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 stateof-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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[1] This research program was aimed at developing an objective methodology for water quality management on a river basin scale. To that end, a mathematical model has been formulated to determine the best configuration of wastewater treatment plants consistent with either fixedemission standards or prescribed river quality objectives. It will, of course, be appreciated that the latter case is considerably more difficult since this involves not only site selection but also waste load allocation. In the case of waste load allocation it was first necessary to use a process-based river water quality simulation model to predict the impact of different combinations of effluent discharge standards on the river. For reasons of computational efficiency an artificial neural network was employed to replicate the process-based model, which was then used in conjunction with a genetic algorithm to determine both the best sites and individual effluent discharge standards, subject to meeting the required river water quality. The overall model has been applied to the upper Thames basin in southern England, initially for site selection alone and then for site selection with waste load allocation. The results show that the genetic algorithm performs well for both options, thereby providing an efficient means of planning wastewater treatment on a regional basis.

The natural apportionment of rainfall excess to surface and subsurface flow can mean the difference between large flow rates for a short time or more manageable flow rates over a longer time. By using available soil storage, peak flood flows can be reduced without necessarily reducing the total volume of runoff. An attempt has been made to simulate this phenomenon. The land phase of the hydrologic system has been simulated by a series of interconnected, nonlinear reservoirs and the channel phase by a single linear reservoir. By manipulation of parameters, which mathematically represent physical measurements, the watershed response to the addition of conservation practices, such as terracing, may be predicted. ONSTAD AND JAMIESON