Neural network based correlation for estimating water permeability constant in RO desalination process under fouling
- Submitting institution
-
The University of Bradford
- Unit of assessment
- 12 - Engineering
- Output identifier
- 89
- Type
- D - Journal article
- DOI
-
10.1016/j.desal.2014.04.016
- Title of journal
- Desalination
- Article number
- -
- First page
- 101
- Volume
- 345
- Issue
- -
- ISSN
- 0011-9164
- Open access status
- Out of scope for open access requirements
- Month of publication
- -
- Year of publication
- 2014
- URL
-
https://www.sciencedirect.com/science/article/abs/pii/S0011916414002318?via%3Dihub
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
3
- Research group(s)
-
-
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- With the scarcity of water in dry regions of the world, Reverse Osmosis (RO) is fast becoming the process of choice for producing potable water from saline water. This article describes the NN based modelling carried out to study the permeability of membranes used in RO processes. The novel feature is that the developed NN model is able to predict the permeability constant for a range of operating conditions and for the two most common membranes. The ability to predict the water permeability constant will significantly enhance future design and operation of RO plants.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
