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dc.contributor.authorArshad, Muhammad Yousaf
dc.contributor.authorAhmad, Anum Suhail
dc.contributor.authorMularski, Jakub
dc.contributor.authorModzelewska, Aleksandra
dc.contributor.authorJackowski, Mateusz
dc.contributor.authorPawlak-Kruczek, Halina
dc.contributor.authorNiedźwiecki, Łukasz
dc.date.accessioned2024-10-14T10:25:37Z
dc.date.available2024-10-14T10:25:37Z
dc.date.issued2024
dc.identifier.citationCatalysts. 2024, vol. 14, issue 1, art. no. 40.cs
dc.identifier.issn2073-4344
dc.identifier.urihttp://hdl.handle.net/10084/155148
dc.description.abstractThe 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.isoencs
dc.publisherMDPIcs
dc.relation.ispartofseriesCatalystscs
dc.relation.urihttps://doi.org/10.3390/catal14010040cs
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.urihttp://creativecommons.org/licenses/by/4.0/cs
dc.subjectplasmacs
dc.subjectnon-thermal plasma reactorscs
dc.subjectplasma catalysiscs
dc.titlePioneering the future: A trailblazing review of the fusion of computational fluid dynamics and machine learning revolutionizing plasma catalysis and non-thermal plasma reactor designcs
dc.typearticlecs
dc.identifier.doi10.3390/catal14010040
dc.rights.accessopenAccesscs
dc.type.versionpublishedVersioncs
dc.type.statusPeer-reviewedcs
dc.description.sourceWeb of Sciencecs
dc.description.volume14cs
dc.description.issue1cs
dc.description.firstpageart. no. 40cs
dc.identifier.wos001149280800001


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© 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.
Kromě případů, kde je uvedeno jinak, licence tohoto záznamu je © 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.