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

Authors

  • Zhen Chen,

    1. State Key Laboratory of Chemical Engineering, Engineering Research Center of Large Scale Reactor Engineering and Technology, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China
    2. School of Chemistry and Pharmaceutical Engineering, Shandong Polytechnic University, Ji'nan, Shandong 250353, China
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  • Hongwei Liu,

    1. School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
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  • Haitao Zhang,

    1. State Key Laboratory of Chemical Engineering, Engineering Research Center of Large Scale Reactor Engineering and Technology, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China
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  • Weiyong Ying,

    Corresponding author
    1. State Key Laboratory of Chemical Engineering, Engineering Research Center of Large Scale Reactor Engineering and Technology, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China
    • State Key Laboratory of Chemical Engineering, Engineering Research Center of Large Scale Reactor Engineering and Technology, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China.
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  • Dingye Fang

    1. State Key Laboratory of Chemical Engineering, Engineering Research Center of Large Scale Reactor Engineering and Technology, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China
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Abstract

The gas–liquid volumetric mass transfer coefficient was determined by the dynamic oxygen absorption technique using a polarographic dissolved oxygen probe and the gas–liquid 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 µm. The effects of various axial locations (height–diameter ratio = 1–12), superficial gas velocity (uG = 0.011–0.085 m/s) and solid concentration (εS = 0–30 wt.%) on the gas–liquid 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 gas–liquid volumetric mass transfer coefficient and liquid-side mass transfer coefficient for an air–water–hollow glass microspheres system in a commercial-scale bubble column slurry reactor. © 2012 Canadian Society for Chemical Engineering

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