Simulation Study of Mass Transfer Coefficient in Slurry Bubble Column Reactor Using Neural Network

  • Safa A. Al-Naimi Department of Chemical Engineering/ University of Technology
  • Salih A.J. Salih Department of Chemical Engineering/ University of Qadisiya
  • Hayder A. Mohsin Department of Chemical Engineering/ University of Technology
Keywords: slurry bubble column reactor, mass transfer coefficient, neural network

Abstract

 

The objective of this study was to develop neural network algorithm, (Multilayer Perceptron), based correlations for the prediction overall volumetric mass-transfer coefficient (kLa), in slurry bubble column for gas-liquid-solid systems. The Multilayer Perceptron is a novel technique based on the feature generation approach using back propagation neural network. Measurements of overall volumetric mass transfer coefficient were made with the air - Water, air - Glycerin and air - Alcohol systems as the liquid phase in bubble column of 0.15 m diameter. For operation with gas velocity in the range 0-20 cm/sec, the overall volumetric mass transfer coefficient was found to decrease with increasing solid concentration. From the experimental work 1575 data points for three systems, were collected and used to predicate  kLa. Using SPSS 17 software, predicting of overall volumetric mass-transfer coefficient (kLa) was carried out and an output of 0.05264 sum of square error was obtained for trained data and 0.01064 for test data.

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Published
2017-12-28
How to Cite
Al-Naimi, S., Salih, S., & Mohsin, H. (2017). Simulation Study of Mass Transfer Coefficient in Slurry Bubble Column Reactor Using Neural Network. Al-Khwarizmi Engineering Journal, 9(1), 60-70. Retrieved from http://alkej.uobaghdad.edu.iq/index.php/alkej/article/view/159
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Articles