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

IF 3.8 3区 化学 Q2 CHEMISTRY, PHYSICAL Catalysts Pub Date : 2024-01-06 DOI:10.3390/catal14010040
Muhammad Yousaf Arshad, Anam Suhail Ahmad, Jakub Mularski, Aleksandra Modzelewska, M. Jackowski, H. Pawlak-Kruczek, Lukasz Niedzwiecki
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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.
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开拓未来:计算流体力学与机器学习融合的开拓性回顾:等离子体催化和非热等离子体反应器设计的革命性变革
等离子体技术的发展与计算流体动力学(CFD)模型的利用密切相关,CFD 模型在工业规模等离子体反应器的设计和优化中发挥着关键作用。这本内容全面的汇编囊括了等离子体反应器设计的演变过程,包括流体动力学、化学动力学、热传递和辐射能。通过使用 FLUENT、Python、MATLAB 和 Abaqus 等多种工具,CFD 技术揭示了湍流、多相流和物种传输的复杂性。等离子体行为方程的频谱,包括离子和电子密度、电场和重组反应,都以整体的方式呈现。非热等离子体反应器的建模以精确的数学公式和计算策略为基础,通过整合机器学习算法进行预测建模和优化,进一步增强了建模能力。从生物质气化到复杂的化学反应,这项工作凸显了等离子体混合建模在重塑各种工业流程方面的多功能潜力。在等离子体催化领域,建模和模拟方法为取得变革性进展铺平了道路。本汇编涵盖了反应器配置、动力学途径、氢气生产、废物资源化等内容,为等离子体催化的多面性提供了一个全景视角。微动力学建模和催化剂设计是优化二氧化碳转化的重点,而等离子体和催化剂之间错综复杂的相互作用则为氨合成、甲烷转化和碳氢化合物转化提供了启示。利用神经网络和先进的建模技术,可以对等离子体催化过程的优化进行预测。将等离子体和催化剂整合在一起,应用于从废物价值化到合成气生产和二氧化碳/CH4 直接转化等多种领域,体现了等离子体催化在可持续发展实践中的广泛潜力。最终,这本选集强调了建模和模拟在塑造等离子体催化过程前沿、促进创新和可持续应用方面的变革性影响。
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来源期刊
Catalysts
Catalysts CHEMISTRY, PHYSICAL-
CiteScore
6.80
自引率
7.70%
发文量
1330
审稿时长
3 months
期刊介绍: Catalysts (ISSN 2073-4344) is an international open access journal of catalysts and catalyzed reactions. Catalysts publishes reviews, regular research papers (articles) and short communications. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible. Therefore, there is no restriction on the length of the papers. The full experimental details must be provided so that the results can be reproduced.
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