陶瓷膜通道相互作用的实验分析及CFD优化

PENG Wenbo; QI Hong; LI Weixing; XING Weihong; XU Nanping

您当前的位置：

首页 >

文章列表页 >

陶瓷膜通道相互作用的实验分析及CFD优化

更新时间：2025-12-10

- 陶瓷膜通道相互作用的实验分析及CFD优化
- CIESC Journal Issue 3, (2008)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Online First：05 March 2008，
  
  Published：05 March 2008
- 稿件说明：
移动端阅览
PENG Wenbo, QI Hong, LI Weixing, et al. 陶瓷膜通道相互作用的实验分析及CFD优化[J]. CIESC Journal, 2008, (3).
DOI：

PENG Wenbo, QI Hong, LI Weixing, et al. 陶瓷膜通道相互作用的实验分析及CFD优化[J]. CIESC Journal, 2008, (3). DOI：

摘要

提出陶瓷膜过滤时，通道之间存在3种效应关系（壁厚效应、干扰效应、遮挡效应），并用实验进行了验证。对于多通道陶瓷膜构型的设计，要考虑3种效应。膜孔径小于200 nm的陶瓷膜，可以增大其通道的排布密度，通过提高装填密度可以提高单位体积的处理量；膜孔径大于500 nm的陶瓷膜，中间的通道对通量几乎没有贡献，提高装填密度意义不大。固定膜元件的外径，选取通道直径ac和壁厚aw

并设定两参数的比值为α，采用计算流体力学（CFD）软件进行模拟计算，获得了通量与处理量随孔径与α值的变化关系。

Abstract

There are three effects in the ceramic membrane filtration process

wall thickness effect

interfering effect

sheltering effect

and these effects are validated by experiments.When mean pore size of membrane is less than 200 nm

flow by volume is increased by enhancing loading density.When mean pore size of membrane is more than 500 nm

the contribution of middle channel to the total pure water flux (PWF) of ceramic membranes can be neglected

and it is no use to enhance loading density.It is assumed that the diameter of membrane module was unchanged

and defined channel diameter ac

thickness of the wall aw

and set α as the ratio of ac and aw.The relationship between flux

flow

mean pore size and α was acquired by numerical simulation with CFD software.

关键词

Keywords

references

Views

783

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Recent advances in machine learning for modeling and control of crystallization processes

Study on impact of impurities in coal chemical waste salt on solvation of NaCl and Na2SO4

Prediction model for COD in petrochemical wastewater influent based on temporal-spatial dual-stream convolutional neural network

Thermal analysis of phase change suspension jet impinging on wall surface covered with variable-shape porous layer

A two-stage dual-channel deep learning model for chemical fault classification

Related Author

No data

Related Institution

No data

⁰