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In this work, we develop a time dependent neural network (NN) based correlation to predict Kw in RO desalination processes under fouling conditions.
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We also performed gene classification in this module based on expression correlation to predict the genes influenced by E6 or E7, and found the YWHAZ gene, known to interact with TP53, tightly clustered with E6 and E7 (Additional file 1: Figure S24).
Hence, a reduction factor was proposed for older pavements based on cumulative traffic volume for the above correlation to predict the skid resistance of older pavements.
Based on the experimental data, a dimensionless analysis was conducted, and a new correlation to predict the OFI for supercritical fluids was obtained.
Finally, a simple empirical correlation to predict the optimal size of battery and the lowest payback period are proposed based on the annual electrical production and consumption and the feed-in tariff.
An empirical correlation to predict kLa values was proposed.
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.
It is found that the NN based correlations can predict the experimental TE very closely.
The interest was given to develop empirical correlations to predict the erosion-corrosion rate as a function of the Reynolds number, based on the result presented in the 'Iron weight losses' section.
Several investigators propose correlations to predict the flooding velocity.
In this work, we develop several neural network (NN) based correlations for predicting TE.
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