NOx and Hydrocarbon Trapping and Conversion in a Sequential Three-Zone Monolith: Spatiotemporal Features

IF 4.3 Q2 ENGINEERING, CHEMICAL ACS Engineering Au Pub Date : 2022-07-08 DOI:10.1021/acsengineeringau.2c00023
Abhay Gupta, Mugdha Ambast and Michael P. Harold*, 
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引用次数: 2

Abstract

The spatiotemporal features of the multifunctional monolithic lean hydrocarbon NOx trap (LHCNT), for eliminating NOx (x = 1 and 2) and ethylene (C2H4), are examined using spatially resolved mass spectrometry (SpaciMS), spanning the sequentially positioned passive NOx adsorber (PNA; Pd/SSZ-13), hydrocarbon trap (HCT; Pd/BEA), and oxidation catalyst (OC; Pt/Al2O3–CeO2). The overall LHCNT performance is captured in temporal trapping efficiency profiles, which show the integral NO and C2H4 uptake followed by delayed NO release along with NO and ethylene oxidation. Spatially resolved transient concentration profiles spanning uptake, release, and conversion of NO, H2, and C2H4, alone or as mixtures in feeds containing H2O, provide detailed insight into the transient coupling not attainable with effluent concentration monitoring alone. The PNA serves as the primary zone for NO uptake, followed by the OC and HCT. NO oxidation to NO2 occurs during NO uptake in the PNA due to Pd(II) reduction, while more extensive oxidation occurs in the OC at higher temperature. C2H4 uptake and oxidation occur in each of the functions with oxidation occurring the earliest (lowest temperature) in the OC. NO uptake in the PNA and HCT is negligibly affected by H2 but protracted oxidation of H2 during the temperature ramp delays NO release, suggesting persistence of NO bound on Pd(I). Both the PNA and HCT exhibit excellent C2H4 uptake, which diminishes in the presence of NO. Spatially resolved concentration data reveal several interesting features, such as high-temperature, sequential NO oxidation (by O2 to NO2) and C2H4 oxidation (by NO2 to NO + CO2) in the PNA. Simulated warmup experiments reveal that the LHCNT NO trapping is enhanced with C2H4 addition but that a reduction in space velocity may be needed to improve performance. A previously developed PNA model predicts satisfactorily the main features of spatially resolved NO and NO + C2H4 data.

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氮氧化物和碳氢化合物的捕获和转化在一个连续的三区整体:时空特征
利用空间分辨质谱(SpaciMS)研究了用于去除NOx (x = 1和2)和乙烯(C2H4)的多功能单片贫碳氢化合物NOx捕集器(LHCNT)的时空特征,该捕集器跨越顺序定位的被动NOx吸附器(PNA;Pd/SSZ-13)、油气圈闭(HCT;Pd/BEA)和氧化催化剂(OC;Pt / Al2O3-CeO2)。LHCNT的整体性能表现在时间捕获效率曲线中,该曲线显示了NO和C2H4的整体吸收,然后是延迟的NO释放以及NO和乙烯氧化。空间分辨的瞬态浓度曲线跨越了NO、H2和C2H4单独或作为含有H2O的饲料中的混合物的吸收、释放和转化,提供了对瞬态耦合的详细了解,这是单独通过出水浓度监测无法实现的。PNA是NO摄取的主要区域,其次是OC和HCT。由于Pd(II)的还原,PNA在NO摄取过程中发生NO氧化为NO2,而OC在较高温度下发生更广泛的氧化。C2H4的摄取和氧化发生在每个功能中,氧化发生的最早(最低温度)在OC中。PNA和HCT中的NO摄取受H2的影响可以忽略不计,但在温度斜坡过程中H2的持续氧化延迟了NO的释放,这表明NO结合在Pd(I)上的持久性。PNA和HCT均表现出良好的C2H4摄取,在NO存在下C2H4摄取减少。空间分辨的浓度数据揭示了PNA中一些有趣的特征,如高温、连续NO氧化(由O2氧化为NO2)和C2H4氧化(由NO2氧化为NO + CO2)。模拟预热实验表明,添加C2H4可以增强LHCNT的NO捕获,但可能需要降低空间速度来提高性能。先前开发的PNA模型令人满意地预测了空间分辨NO和NO + C2H4数据的主要特征。
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ACS Engineering Au
ACS Engineering Au 化学工程技术-
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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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