{"title":"Effective 1D Model Reflecting the Impact of Deposited Soot on Coupled Reaction and Transport Inside the Catalytic Filter Wall","authors":"Jan Němec, and , Petr Kočí*, ","doi":"10.1021/acsengineeringau.5c00024","DOIUrl":null,"url":null,"abstract":"<p >This paper proposes an extension of the 1D+1D model for a catalytic monolith filter used in exhaust gas aftertreatment, which allows for the prediction of the effects that soot deposits formed inside the filter wall can have on the catalytic conversion of exhaust gas components. The soot deposits act as an additional barrier between the flowing gas and catalytic sites. The extended model considers three characteristic lengths for diffusion: (i) through the soot deposits, (ii) through the remaining free pores, and (iii) through the catalytic coating. The diffusion resistance of each part is considered based on the corresponding characteristic length and local effective diffusivity. The simulations predict no influence of soot on the reaction onset but an increased slip of unreacted gas above the light-off temperature, particularly at higher flow rates. The predicted trends are consistent with the observations reported in the literature.</p>","PeriodicalId":29804,"journal":{"name":"ACS Engineering Au","volume":"5 4","pages":"416–424"},"PeriodicalIF":5.1000,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/pdf/10.1021/acsengineeringau.5c00024","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Engineering Au","FirstCategoryId":"1085","ListUrlMain":"https://pubs.acs.org/doi/10.1021/acsengineeringau.5c00024","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, CHEMICAL","Score":null,"Total":0}
引用次数: 0
Abstract
This paper proposes an extension of the 1D+1D model for a catalytic monolith filter used in exhaust gas aftertreatment, which allows for the prediction of the effects that soot deposits formed inside the filter wall can have on the catalytic conversion of exhaust gas components. The soot deposits act as an additional barrier between the flowing gas and catalytic sites. The extended model considers three characteristic lengths for diffusion: (i) through the soot deposits, (ii) through the remaining free pores, and (iii) through the catalytic coating. The diffusion resistance of each part is considered based on the corresponding characteristic length and local effective diffusivity. The simulations predict no influence of soot on the reaction onset but an increased slip of unreacted gas above the light-off temperature, particularly at higher flow rates. The predicted trends are consistent with the observations reported in the literature.
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)
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