Modeling The Reverse Osmosis Processes Performance Using Artificial Neural Networks


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One of the more serious problems encountered in reverse osmosis (RO) water treatment processes is the occurrence of membrane fouling, which limits both operation efficiency (separation performances, water permeate flux, salt rejection) and membrane life‐time. In the present study, artificial neural network (ANN)‐based models were developed based on direct analysis of experimental data for predicting process operation performance. Two approaches were considered; one based on characterizing the organic compounds passage through RO membranes, and a second one based on modeling the dynamics of permeate flow and separation performances for a full‐scale RO desalination plant.

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