Optimal design of methane tri-reforming reactor to produce proper syngas for Fischer-Tropsch and methanol synthesis processes: A comparative analysis between different side-feeding strategies

Khademi, Mohammad Hasan; Alipour-Dehkordi, Afshar; Tabesh, Massoud

doi:10.1016/j.ijhydene.2021.01.215

Cited by 37 publications

(11 citation statements)

References 43 publications

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“…70,71 Likewise, H 2 /CO ratio for methanol synthesis should also be set around two (1.7-2.3). 70,72 However, the synthesis of methane by the methanation reaction needs a higher H 2 /CO ratio. 70 The stoichiometric H 2 /CO ratio is three for the CO methanation reaction, and it has been shown that high H 2 /CO ratios improve methanation activity.…”

Section: Ranking Biomasses For Different Applicationsmentioning

confidence: 99%

Biomass to energy: a machine learning model for optimum gasification pathways

et al. 2023

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Section: Ranking Biomasses For Different Applicationsmentioning

confidence: 99%

Biomass to energy: a machine learning model for optimum gasification pathways

et al. 2023

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“…49,50 Likewise, H 2 /CO ratio for methanol synthesis should also be set around two (1.7-2.3). 49,51 However, the synthesis of methane by the methanation reaction needs a higher H 2 /CO ratio. 49 The stoichiometric H 2 /CO ratio is three for the CO methanation reaction, and it has been shown that high H 2 /CO ratios improve methanation activity.…”

Section: Ranking Biomasses For Different Applicationsmentioning

confidence: 99%

Biomass to energy: A machine learning model for optimum gasification pathways

Gil

Jablonka

García³

et al. 2023

Preprint

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Biomass is a highly versatile renewable resource for decarbonizing energy systems. Gasification is a promising conversion technology that can transform biomass into multiple energy carriers to produce heat, electricity, biofuels, or chemicals. At present, identifying the best gasification route for a given biomass relies on trial and error, time-consuming experimentation that, given the wide range of biomass feedstocks available, slows down the deployment of the technology. Here we develop a supervised machine-learning model to find the optimal application of a particular biomass in gasification processes. Our model can select the suitable gasification pathway from the characteristics of the biomass, and also identify the optimal operating conditions for a selected application of the produced gas. In addition, with this model, we can obtain insights into the relationships between biomass properties and gasification results, leading to a better understanding of the process. A relevant aspect of this work is that these results rely on a relatively small dataset, representative of those typically collected by research groups using different types of gasifiers worldwide. This study opens the path for future integration of such data, which would allow addressing the complexity of biomass and conversion process simultaneously. With this work, we aim to increase the flexibility of biomass gasification processes and promote the development of bioenergy technologies, considered crucial in the energy transition context.

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“…Borreguero et al [4] assessed the economic feasibility of methanol synthesis from natural tri-reforming based syngas. Khademi et al [13] also applied optimization techniques to investigate the tri-reforming of methane to produce a suitable syngas ratio for the synthesis of methanol and FT fuels with an objective function of minimizing the tri-reformer length. Hernandez and Martin [14] proposed a non-linear programming (NLP) optimization approach for syngas production from biogas by analyzing three syngas ratios (methanol synthesis, H 2 :CO = 2.5:1, ethanol synthesis, H 2 :CO= 1:1 and FT fuels, H 2 :CO = 1.7:1).…”

Section: Introductionmentioning

confidence: 99%

Developing a Targeting Approach for Syngas Generation from Natural Gas

Patel

Mukeru

2023

Period. Polytech. Chem. Eng.

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The conversion of natural and unconventional gas into syngas is a crucial intermediate step in the production of various important chemicals and liquid fuels. The syngas generation step usually requires the largest capital investment of the process and may also be very energy intensive. Therefore, determining the most efficient method of converting feedstock into syngas of the correct H2:CO ratio is of significant importance. The aim of this work was to set design and performance targets for different H2:CO ratios (depending on the downstream requirements) in terms of the carbon efficiency (including CO2 utilization or emissions), water usage, and energy requirements. It was shown that the overall process for natural gas tri-reforming is limited by the enthalpy change (ΔH = 0) and this process was able to produce work. It was further shown that high syngas ratios not only require significant amounts of natural gas and oxygen but also emit CO2.

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Optimal design of methane tri-reforming reactor to produce proper syngas for Fischer-Tropsch and methanol synthesis processes: A comparative analysis between different side-feeding strategies

Cited by 37 publications

References 43 publications

Biomass to energy: a machine learning model for optimum gasification pathways

Biomass to energy: a machine learning model for optimum gasification pathways

Biomass to energy: A machine learning model for optimum gasification pathways

Developing a Targeting Approach for Syngas Generation from Natural Gas

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