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Stacked neural networks for predicting the membranes performance by treating the pharmaceutical active compounds

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Abstract

The removal of pharmaceutical actives compounds (PhACs) by nanofiltration (NF) and reverse osmosis (RO) of paramount importance in membrane separation processes. However, modeling remains a difficult approach due to the strongly nonlinear performance of the removal mechanisms of organic molecules by NF/RO. The present work features the application of neural networks based on quantitative structure–activity relationship (single neural networks “QSAR-SNN” and bootstrap aggregated neural networks “QSAR-BANN(Staking of 30 networks)") for prediction of the removal of 23 pharmaceutical active compounds (PhACs). Overall, the models proposed are able to accurately correlate 599 experimental data points gathered from the literature. According to the results, the QSAR-BANN(Staking of 30 networks) is a more powerful and effective computational learning machine than the QSAR-SNN. The regression coefficients “\({R}^{2}\)” and the root mean squared error “RMSE” for the QSAR-BANN(Staking of 30 networks) model are estimated to be 0.9672 and 3.2810%, respectively. Moreover, QSAR-BANN(Staking of 30 networks) model capabilities is showed to describe the removal of PhACS by NF/RO and its precision is compared to proposed previous models, where this comparison showed the superiority of our BANN model. The work with one class of organic compounds (PhACs) is more suitable for prediction performances NF/RO by QSAR-BANN model.

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Acknowledgements

The authors gratefully acknowledge the group of Laboratory of Biomaterials and Transport Phenomena and the University of Relizane.

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Authors and Affiliations

  1. Laboratory of Biomaterials and Transport Phenomena (LBMPT), University Yahia Fares of Médéa, 26000, Médéa, Algérie

    Yamina Ammi, Latifa Khaouane & Salah Hanini

  2. University Center Ahmed Zabana Relizane, 48000, Relizane, Algeria

    Yamina Ammi

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  1. Yamina Ammi
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Ammi, Y., Khaouane, L. & Hanini, S. Stacked neural networks for predicting the membranes performance by treating the pharmaceutical active compounds. Neural Comput & Applic 33, 12429–12444 (2021). https://doi.org/10.1007/s00521-021-05876-0

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