Neural Network Simulation for Mass Transfer Coefficient of Alcohol/Ether Fuel Direct Synthesis

Qing Chun Chen; Zhen Chen

doi:10.4028/www.scientific.net/AMR.781-784.2400

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 781-784Neural Network Simulation for Mass Transfer...

Neural Network Simulation for Mass Transfer Coefficient of Alcohol/Ether Fuel Direct Synthesis

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. 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 Alcohol/Ether fuel direct synthesis system in a commercial-scale bubble column slurry reactor. And the effects of various axial locations, superficial gas velocity and solid concentration on the gas-liquid volumetric mass transfer coefficient k_La_L and liquid-side mass transfer coefficient k_L were discussed in detail in the range of operating variables investigated.

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Periodical:

Advanced Materials Research (Volumes 781-784)

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2400-2405

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.781-784.2400

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Online since:

September 2013

Authors:

Qing Chun Chen, Zhen Chen

Keywords:

Gas-Liquid Interfacial Area, Mass Transfer Coefficient, Neural Network (NN)

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