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An Investigation on the Regeneration of Lean NOx Trap Using Ethanol and n-Butanol
Technical Paper
2019-01-0737
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
Reduction of nitrogen oxides (NOx) in lean burn and diesel fueled Compression Ignition (CI) engines is one of the major challenges faced by automotive manufacturers. Lean NOx Trap (LNT) and urea-based Selective Catalytic Reduction (SCR) exhaust after-treatment systems are well established technologies to reduce NOx emissions. However, each of these technologies has associated advantages and disadvantages for use over a wide range of engine operating conditions. In order to meet future ultra-low NOx emission norms, the use of both alternative fuels and advanced after-treatment technology may be required. The use of an alcohol fuel such as n-butanol or ethanol in a CI engine can reduce the engine-out NOx and soot emissions. In CI engines using LNTs for NOx reduction, the fuel such as diesel is utilized as a reductant for LNT regeneration. In the present work, a detailed evaluation of the performance of long breathing LNT (requiring fewer regenerations than conventional LNT) is carried out using ethanol and n-butanol as the reductants and are compared with diesel as the reductant. For this purpose, a long breathing LNT catalyst is examined on a flow bench under simulated exhaust conditions. The NOx adsorption period is decoupled from regeneration, and reductant quantities are varied at 3% and 8.5% oxygen concentration. Ethanol and n-butanol are found to be more effective as reductants compared to diesel in terms of NOx conversion and hydrogen yield during the LNT regeneration at the tested conditions. In order to further understand the impact of using ethanol and n-butanol, the formation of different hydrocarbon species due to reforming on the Diesel Oxidation Catalyst (DOC) and LNT catalyst has been studied as well.
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Purohit, D., Dev, S., Tan, Q., Sandhu, N. et al., "An Investigation on the Regeneration of Lean NOx Trap Using Ethanol and n-Butanol," SAE Technical Paper 2019-01-0737, 2019, https://doi.org/10.4271/2019-01-0737.Data Sets - Support Documents
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