Optimization models to save energy and enlarge the operational life of water pumping systems

Water pumping systems are widely used in industrial and civil applications. During their operational life, they consume energy and materials (mainly for installation and replacement of components). The aim of this paper is to optimize the pump operations in order to save energy and enlarge the operational life of the pumps and their components. This paper addresses the multi-period (e.g. 24-hours time horizon) optimization of pumping systems. To this end, we have developed a simulation-based optimization approach including novel relevant technical features of a generic but realistic pumping system, namely cavitation and overflow. The proposed multi-period optimization differs further from the classic static optimization (i.e. at the design stage), proposing a new dynamic approach in which pump activation is steered dynamically for an optimal management of the variability of water inflow. Furthermore, the problem includes constraints on the number of pump activations allowed to reduce material strain. The performances of different meta-heuristic optimization algorithms (e.g. genetic algorithm, simulated annealing and particle swarm optimization) for solving the problem are compared. The numerical results show that energy savings are possible with the dynamic approach and that particle swarm optimization and simulated annealing algorithms provide the most suitable solutions for this problem.