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• W2073001825 abstract "The water permeability constant, (Kw), is one of the many important parameters that affect optimal design and operation of RO processes. In model based studies, e.g. within the RO process model, estimation of Kw is therefore important. There are only two available literature correlations for calculating the dynamic Kw values. However, each of them is only applicable for a given membrane type, given feed salinity over a certain operating pressure range. In this work, we develop a time dependent neural network (NN) based correlation to predict Kw in RO desalination processes under fouling conditions. It is found that the NN based correlation can predict the Kw values very closely to those obtained by the existing correlations for the same membrane type, operating pressure range and feed salinity. However, the novel feature of this correlation is that it is able to predict Kw values for any of the two membrane types and for any operating pressure and any feed salinity within a wide range. In addition, for the first time the effect of feed salinity on Kw values at low pressure operation is reported. Whilst developing the correlation, the effect of numbers of hidden layers and neurons in each layer and the transfer functions is also investigated." @default.
• W2073001825 created "2016-06-24" @default.
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• W2073001825 date "2014-07-01" @default.
• W2073001825 modified "2023-10-17" @default.
• W2073001825 title "Neural network based correlation for estimating water permeability constant in RO desalination process under fouling" @default.
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• W2073001825 doi "https://doi.org/10.1016/j.desal.2014.04.016" @default.
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