Model-predictive control of feed flow reversal in a reverse osmosis desalination process

Abstract

Model-predictive control algorithms are applied to a high capacity reverse osmosis (RO) membrane desalination process simulation that utilizes feed flow reversal in order to prevent and/or reverse scale crystal formation on the membrane surface. A dynamic non-linear model which incorporates feed concentration and membrane properties is used for simulation and demonstration of optimally controlled feed flow reversal. Before flow reversal can take place on a high capacity RO plant, the flow into the membrane unit must be carefully reduced to eliminate the risk of membrane module damage and unnecessary energy consumption. A cost function is formulated for the transition between the normal high flow steady-state operating point to a low flow steady-state operating point where it is safe to reverse the flow direction. Open-loop and closed-loop simulations demonstrate non-linear model-predictive control strategies that induce transition from the high-flow to low-flow steady-states in an optimal way while subjected to plant-model mismatch on the feed concentration, actuator magnitude and rate constraints, and sampled measurements.