Numerical Study of Dry Reforming of Methane in Packed and Fluidized Beds: Effects of Key Operating Parameters

Al-Otaibi, Fahad; Xiao, Hongliang; Berrouk, Abdallah S.; Polychronopoulou, Kyriaki

doi:10.3390/chemengineering7030057

Cited by 12 publications

(4 citation statements)

References 61 publications

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“…The study of Alotaibi et al involved investigating influential key operating parameters and conditions of the DRM process hosted in different reactor settings (Figure ) by utilizing the MP-PIC approach, simulating its performance, and generating an ample amount of validated data. , Contrary to conducting expensive sensitivity analysis experiments with time- and resource-intensive requirements, a thoroughly validated CFD was the preferred option, as stipulated in the Alotaibi et al results. While CFD is a crucial tool for comprehending complex multiphase behavior, machine learning (ML) takes advantage of the additional efficiency in computational time, bridging complex relationships among the parameters and enabling multiobjective optimization analysis.…”

Section: Methodology and Data Generation Strategymentioning

confidence: 99%

“…In this paper, the data set provided by Alotaibi et al , will be used to build a suitable ML algorithm to predict the DRM performance considering the underlying parametric studies, and based on a specific criterion, the algorithm will determine the optimum operating conditions by employing a predetermined optimization strategy. Both data sets were generated by Barracuda software version 21.0 software…”

Section: Methodology and Data Generation Strategymentioning

confidence: 99%

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Optimization of Yield and Conversion Rates in Methane Dry Reforming Using Artificial Neural Networks and the Multiobjective Genetic Algorithm

Alotaibi,

Berrouk,

Saeed

2023

Ind. Eng. Chem. Res.

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This research aimed to enhance the dry reforming of methane by integrating computational fluid dynamics (CFD), artificial neural network (ANN), and multiobjective genetic algorithm (MOGA) techniques. Through a comprehensive analysis of reactor setups using computational fluid dynamics, reliable data were generated. Machine learning models based on the ANN were then trained with this data to establish connections between the yield, the conversion rates, the flow rates, the carbon content, and the input parameters. The trained ANN models were subsequently employed as an objective function for the MOGA, enabling the identification of optimized input parameter values that maximize the conversion rates, yields, and flow rates and minimize the carbon content. The optimization results from the MOGA revealed that in the low flow rate regime, the optimal input parameters for the reactor performance fell within a U g range of 0.08−1.0 m/ s with corresponding h/H values of 0.37 and 0.48. In the high flow rate regime, the Pareto front analysis unveiled that the optimal U g values ranged from 1.5 to 1.6 m/s accompanied by corresponding h/H values ranging from 0.5 to 0.8. Furthermore, the study identified temperature values between 814 and 817 m/s as the optimal range for achieving a balanced compromise between the coke content and the conversion rates/yields. Overall, this research provides valuable insights into optimizing the dry reforming process, highlighting the effectiveness of the ANN models, the significance of specialized methods for optimization, and the relationship between the input parameters and the performance indicators. These findings contribute to the development of more efficient and productive reactors for the dry reforming of methane.

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Section: Methodology and Data Generation Strategymentioning

confidence: 99%

Section: Methodology and Data Generation Strategymentioning

confidence: 99%

Optimization of Yield and Conversion Rates in Methane Dry Reforming Using Artificial Neural Networks and the Multiobjective Genetic Algorithm

Alotaibi,

Berrouk,

Saeed

2023

Ind. Eng. Chem. Res.

