Application of real-time control (RTC) is one possible measure to increase the performance of the urban wastewater system. However, the potential and the benefits of control depend strongly on the characteristics of the individual site under question. Conventionally, RTC potential is evaluated by performing a detailed feasibility study, which in some cases may conclude that for the given site real-time control does not have any significant potential. This can result in spending considerable precious resources for a detailed feasibility study only to identify the non-feasibility of RTC in the system.

It would therefore be desirable to have a methodology that allows simple, and cost-effective, screening of sites for which the analysis of real-time control may be beneficial. Earlier research has led to the provision of an easy-to-apply scoring system allowing a quick assessment of the RTC potential of controlling flow in sewer systems. However, as this procedure does not take into account water quality aspects, nor the treatment plant or the receiving water body, it cannot be used for assessing the potential of RTC of the complete system.

This paper describes the work of an on-going project aimed at establishing an enhanced procedure for assessing the real-time control potential for the entire urban wastewater system. This entails simulating many (partly hypothetical) case studies (varying several key parameters of the wastewater system) using the simulation tool SYNOPSIS. For each of these sites, several real-time control algorithms are developed and optimised, following a general procedure, which allows for local, global and integrated scenarios to be considered. Analysis of the results reveals those system parameters which are of particular significance to the RTC potential of urban wastewater systems. Furthermore, it is recognised that there is considerable uncertainty associated with modelling of such a large and diverse system and a framework is developed for incorporating this in the RTC potential screening tool. Finally, further work is currently underway in broadening the number of case study simulations and developing more complex approaches to quantifying and propagating uncertainty in the model.

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