Hen Ohayon Dahan, Gal Sror, Miron V. Landau, Eran Edri and Moti Herskowitz*,
{"title":"LSF移动晶格氧用于甲烷与CO2的选择性部分氧化","authors":"Hen Ohayon Dahan, Gal Sror, Miron V. Landau, Eran Edri and Moti Herskowitz*, ","doi":"10.1021/acsengineeringau.3c00008","DOIUrl":null,"url":null,"abstract":"<p >The effects of co-feeding CO<sub>2</sub> and methane on the performance of La<sub>0.8</sub>Sr<sub>0.2</sub>FeO<sub>3</sub> (LSF) were studied with different CO<sub>2</sub> concentrations. The reaction was conducted in chemical looping mode at 900 °C and a weight hourly space velocity (WHSV; g methane/g catalyst/h) of 3 h<sup>–1</sup> during 15 min reduction (10 mol % methane with 0–1.8% CO<sub>2</sub> in nitrogen) and 10 min oxidation (10 mol % oxygen in nitrogen) cycles. Analyses of X-ray diffraction and X-ray photoelectron spectroscopy data of spent materials indicated that CO<sub>2</sub> reacts with the oxygen vacancies on the LSF surface during methane reduction, increasing CO selectivity in POM. As the CO<sub>2</sub> feed concentration increased to an optimal value (1.6% CO<sub>2</sub>), the CO selectivity increased to 94%. Under those conditions, the EOR (extent of reduction) of LSF, defined as the amount of oxygen depleted from the lattice, was 0.18–0.15 mmol/min·g<sub>cat</sub>. Reducing the EOR to 0.09–0.08 mmol/min·g<sub>cat</sub> (1.8% CO<sub>2</sub>) led to partial methane combustion. These results were confirmed by altering the operating conditions (WHSV = 2 and 1 h<sup>–1</sup>, <i>T</i> = 950 °C) and CO<sub>2</sub> feed concentrations while extending the reduction time. Operation in an optimal EOR range (0.17–0.10 mmol/min·gcat) that enabled optimal CO selectivity (>90%) was obtained without oxidative regeneration for the 18 h reduction time.</p>","PeriodicalId":29804,"journal":{"name":"ACS Engineering Au","volume":null,"pages":null},"PeriodicalIF":4.3000,"publicationDate":"2023-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/epdf/10.1021/acsengineeringau.3c00008","citationCount":"1","resultStr":"{\"title\":\"Selective Partial Oxidation of Methane with CO2 Using Mobile Lattice Oxygens of LSF\",\"authors\":\"Hen Ohayon Dahan, Gal Sror, Miron V. Landau, Eran Edri and Moti Herskowitz*, \",\"doi\":\"10.1021/acsengineeringau.3c00008\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p >The effects of co-feeding CO<sub>2</sub> and methane on the performance of La<sub>0.8</sub>Sr<sub>0.2</sub>FeO<sub>3</sub> (LSF) were studied with different CO<sub>2</sub> concentrations. The reaction was conducted in chemical looping mode at 900 °C and a weight hourly space velocity (WHSV; g methane/g catalyst/h) of 3 h<sup>–1</sup> during 15 min reduction (10 mol % methane with 0–1.8% CO<sub>2</sub> in nitrogen) and 10 min oxidation (10 mol % oxygen in nitrogen) cycles. Analyses of X-ray diffraction and X-ray photoelectron spectroscopy data of spent materials indicated that CO<sub>2</sub> reacts with the oxygen vacancies on the LSF surface during methane reduction, increasing CO selectivity in POM. As the CO<sub>2</sub> feed concentration increased to an optimal value (1.6% CO<sub>2</sub>), the CO selectivity increased to 94%. Under those conditions, the EOR (extent of reduction) of LSF, defined as the amount of oxygen depleted from the lattice, was 0.18–0.15 mmol/min·g<sub>cat</sub>. Reducing the EOR to 0.09–0.08 mmol/min·g<sub>cat</sub> (1.8% CO<sub>2</sub>) led to partial methane combustion. These results were confirmed by altering the operating conditions (WHSV = 2 and 1 h<sup>–1</sup>, <i>T</i> = 950 °C) and CO<sub>2</sub> feed concentrations while extending the reduction time. Operation in an optimal EOR range (0.17–0.10 mmol/min·gcat) that enabled optimal CO selectivity (>90%) was obtained without oxidative regeneration for the 18 h reduction time.