Abstract
The current model of the full-scale wastewater treatment plant (WWTP) model in Eindhoven uses a state-of-the-art model for the biological processes (ASM2d) and is calibrated for C- and N- removal in dry weather. However, for the ‘Kallisto’ project, which is an innovation programme aiming at a smart improvement of the surface water quality of the river Dommel by applying cost effective integrated system measures, the WWTP model needs to be improved to predict the WWTP performance under all conditions foreseen in the scenarios (e.g. storm events). A project approach was developed with parallel improvements in the different submodels, based on the interaction between submodels and the availability of several on-line sensors in influent, in-process and effluent. This is in contrast to most WWTP modelling studies, where focus is only on one submodel. It should lead to a well-balanced dynamic model that is able to predict WWTP behaviour under various conditions and that will be included in the integrated model, which will serve as an important decision support tool.
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