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The modeling of the Organic Molecules Rejection using the Bootstrap Aggregated Neural Networks for the evaluation of the Forward Osmosis Process performance

  • Authors (legacy)
    Corresponding: kratbi fouad
    Co-authors: Kratbi F
    Ammi Y
    Hanini S
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  • gnest_05404_final.pdf
  • Paper ID
    gnest_05404
  • Paper status
    Published
  • Date paper submitted
  • Date paper accepted
  • Date paper online
DOI
https://doi.org/10.30955/gnj.005404
Graphical abstract
Abstract

The forward osmosis process is currently more studied to be a replacement for another consuming-energy process, for this, many works show up the rejection of different molecules, energy consumption, and modeling of different objectives related to FO process. Our study consists to model the rejection of organic molecules (neutral and ionic) by FO process; however, this paper is the simultaneous applications of the single neural network based on quantitative- structure properties relationship (QSPR-SNN) and the bootstrap aggregated neural network (BANN) to predict the rejection of 53 OM. According to the results obtained, the coefficient correlation "R" is used to evaluate the performance of each model for the unseen data, the QSPR-BANN gives R value equal to 0.9909 higher than the value of the SNN which is 0.9401, the Root Mean Square Error of the QSPR-BANN is less than that of the QSPR-SNN with values equal to 0.5764% and 1.2826% respectively.

Keywords
Modeling Organic molecules Rejection Bootstrap Aggregated Neural Networks Forward Osmosis Membranes.
Cite this article
Kratbi, F., Ammi, Y. and Hanini, S. (2024) “The modeling of the Organic Molecules Rejection using the Bootstrap Aggregated Neural Networks for the evaluation of the Forward Osmosis Process performance”, Global NEST Journal, 26(5). Available at: https://doi.org/10.30955/gnj.005404.
PDF file
  • gnest_05404_final.pdf