Speciation data for fuel-rich methane oxy-combustion and reforming under prototypical partial oxidation conditions

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• 作者：Markus Kö ; hler^a ; ^{m.koehler@dlr.de" class="auth_mail" title="E-mail the corresponding author} ; Patrick Oß ; wald^a ; Hongbin Xu^b ; Trupti Kathrotia^a ; Christian Hasse^b ; Uwe Riedel^a
• 关键词：Gasification ; Rich combustion ; Partial oxidation ; Chemical modeling ; Molecular-beam mass spectrometry (MBMS) ; Flow reactor
• 刊名：Chemical Engineering Science
• 出版年：2016
• 出版时间：12 January 2016
• 年：2016
• 卷：139
• 期：Complete
• 页码：249-260
• 全文大小：1362 K

文摘

Non-catalytic partial oxidation (POX) of hydrocarbon fuels is an important process for producing syngas. Quantitative experimental data under the demanding conditions relevant for POX reactions, e.g. long residence times, rich stoichiometries and high temperatures, respectively, are rare in literature. Here, the DLR high-temperature flow reactor setup was used to obtain a unique experimental data set for validation of reaction models and general understanding of fuel-rich hydrocarbon chemistry. A systematic experimental speciation data set for rich methane conditions with relevance to partial oxidation/gasification processes is presented. Both fast oxidation and slow reforming reactions are considered here. Quantitative data is obtained in the DLR high temperature flow reactor setup with coupled molecular beam mass spectrometry (MBMS) detection. Five test case scenarios are investigated, featuring rich methane conditions (ϕ=2.5) for the temperature range from 1100–1800 K under atmospheric conditions. CO, CO₂ and acetylene in two different amounts is added to the system for systematic analysis for addressing phenomena related to partial oxidation. The new experimental database includes quantitative species profiles of major and intermediate species and is available as Supplemental material. The experimental data is compared with results from a 0D modeling approach using the GRI 3.0, USC-II, Chernov and a reduced model based on the full Chernov mechanism. The comparisons reveal significant differences in the model predictions among themselves and with respect to the experimental data, underlining the relevance of this unique data set for further mechanism development and/or optimization.