scholarly journals Learning Optimal Control of Water Distribution Networks Through Sequential Model-Based Optimization

Author(s):  
Antonio Candelieri ◽  
Bruno Galuzzi ◽  
Ilaria Giordani ◽  
Francesco Archetti
2020 ◽  
Vol 284 (1) ◽  
pp. 345-354 ◽  
Author(s):  
Dimitrios Nerantzis ◽  
Filippo Pecci ◽  
Ivan Stoianov

2013 ◽  
Vol 16 (3) ◽  
pp. 649-670 ◽  
Author(s):  
Myrna V. Casillas Ponce ◽  
Luis E. Garza Castañón ◽  
Vicenç Puig Cayuela

In this paper, we propose a new approach for model-based leak detection and location in water distribution networks (WDN), which considers an extended time-horizon analysis of pressure sensitivities. Five different ways of using the leak sensitivity matrix to isolate the leaks are described and compared. The first method is based on the binarization approach. The second, third and fourth methods are based on the comparison of the measured pressure vectors with the leak sensitivity matrix using different metrics: correlation, angle between vectors and Euclidean distance, respectively. The fifth method is based on the least square optimization method. The performance of these methods is compared when applied to two academic small networks (Hanoi and Quebra) widely used in the literature. Finally, the three methods with better performance are applied to a district metering area of the Barcelona WDN using real data.


2007 ◽  
Vol 9 (1) ◽  
pp. 3-14 ◽  
Author(s):  
Derek G Jamieson ◽  
Uri Shamir ◽  
Fernando Martinez ◽  
Marco Franchini

This paper is intended to serve as an introduction to the POWADIMA research project, whose objective was to determine the feasibility and efficacy of introducing real-time, near-optimal control for water-distribution networks. With that in mind, its contents include the current state-of-the-art and some of the difficulties that would need to be addressed if the goal of near-optimal control was to be achieved. Subsequently, the approach adopted is outlined, together with the reasons for the choice. Since it would be somewhat impractical to use a conventional hydraulic simulation model for real-time, near-optimal control, the methodology includes replicating the model by an artificial neural network which, computationally, is far more efficient. Thereafter, the latter is embedded in a dynamic genetic algorithm, designed specifically for real-time use. In this way, the near-optimal control settings to meet the current demands and minimize the overall pumping costs up to the operating horizon can be derived. The programme of work undertaken in achieving this end is then described. By way of conclusion, the potential benefits arising from implementing the control system developed are briefly reviewed, as are the possibilities of using the same approach for other application areas.


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