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

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  • Ye, Jiahao
  • Peng, Qingguo

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.

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  • Ye, Jiahao & Peng, Qingguo, 2023. "Improved emissions conversion of diesel oxidation catalyst using multifactor impact analysis and neural network," Energy, Elsevier, vol. 271(C).
    • Handle: RePEc:eee:energy:v:271:y:2023:i:c:s0360544223004425
    DOI: 10.1016/j.energy.2023.127048
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    2. Peng, Qingguo & Ye, Jiahao & Kang, Zhuang, 2024. "Optimization of diesel oxidation catalyst for enhanced emission reduction in engines," Energy, Elsevier, vol. 290(C).
    3. Tang, Shihao & Wei, Jia & Xie, Bo & Shi, Zhiwei & Wang, Hao & Tian, Xinghua & He, Biao & Peng, Qingguo, 2023. "Experimental and numerical investigation on H2-fueled thermophotovoltaic micro tube with multi-cavity," Energy, Elsevier, vol. 274(C).
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    6. Tan, Dongli & Li, Dongmei & Wang, Su & Zhang, Zhiqing & Tian, Jie & Li, Jiangtao & Lv, Junshuai & Zheng, Wenling & Ye, Yanshuai, 2023. "Evaluation and optimization of hydrogen addition on the performance and emission for biodiesel dual-fuel engines with different blend ratios based on the response surface method," Energy, Elsevier, vol. 283(C).
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    8. Wang, Hao & Peng, Qingguo & Tian, Xinghua & Yan, Feng & Wei, Depeng & Liu, Hui, 2024. "Experimental and numerical investigation on H2-fueled micro-thermophotovoltaic with CH4 and C3H8 blending in a tube fully/partially inserted porous media," Renewable and Sustainable Energy Reviews, Elsevier, vol. 191(C).
    9. Gao, Sheng & Zhang, Yanhui & Zhang, Zhiqing & Tan, Dongli & Li, Junming & Yin, Zibin & Hu, Jingyi & Zhao, Ziheng, 2023. "Multi-objective optimization of the combustion chamber geometry for a highland diesel engine fueled with diesel/n-butanol/PODEn by ANN-NSGA III," Energy, Elsevier, vol. 282(C).
    10. Liu, Wenlong & Gao, Ying & You, Yuelin & Jiang, Changwen & Hua, Taoyi & Xia, Bocong, 2024. "Nonlinear model predictive control(NMPC) of diesel oxidation catalyst (DOC) outlet temperature for active regeneration of diesel particulate filter (DPF) in diesel engine," Energy, Elsevier, vol. 293(C).

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