Operational cost optimization of a full-scale SWRO system under multi-parameter variable conditions

In this work, an operational optimization designed to reduce the operational cost of a full-scale seawater reverse osmosis (SWRO) system was studied under variable operating conditions. To increase operational flexibility and cost savings potential, the storage tank was used as the buffer between freshwater production and supply. With a well-developed mathematical model, which included reverse osmosis (RO) process equations, the storage tank equations and operational cost equations, the optimal problem was formulated in the form of nonlinear DAOPs (differential algebraic optimization problems). To solve the problem effectively, a simultaneous approach was introduced in which the differential and algebraic variables were fully discretized. Then nonlinear solver of IPOPT was used to solve the discretized large-scale nonlinear programming (NLP) problem. Computational results show that the operational optimization has significant potential of more than 26% cost saving over conventional method. Based on the successful solution, the impacts of variable parameters, such as feed temperature, seawater salinity, electricity price and freshwater demand were analyzed in detail. The effect of these parameters on operational cost savings and corresponding key performance indicators was determined, which enhances the understanding of the SWRO process and its optimal control.