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“…Non-isothermicity is also a problem for conventional packed-bed steam-reforming reactors, due to heat-exchange limitations [256,257], and PO, DR and the dual-reforming process can be expected to suffer from similar problems. While fluidized beds can offer a solution in terms of heat-and mass-transfer optimization [258], they also add complexity to reaction operation and catalyst selection compared to fixed-bed reactors, as factors such as the control of a correct fluidization regime and attrition resistance of catalyst and reactor components have to be considered [259,260]. Therefore, improvement in the performance of fixed-bed reactors is particularly interesting for future developments.…”

Section: Catalyst Patterningmentioning

confidence: 99%

New Perspectives on Catalytic Hydrogen Production by the Reforming, Partial Oxidation and Decomposition of Methane and Biogas

Boscherini,

Storione,

Minelli

et al. 2023

Energies

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The article provides a short review on catalyst-based processes for the production of hydrogen starting from methane, both of fossil origin and from sustainable processes. The three main paths of steam- and dry-reforming, partial oxidation and thermo-catalytic decomposition are briefly introduced and compared, above all with reference to the latest publications available and to new catalysts which obey the criteria of lower environmental impact and minimize the content of critical raw materials. The novel strategies based on chemical looping with CO2 utilization, membrane separation, electrical-assisted (plasma and microwave) processes, multistage reactors and catalyst patterning are also illustrated as the most promising perspective for CH4 reforming, especially on small and medium scale. Although these strategies should only be considered at a limited level of technological readiness, research on these topics, including catalyst development and process optimization, represents the crucial challenge for the scientific community.

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“…Dry reforming of methane (DRM) that utilizes two major greenhouse gases as reactants, CH 4 and CO 2 , is one of the promising approaches toward carbon neutrality [1,2]. A mixture of H 2 and CO obtained as products from this reaction can be used as a feedstock for synthesizing various value-added oxygenated chemicals and long-chain hydrocarbons through Fischer-Tropsch reactions [3].…”

Section: Introductionmentioning

confidence: 99%

Enhanced Methane Dry Reforming with Ni/SiO2 Catalysts Featuring Hierarchical External Nanostructures

Kim,

Kim

et al. 2024

Catalysts

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Global energy demand escalates the interest in effective and durable catalytic systems for the dry reforming of methane (DRM), a process that converts CO2/CH4 into H2/CO syngas. Porous silica-supported nickel (Ni) catalysts are recognized as a promising candidate due to robust DRM activity associated with the confinement of Ni particles in the mesopores that reduces the catalyst deactivation by carbon byproduct deposits and sintering of active Ni sites. However, the small-sized pore configurations in the mesoporous catalysts hinders the fast mass transfer of reactants and products. A unique combination of the hierarchical nanostructure with macro–mesoporous features of the support is adopted to enhance the catalytic performance via the dual effect of the efficient mass transfer and minimized sintering issue. This study delves into the influence of SiO2 geometry and pore structure on the catalytic performance of Ni-based catalysts. Three types of porous silica supports were synthesized through various methods: (a) hydrothermal-assisted sol–gel for dendritic mesoporous silica (DMS), (b) spray-pyrolysis-assisted sol–gel for spray evaporation-induced self-assembly (EISA) silica, and (c) oven-assisted sol–gel for oven EISA silica. Among the prepared catalysts the hierarchical external nanostructured Ni/DMS showed the superior CH4 and CO2 conversion rates (76.6% and 82.1%), even at high space velocities (GHSV = 360 L∙g−1·h−1). The distinctive macro–mesoporous geometry effectively prevents the sintering of Ni particles and promotes the smooth diffusion of the reactants and products, thus improving catalytic stability over extended reaction periods (24 h). This research highlights the significant impact of macro–mesoporosity revealed in DMS support catalysts on the physicochemical properties of Ni/DMS and their crucial role in enhancing DRM reaction efficiency.

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Numerical Study of Dry Reforming of Methane in Packed and Fluidized Beds: Effects of Key Operating Parameters

Cited by 12 publications

References 61 publications

Optimization of Yield and Conversion Rates in Methane Dry Reforming Using Artificial Neural Networks and the Multiobjective Genetic Algorithm

Optimization of Yield and Conversion Rates in Methane Dry Reforming Using Artificial Neural Networks and the Multiobjective Genetic Algorithm

New Perspectives on Catalytic Hydrogen Production by the Reforming, Partial Oxidation and Decomposition of Methane and Biogas

Enhanced Methane Dry Reforming with Ni/SiO2 Catalysts Featuring Hierarchical External Nanostructures

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