</p>\",\"PeriodicalId\":29804,\"journal\":{\"name\":\"ACS Engineering Au\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.3000,\"publicationDate\":\"2023-06-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://pubs.acs.org/doi/epdf/10.1021/acsengineeringau.3c00008\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACS Engineering Au\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://pubs.acs.org/doi/10.1021/acsengineeringau.3c00008\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, CHEMICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Engineering Au","FirstCategoryId":"1085","ListUrlMain":"https://pubs.acs.org/doi/10.1021/acsengineeringau.3c00008","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, CHEMICAL","Score":null,"Total":0}
Selective Partial Oxidation of Methane with CO2 Using Mobile Lattice Oxygens of LSF
The effects of co-feeding CO2 and methane on the performance of La0.8Sr0.2FeO3 (LSF) were studied with different CO2 concentrations. The reaction was conducted in chemical looping mode at 900 °C and a weight hourly space velocity (WHSV; g methane/g catalyst/h) of 3 h–1 during 15 min reduction (10 mol % methane with 0–1.8% CO2 in nitrogen) and 10 min oxidation (10 mol % oxygen in nitrogen) cycles. Analyses of X-ray diffraction and X-ray photoelectron spectroscopy data of spent materials indicated that CO2 reacts with the oxygen vacancies on the LSF surface during methane reduction, increasing CO selectivity in POM. As the CO2 feed concentration increased to an optimal value (1.6% CO2), the CO selectivity increased to 94%. Under those conditions, the EOR (extent of reduction) of LSF, defined as the amount of oxygen depleted from the lattice, was 0.18–0.15 mmol/min·gcat. Reducing the EOR to 0.09–0.08 mmol/min·gcat (1.8% CO2) led to partial methane combustion. These results were confirmed by altering the operating conditions (WHSV = 2 and 1 h–1, T = 950 °C) and CO2 feed concentrations while extending the reduction time. Operation in an optimal EOR range (0.17–0.10 mmol/min·gcat) that enabled optimal CO selectivity (>90%) was obtained without oxidative regeneration for the 18 h reduction time.
期刊介绍:
)ACS Engineering Au is an open access journal that reports significant advances in chemical engineering applied chemistry and energy covering fundamentals processes and products. The journal's broad scope includes experimental theoretical mathematical computational chemical and physical research from academic and industrial settings. Short letters comprehensive articles reviews and perspectives are welcome on topics that include:Fundamental research in such areas as thermodynamics transport phenomena (flow mixing mass & heat transfer) chemical reaction kinetics and engineering catalysis separations interfacial phenomena and materialsProcess design development and intensification (e.g. process technologies for chemicals and materials synthesis and design methods process intensification multiphase reactors scale-up systems analysis process control data correlation schemes modeling machine learning Artificial Intelligence)Product research and development involving chemical and engineering aspects (e.g. catalysts plastics elastomers fibers adhesives coatings paper membranes lubricants ceramics aerosols fluidic devices intensified process equipment)Energy and fuels (e.g. pre-treatment processing and utilization of renewable energy resources; processing and utilization of fuels; properties and structure or molecular composition of both raw fuels and refined products; fuel cells hydrogen batteries; photochemical fuel and energy production; decarbonization; electrification; microwave; cavitation)Measurement techniques computational models and data on thermo-physical thermodynamic and transport properties of materials and phase equilibrium behaviorNew methods models and tools (e.g. real-time data analytics multi-scale models physics informed machine learning models machine learning enhanced physics-based models soft sensors high-performance computing)