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Modeling and Analysis on Emission Characteristics of Light-Duty Diesel Engine After-Treatment System Based on Neural Network
Technical Paper
2021-01-0595
ISSN: 0148-7191, e-ISSN: 2688-3627
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SAE WCX Digital Summit
Language:
English
Abstract
With the increasing complexity of diesel engine after-treatment systems, the
explosion of dimensions has made it difficult for traditional bench test
research to be competent for multi-parameter analysis. In this paper, based on
the experimental samples derived from a light-duty diesel engine bench test, a
neural network model for the emission characteristics of the after-treatment
system is established. After correctly training, the R values of the neural
networks for gaseous and particulate emission are all close to 1. The influence
of the precious metal parameters and structure of diesel oxidation catalyst
(DOC) and catalytic diesel particulate filter (CDPF) on diesel engine gaseous
and particulate emission reduction efficiency is analyzed through the neural
network. The result shows that with the increase of the engine speed under the
external characteristic, the particulate emission increases while the gaseous
emission reaches a peak at medium speed; as the DOC precious metal loading and
the DOC precious metal ratio (Pt/Pd) increase, the reduction efficiency of the
gaseous emission increases and the former has a greater impact; as the CDPF
precious metal loading increases, the reduction efficiency of the gaseous and
particulate emission both increase; as the CDPF precious metal ratio (Pt/Pd)
increases, the reduction efficiency of PN decreases; increasing the
length-diameter ratio of the DOC improves the reduction efficiency of gaseous
emission, while the length-diameter ratio of the CDPF is more sensitive to
particulate emission.
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Lou, D., Zhao, Y., Zhang, Y., and Sun, Y., "Modeling and Analysis on Emission Characteristics of Light-Duty Diesel Engine After-Treatment System Based on Neural Network," SAE Technical Paper 2021-01-0595, 2021, https://doi.org/10.4271/2021-01-0595.Data Sets - Support Documents
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