Title
Predicting Ro/Nf Water Quality By Modified Solution Diffusion Model And Artificial Neural Networks
Keywords
Diffusion; Neural network; Reverses osmosis; Water treatment
Abstract
Membrane solute mass transfer is affected by physical-chemical properties of membrane films, solvent (water) and solutes. Existing mechanistic or empirical models that predict finished water quality from a diffusion controlled membrane can be significantly improved. Modelling membrane solute mass transfer by diffusion solution model is generally restricted to developing specific solute mass transfer coefficients that are site and stage specific. A modified solution diffusion model and two artificial neural network models have been developed for modelling diffusion controlled membrane mass transfer using stage specific solute MTCs. These models compensate for the effects of system flux, recovery and feed water quality on solute MTC and predict more accurately than existing models. © 2005 Elsevier B.V. All rights reserved.
Publication Date
10-15-2005
Publication Title
Journal of Membrane Science
Volume
263
Issue
1-2
Number of Pages
38-46
Document Type
Article
Personal Identifier
scopus
DOI Link
https://doi.org/10.1016/j.memsci.2005.04.004
Copyright Status
Unknown
Socpus ID
24944461921 (Scopus)
Source API URL
https://api.elsevier.com/content/abstract/scopus_id/24944461921
STARS Citation
Zhao, Yu; Taylor, James S.; and Chellam, Shankar, "Predicting Ro/Nf Water Quality By Modified Solution Diffusion Model And Artificial Neural Networks" (2005). Scopus Export 2000s. 3647.
https://stars.library.ucf.edu/scopus2000/3647