experimental study on the influence of dpf micropore structure and particle property on its filtration process

Clicks: 147
ID: 234162
2016
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Emerging

Ranked #33 of 35 articles by views in journal of neuroscience methods

Most read Least read

Bar heights use a square-root scale.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Abstract
A single layer filtration system was developed to investigate the filtration and regeneration performance of diesel particle filter (DPF). The particle layer thickness was directly measured online to analyze the different filtration stages. The influence of particle property on particle layer stage performance was also investigated. The results indicate that the filtration velocity can greatly affect the deep bed filtration stage, and the deposited particle layer can be compressed even in very low filtration velocity and higher filtration velocity trends to form denser particle layer. Optimizing the pore structure can effectively shorten the deep bed filtration stage and reduce the pressure drop eventually. An empirical function was proposed to relate the pore structure and the initial increment rate of pressure drop, which presented that reducing the pore size distribution range (3σ) can result in low DPF filtration pressure drop. The filtration stage could be further divided into four stages, and the value of particle layer thickness ranging within 15~20 μm has been found to be critical number for the shift from the transient stage to the cake filtration stage. Particle with large primary diameter and BET surface was beneficial to form loose particle layer.
Reference Key
meng2016journalexperimental Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Zhongwei Meng;Jia Fang;Yunfei Pu;Yan Yan;Yi Wu;Yongzhong Wang;Qiang Song
Journal journal of neuroscience methods
Year 2016
DOI
10.1155/2016/9612856
URL
Keywords

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

No comments yet. Be the first to comment on this article.