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

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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.

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Zhao, Y., Taylor, J. S., & Chellam, S. (2005). Predicting RO/NF water quality by modified solution diffusion model and artificial neural networks. Journal of Membrane Science, 263(1–2), 38–46. https://doi.org/10.1016/j.memsci.2005.04.004

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