Skip to the main content

Chemistry in Industry : Journal of Chemists and Chemical Engineers of Croatia, Vol. 69 No. 3-4, 2020.

Original scientific paper

https://doi.org/10.15255/KUI.2019.024

A Comparison of “Neural Networks and Multiple Linear Regressions” Models to Describe the Rejection of Micropollutants by Membranes

Yamina Ammi ; University Center Ahmed Zabana Relizane, 48 000, Algeria
Latifa Khaouane ; Laboratory of Biomaterials and Transport Phenomena (LBMPT), University of Médéa, 26 000, Algeria
Salah Hanini ; Laboratory of Biomaterials and Transport Phenomena (LBMPT), University of Médéa, 26 000, Algeria

Full text: english pdf 1.469 Kb

page 111-127

downloads: 413

cite

Abstract

A rejection process of organic compounds by nanofiltration and reverse osmosis membranes was modelled using the artificial neural networks. Three feed-forward neural networks based on quantitative structure-activity relationship (QSAR-NN models) characterised by a similar structure (twelve neurons for QSAR-NN1, QSAR-NN2, and QSAR-NN3 in the input layer, one hidden layer and one neuron in the output layer), were constructed with the aim of predicting the rejection of organic compounds. A set of 1394 data points for QSAR-NN1, 980 data points for QSAR-NN2, and 436 data points for QSAR-NN3 were used to construct the neural networks. Good agreements between the predicted and experimental rejections were obtained by QSAR-NN models (the correlation coefficient for the total dataset were 0.9191 for QSAR-NN1, 0.9338 for QSAR-NN2, and 0.9709 for QSAR-NN3). Comparison between the feed-forward neural networks and multiple linear regressions based on quantitative structure-activity relationship “QSAR-MLR” revealed the superiority of the QSAR-NN models (the root mean squared errors for the total dataset for the QSAR-NN models were 10.6517 % for QSAR-NN1, 9.1991 % for QSAR-NN2, and 5.8869 % for QSAR-NN3, and for QSAR-MLR models they were 20.1865 % for QSAR-MLR1, 19.3815 % for QSAR-MLR2, and 16.2062 % for QSAR-MLR3).


This work is licensed under a Creative Commons Attribution 4.0 International License.

Keywords

neural networks; multiple linear regressions; models; rejection; micropollutants; membranes

Hrčak ID:

235865

URI

https://hrcak.srce.hr/235865

Publication date:

19.3.2020.

Article data in other languages: croatian

Visits: 1.573 *