Pioneering the future: A trailblazing review of the fusion of computational fluid dynamics and machine learning revolutionizing plasma catalysis and non-thermal plasma reactor design
dc.contributor.author | Arshad, Muhammad Yousaf | |
dc.contributor.author | Ahmad, Anum Suhail | |
dc.contributor.author | Mularski, Jakub | |
dc.contributor.author | Modzelewska, Aleksandra | |
dc.contributor.author | Jackowski, Mateusz | |
dc.contributor.author | Pawlak-Kruczek, Halina | |
dc.contributor.author | Niedźwiecki, Łukasz | |
dc.date.accessioned | 2024-10-14T10:25:37Z | |
dc.date.available | 2024-10-14T10:25:37Z | |
dc.date.issued | 2024 | |
dc.identifier.citation | Catalysts. 2024, vol. 14, issue 1, art. no. 40. | cs |
dc.identifier.issn | 2073-4344 | |
dc.identifier.uri | http://hdl.handle.net/10084/155148 | |
dc.description.abstract | The advancement of plasma technology is intricately linked with the utilization of computational fluid dynamics (CFD) models, which play a pivotal role in the design and optimization of industrial-scale plasma reactors. This comprehensive compilation encapsulates the evolving landscape of plasma reactor design, encompassing fluid dynamics, chemical kinetics, heat transfer, and radiation energy. By employing diverse tools such as FLUENT, Python, MATLAB, and Abaqus, CFD techniques unravel the complexities of turbulence, multiphase flow, and species transport. The spectrum of plasma behavior equations, including ion and electron densities, electric fields, and recombination reactions, is presented in a holistic manner. The modeling of non-thermal plasma reactors, underpinned by precise mathematical formulations and computational strategies, is further empowered by the integration of machine learning algorithms for predictive modeling and optimization. From biomass gasification to intricate chemical reactions, this work underscores the versatile potential of plasma hybrid modeling in reshaping various industrial processes. Within the sphere of plasma catalysis, modeling and simulation methodologies have paved the way for transformative progress. Encompassing reactor configurations, kinetic pathways, hydrogen production, waste valorization, and beyond, this compilation offers a panoramic view of the multifaceted dimensions of plasma catalysis. Microkinetic modeling and catalyst design emerge as focal points for optimizing CO2 conversion, while the intricate interplay between plasma and catalysts illuminates insights into ammonia synthesis, methane reforming, and hydrocarbon conversion. Leveraging neural networks and advanced modeling techniques enables predictive prowess in the optimization of plasma-catalytic processes. The integration of plasma and catalysts for diverse applications, from waste valorization to syngas production and direct CO2/CH4 conversion, exemplifies the wide-reaching potential of plasma catalysis in sustainable practices. Ultimately, this anthology underscores the transformative influence of modeling and simulation in shaping the forefront of plasma-catalytic processes, fostering innovation and sustainable applications. | cs |
dc.language.iso | en | cs |
dc.publisher | MDPI | cs |
dc.relation.ispartofseries | Catalysts | cs |
dc.relation.uri | https://doi.org/10.3390/catal14010040 | cs |
dc.rights | © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. | cs |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | cs |
dc.subject | plasma | cs |
dc.subject | non-thermal plasma reactors | cs |
dc.subject | plasma catalysis | cs |
dc.title | Pioneering the future: A trailblazing review of the fusion of computational fluid dynamics and machine learning revolutionizing plasma catalysis and non-thermal plasma reactor design | cs |
dc.type | article | cs |
dc.identifier.doi | 10.3390/catal14010040 | |
dc.rights.access | openAccess | cs |
dc.type.version | publishedVersion | cs |
dc.type.status | Peer-reviewed | cs |
dc.description.source | Web of Science | cs |
dc.description.volume | 14 | cs |
dc.description.issue | 1 | cs |
dc.description.firstpage | art. no. 40 | cs |
dc.identifier.wos | 001149280800001 |
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