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Because the optimal operation trajectory obtained under a fixed freshwater demand and known operational conditions could not achieve cost savings when these parameters change uncertainly over time, in this paper, a real time operational optimization method based on rolling prediction of hourly freshwater demand is proposed. First, via analysis of the historical data of hourly water consumption in a seawater desalination system, a new method for predicting the daily water demand in the next 24 h is proposed, and the predicted trajectory is continuously corrected and updated with present freshwater consumption. Then, with the well-established seawater desalination system model combined with the dynamics of a storage tank, a real time optimal operational problem with the strategy for its solution is given to minimize the daily operational cost. The optimization problem with differential and algebraic equations is discretized into a nonlinear programming problem by finite element collocation, and then a rolling optimization solution strategy based on simulation is used. Finally, a case study is used to verify the proposed method. The results show that the proposed optimal operation method can achieve significant cost savings and can also overcome the water level violation problem under a fixed freshwater demand or via the conventional method.

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Reverse osmosis (RO) technique is one of the most efficient ways for seawater desalination to solve the shortage of freshwater. For prediction and analysis of the performance of seawater reverse osmosis (SWRO) process, an accurate and detailed model based on the solution-diffusion and mass transfer theory is established. Since the accurate formulation of the model includes many differential equations and strong nonlinear equations (differential and algebraic equations, DAEs), to solve the problem efficiently, the simultaneous method through orthogonal collocation on finite elements and large scale solver were used to obtain the solutions. The model was fully discretized into NLP (nonlinear programming) with large scale variables and equations, and then the NLP was solved by large scale solver of IPOPT. Validation of the formulated model and solution method is verified by case study on a SWRO plant. Then simulation and analysis are carried out to demonstrate the performance of reverse osmosis process; operational conditions such as feed pressure and feed flow rate as well as feed temperature are also analyzed. This work is of significant meaning for the detailed understanding of RO process and future energy saving through operational optimization.

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