Heat Recuperation from Internal Combustion Engines by Fuel Reforming: Kinetics-Based Analysis

IF 4.3 Q2 ENGINEERING, CHEMICAL ACS Engineering Au Pub Date : 2023-05-14 DOI:10.1021/acsengineeringau.3c00007
Moshe Sheintuch*, Olga Nekhamkina and Leonid Tartakovsky, 
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Abstract

In an effort to estimate the feasibility of heat recuperation from an internal combustion engine (ICE) by steam reforming (SR) or by decomposition of the fuel, we study here the required size of a reformer heat exchanger in order to power a 3.7 kW engine. To that end, we experimentally test the heat transfer in a structured commercial reactor with ∼0.39 m2 of heat transfer area in an ∼1 L unit. We then simulate the required length for evaporation and reforming of several fuels, using published kinetics with a highly active catalyst, under a fixed exhaust temperature of 973 K, and study the effect of pressure and steam-to-fuel ratio. Both co- and counter-current schemes are considered. Methanol decomposition is probably the best solution from the energy point of view. However, it is known to lead to deactivation. Methanol SR (with S/M = 1) requires about 2 L of reformer-HE and seems to be a reasonable solution, yielding a chemical energy gain of ∼16%, a value close to the asymptotic thermodynamic value. Moreover, the presence of CO2 in the reformate is known to mitigate to NOx emissions down to zero-impact levels. Ethanol SR (with S/E = 1 or 3) yields poor results since CH4 is an intermediate, which requires high temperatures for reforming; operating ESR requires exhaust temperatures of ∼1250 °K or higher. While such high temperatures may be attained and may yield an energetic gain of more than 20%, it will require modification of the process. Methylal SR (S/MA = 1) yields good results as well.

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燃料重整的内燃机热回收:基于动力学的分析
为了估计通过蒸汽重整(SR)或燃料分解从内燃机(ICE)中回收热量的可行性,我们在这里研究了为3.7 kW发动机提供动力所需的重整热交换器尺寸。为此,我们在一个结构化的商业反应器中实验测试了传热,传热面积为~ 0.39 m2,单位为~ 1 L。在973 K的固定排气温度下,我们利用已发表的动力学和高活性催化剂模拟了几种燃料蒸发和重整所需的长度,并研究了压力和蒸汽燃料比的影响。同时考虑了共电流和逆流方案。从能源的角度来看,甲醇分解可能是最好的解决方案。然而,已知它会导致失活。甲醇SR (S/M = 1)需要约2l的重整器he,这似乎是一个合理的解决方案,产生约16%的化学能增益,接近渐近热力学值。此外,已知重整物中二氧化碳的存在可以将氮氧化物排放降低到零影响水平。乙醇SR (S/E = 1或3)的结果很差,因为CH4是中间体,需要高温才能转化;运行ESR要求排气温度为~ 1250°K或更高。虽然可以达到这样的高温,并且可以产生超过20%的能量增益,但这需要对工艺进行修改。甲缩醛SR (S/MA = 1)也有很好的效果。
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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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