Improved emissions conversion of diesel oxidation catalyst using multifactor impact analysis and neural network

Jiahao Ye and Qingguo Peng

Energy, 2023, vol. 271, issue C

Abstract: Diesel Oxidation Catalyst (DOC) is an effective device to reduce engine emissions. To improve the oxidation performance of HCs, NOx and CO, a 3D simulation model of emissions conversion of DOC and the BP neural network prediction model are established. Effects of exhaust conditions (catalyst Pd/Pt blended ratio, velocity Vi, temperature Te and O2 fraction Fo2) on DOC working performance are investigated. The results show that the increased Fo2/Te and decreased Vi can both improve the conversion rate of emissions, and the Pd/Pt blended ratio strongly affects the emissions oxidation. The conversion rate of CO, C3H6 and NO are increased by 2.917%,17.695% and 94.729% respectively when Pd/Pt blended ratio is increased from 0 to 1 at Te = 400 K. 4992 cases are predicted by BP neural network after the validation by 1164 group simulation results of the DOC with various exhaust conditions. Then, 71 cases with emissions conversion rate above 90% are obtained, particularly the three pollutants conversion rate are both achieved 99% under the condition between Pd/Pt = 0.75, Te = 550 K, Vi = 5 m/s, Fo2 = 0.05 and Pd/Pt = 0.25, Te = 600 K, Vi = 5 m/s, Fo2 = 0.05. So, optimized boundary conditions are selected to reduce emissions.

Keywords: Diesel oxidation catalyst; Conversion rate; BP neural Network; Oxidation performance (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:271:y:2023:i:c:s0360544223004425

DOI: 10.1016/j.energy.2023.127048

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