详细信息

Oxygen mass transfer coefficient in bubble column slurry reactor with ultrafine suspended particles and neural network prediction  ( SCI-EXPANDED收录 EI收录)  

文献类型:期刊文献

英文题名:Oxygen mass transfer coefficient in bubble column slurry reactor with ultrafine suspended particles and neural network prediction

作者:Chen, Zhen[1,2];Liu, Hongwei[3];Zhang, Haitao[1];Ying, Weiyong[1];Fang, Dingye[1]

机构:[1]E China Univ Sci & Technol, State Key Lab Chem Engn, Engn Res Ctr Large Scale Reactor Engn & Technol, Minist Educ, Shanghai 200237, Peoples R China;[2]Shandong Polytech Univ, Sch Chem & Pharmaceut Engn, Jinan 250353, Shandong, Peoples R China;[3]E China Univ Sci & Technol, Sch Pharm, Shanghai 200237, Peoples R China

年份:2013

卷号:91

期号:3

起止页码:532

外文期刊名:CANADIAN JOURNAL OF CHEMICAL ENGINEERING

收录:;EI(收录号:20130716019170);WOS:【SCI-EXPANDED(收录号:WOS:000314655600016)】;

基金:The authors gratefully acknowledge the financial support by the National Basic Research Program of China (973 Program) (no. 2005CB221205) and by the National Technology Support Program of China (no. 2006BAE02B02).

语种:英文

外文关键词:bubble column slurry reactor; dissolved oxygen; mass transfer coefficient; interfacial area; artificial neural network

摘要:The gasliquid volumetric mass transfer coefficient was determined by the dynamic oxygen absorption technique using a polarographic dissolved oxygen probe and the gasliquid interfacial area was measured using dual-tip conductivity probes in a bubble column slurry reactor at ambient temperature and normal pressure. The solid particles used were ultrafine hollow glass microspheres with a mean diameter of 8.624 mu m. The effects of various axial locations (heightdiameter ratio=112), superficial gas velocity (uG=0.0110.085m/s) and solid concentration (epsilon S=030wt.%) on the gasliquid volumetric mass transfer coefficient kLaL and liquid-side mass transfer coefficient kL were discussed in detail in the range of operating variables investigated. Empirical correlations by dimensional analysis were obtained and feed-forward back propagation neural network models were employed to predict the gasliquid volumetric mass transfer coefficient and liquid-side mass transfer coefficient for an airwaterhollow glass microspheres system in a commercial-scale bubble column slurry reactor. (c) 2012 Canadian Society for Chemical Engineering

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