Application of the Model Predictive Control and Particle Swarm Optimization To Improving Management of Large Scale Water Transfer Systems: A Case Study

This paper has been studied to survey the performance of model predictive control and particle swarm optimization in improving the management of the water transport network. generally, heuristic approaches are computationally common and convenient, but do not provide guarantee on the global optimality. also, the main limitation of heuristic algorithms is the inability to solve large-scale systems due to problem dimensions. mpc is a modern control method which has been developed for industrial control problems. versatility of mpc has been studied in various fields, but the lack of studies in iran is apparent. in this research, an optimization model is designed by using pso and mpc for zarinehrud water transfer line in iran. the designed models have been solved for the two objective function, maintaining the safety stored water in the reservoirs and reducing the fluctuations in the pump stations. the obtained results showed that the proposed mpc yields high capability to satisfy all constraints while fluctuations in pump stations have been decreased and the volume of water in the reservoir maintains the lowest error in the vicinity of the desired volume. furthermore, the results show that, performance of the mpc in optimizing large-scale systems is better than pso.