Izvorni znanstveni članak
https://doi.org/10.15255/KUI.2019.010
Comparative Study of Predicting the Molecular Diffusion Coefficient for Polar and Non-polar Binary Gas Using Neural Networks and Multiple Linear Regressions
Naima Melzi
; Laboratory of Biomaterials and Transport Phenomena (LBMPT), University of Médéa, 26 000 Médéa, Algeria
Latifa Khaouane
; Laboratory of Biomaterials and Transport Phenomena (LBMPT), University of Médéa, 26 000 Médéa, Algeria
Yamina Ammi
; Laboratory of Biomaterials and Transport Phenomena (LBMPT), University of Médéa, 26 000 Médéa, Algeria
Salah Hanini
; Laboratory of Biomaterials and Transport Phenomena (LBMPT), University of Médéa, 26 000 Médéa, Algeria
Maamar Laidi
orcid.org/0000-0002-8977-9895
; Laboratory of Biomaterials and Transport Phenomena (LBMPT), University of Médéa, 26 000 Médéa, Algeria
Hamid Zentou
; Department of Chemical and Environmental Engineering, Universiti Putra Malaysia, 43 400 Serdang, Malaysia
Sažetak
In the current study, an artificial neural network (ANN) and multiple linear regressions (MLR) have been used to develop predictive models for the estimation of molecular diffusion coefficients of 1252 polar and non-polar binary gases at multiple pressures over a wide range of temperatures and substances. The quality and reliability of each method were estimated in terms of the correlation coefficient (R), mean squared errors (MSE), root mean squared error (RMSE), and in terms of external validation coefficients (Q2ext).
The comparison between the artificial neural network (ANN) and the multiple linear regressions (MLR) revealed that the neural network models showed a good predicting ability with lower errors (the roots of the mean squared errors in the total database were 0.1400 for ANN1 and 0.1300 for ANN2), and (root mean squared errors in the total databases were 0.5172 for MLR1 and 0.5000 for MLR2).
This work is licensed under a Creative Commons Attribution 4.0 International License.
Ključne riječi
prediction; molecular diffusion; neural networks; multiple linear regressions
Hrčak ID:
228059
URI
Datum izdavanja:
7.12.2019.
Posjeta: 1.299 *