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 kLaL and liquid-side mass transfer coefficient kL were discussed in detail in the range of operating variables investigated.