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 operational 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.
Conceptual design of a generic, real-time, near-optimal control system for water-distribution networks
FRANCHINI, Marco
2007
Abstract
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 operational 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.I documenti in SFERA sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
