Predicting RO/NF water quality by modified solution diffusion model and artificial neural networks Academic Article

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.

authors

• Chellam, Shankararaman

published proceedings

• JOURNAL OF MEMBRANE SCIENCE

author list (cited authors)

• Zhao, Y., Taylor, J. S., & Chellam, S.

citation count

• 76

complete list of authors

• Zhao, Y||Taylor, JS||Chellam, S

publication date

• October 2005

publisher

• Elsevier  Publisher

published in

• n0376-7388ISSN  Journal

keywords

• Diffusion
• Neural Network
• Reverses Osmosis
• Water Treatment

Digital Object Identifier (DOI)

• 10.1016/j.memsci.2005.04.004

start page

• 38

end page

• 46

volume

• 263

issue

• 1-2

URL

• http://dx.doi.org/10.1016/j.memsci.2005.04.004