction of Mn3O4 and Li owing to the alternating Mn and O atom layers, resulting in easy formation and decomposition of the amorphous Li2O and the multi-electron reaction. On the other hand, the best electrochemical properties of the smallest Mn3O4 nano-octahedra can also be attributed to the smallest size resulting in the highest specific surface area, which provides maximum contact with the electrolyte and facilitates the rapid Li-ion diffusion at the electrode/electrolyte interface and fast lithium-ion transportation within the particles. The synergy of the exposed {011} facets and the smallest size (and/or the highest surface area) led to the best performance for the Mn3O4 nano-octahedra. Furthermore, HRTEM observations verify the oxidation of MnO to Mn3O4 during the charging process and confirm that the Mn3O4 octahedral structure can still be partly maintained after 50 dischargecharge cycles. The high Li-ion storage capacity and excellent cycling performance suggest that Mn3O4 nano-octahedra with exposed highly active {011} facets could be excellent anode materials for high-performance lithium-ion batteries.
Keywords: A1 Journal article; Engineering sciences. Technology; Electron microscopy for materials research (EMAT)
Impact Factor: 7.367
Times cited: 80
DOI: 10.1039/c4nr01389a
Additional Links: UA library record; WoS full record; WoS citing articles
|