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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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    References listed on IDEAS

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    1. Ye, Jiahao & E, Jiaqiang & Peng, Qingguo, 2023. "Effects of porosity setting and multilayers of diesel particulate filter on the improvement of regeneration performance," Energy, Elsevier, vol. 263(PE).
    2. Peng, Qingguo & Xie, Bo & Yang, Wenming & Tang, Shihao & Li, Zhenwei & Zhou, Peng & Luo, Ningkang, 2021. "Effects of porosity and multilayers of porous medium on the hydrogen-fueled combustion and micro-thermophotovoltaic," Renewable Energy, Elsevier, vol. 174(C), pages 391-402.
    3. Wang, Guoyang & Awad, Omar I. & Liu, Shiyu & Shuai, Shijin & Wang, Zhiming, 2020. "NOx emissions prediction based on mutual information and back propagation neural network using correlation quantitative analysis," Energy, Elsevier, vol. 198(C).
    4. Demir, Emrah & Bektaş, Tolga & Laporte, Gilbert, 2014. "A review of recent research on green road freight transportation," European Journal of Operational Research, Elsevier, vol. 237(3), pages 775-793.
    5. Ren, Lei & Zhou, Sheng & Peng, Tianduo & Ou, Xunmin, 2022. "Greenhouse gas life cycle analysis of China's fuel cell medium- and heavy-duty trucks under segmented usage scenarios and vehicle types," Energy, Elsevier, vol. 249(C).
    6. Hamedi, Mohammad Reza & Doustdar, Omid & Tsolakis, Athanasios & Hartland, Jonathan, 2021. "Energy-efficient heating strategies of diesel oxidation catalyst for low emissions vehicles," Energy, Elsevier, vol. 230(C).
    7. Domínguez-Sáez, Aida & Rattá, Giuseppe A. & Barrios, Carmen C., 2018. "Prediction of exhaust emission in transient conditions of a diesel engine fueled with animal fat using Artificial Neural Network and Symbolic Regression," Energy, Elsevier, vol. 149(C), pages 675-683.
    8. Tan, Dongli & Wu, Yao & Lv, Junshuai & Li, Jian & Ou, Xiaoyu & Meng, Yujun & Lan, Guanglin & Chen, Yanhui & Zhang, Zhiqing, 2023. "Performance optimization of a diesel engine fueled with hydrogen/biodiesel with water addition based on the response surface methodology," Energy, Elsevier, vol. 263(PC).
    9. Yuan, Zhiyi & Ou, Xunmin & Peng, Tianduo & Yan, Xiaoyu, 2019. "Life cycle greenhouse gas emissions of multi-pathways natural gas vehicles in china considering methane leakage," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    10. Tan, Dongli & Meng, Yujun & Tian, Jie & Zhang, Chengtao & Zhang, Zhiqing & Yang, Guanhua & Cui, Shuwan & Hu, Jingyi & Zhao, Ziheng, 2023. "Utilization of renewable and sustainable diesel/methanol/n-butanol (DMB) blends for reducing the engine emissions in a diesel engine with different pre-injection strategies," Energy, Elsevier, vol. 269(C).
    11. Hu, Lin & Tian, Qingtao & Zou, Changfu & Huang, Jing & Ye, Yao & Wu, Xianhui, 2022. "A study on energy distribution strategy of electric vehicle hybrid energy storage system considering driving style based on real urban driving data," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
    12. Wu, Tian & Han, Xiao & Zheng, M. Mocarlo & Ou, Xunmin & Sun, Hongbo & Zhang, Xiong, 2020. "Impact factors of the real-world fuel consumption rate of light duty vehicles in China," Energy, Elsevier, vol. 190(C).
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    3. 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).
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