NDLI: Modeling the Retention of Organic Compounds by Nanofiltration and Reverse Osmosis Membranes Using Bootstrap Aggregated Neural Networks

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Arabian Journal for Science and Engineering : Volume 42

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Modeling the Retention of Organic Compounds by Nanofiltration and Reverse Osmosis Membranes Using Bootstrap Aggregated Neural Networks

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Modeling the Retention of Organic Compounds by Nanofiltration and Reverse Osmosis Membranes Using Bootstrap Aggregated Neural Networks

Content Provider	Springer Nature Link
Author	Khaouane, Latifa Ammi, Yamina Hanini, Salah
Copyright Year	2016
Abstract	The purpose of the current work was to investigate the use of bootstrap aggregated neural networks (BANN) in modeling the rejection processes of charged and uncharged organic compounds by nanofiltration and reverse osmosis membranes. A total database of 436 rejections of 42 charged and uncharged organic compounds collected from the literature was used to build the BANN model. The training dataset (350 data points) is re-sampled using a bootstrap technique to form different training datasets. For each training set, an INN model is constructed, validated, and tested. The predicted outputs obtained from the developed INN models are then combined together by simple averaging. Good agreement between the predicted and experimental rejections for the BANN model was obtained (the correlation coefficient for the test dataset was 0.9862). The comparison between the BANN, single neural network (SNN), and bootstrap aggregated multiple linear regressions (BAMLR) revealed the superiority of the BANN model (the root mean squared errors for the test dataset were 5.3315 for the BANN, 6.4587 for the SNN, and 18.7834 for the BAMLR).
Starting Page	1443
Ending Page	1453
Page Count	11
File Format	PDF
ISSN	2193567X
Journal	Arabian Journal for Science and Engineering
Volume Number	42
Issue Number	4
e-ISSN	21914281
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2016-09-28
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Membranes Organic compounds Rejection Neural networks Bootstrap Engineering Science, Humanities and Social Sciences, multidisciplinary
Content Type	Text
Resource Type	Article
Subject	Multidisciplinary

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Prediction of the rejection of organic compounds (neutral and ionic) by nanofiltration and reverse osmosis membranes using neural networks

Groundwater Remediation of Volatile Organic Compounds Using Nanofiltration and Reverse Osmosis Membranes—A Field Study

Transport and Removal Mechanisms of Trace Organic Pollutants by Nanofiltration and Reverse Osmosis Membranes

Tannery Effluent Treatment by Nanofiltration, Reverse Osmosis and Chitosan Modified Membranes

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Integration of Nanofiltration and Reverse Osmosis Technologies in Polyphenols Recovery Schemes from Winery and Olive Mill Wastes by Aqueous-Based Processing

Aminophosphonates in Nanofiltration and Reverse Osmosis Permeates

Cyclophosphamide removal by nanofiltration and reverse osmosis membranes - effect of water matrix properties

Determination of Perchlorate Rejection and Associated Inorganic Fouling (Scaling) for Reverse Osmosis and Nanofiltration Membranes under Various Operating Conditions

Modeling the Retention of Organic Compounds by Nanofiltration and Reverse Osmosis Membranes Using Bootstrap Aggregated Neural Networks

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