The KALLISTO project aims at finding cost-efficient sets of measures to meet the Water Framework Directive (WFD) derived goals for the river Dommel. Within the project, both acute and long term impacts of the urban wastewater system on the chemical and ecological quality of the river are studied with an integral monitoring campaign in the urban wastewater system (WWTP and sewers) and in the river. Based on this monitoring campaign, detailed models were calibrated. These models are partly simplified and integrated in a single model, which is validated using the detailed submodels. The integrated model was used to study the potential for impact-based real-time control (RTC). Impact based RTC proved to be able to improve the quality of the receiving waters significantly, although additional measures remain necessary to be able to meet the WFD requirements

Membrane bioreactors (MBR) are an important and increasingly implemented wastewater treatment technology, which are operated at low food to microorganism ratios (F/M) and retain slow-growing organisms. Enhanced biological phosphorus removal (EBPR)-related organisms grow slower than ordinary heterotrophs, but have never been studied in detail in MBRs. This study presents a comprehensive analysis of the microorganisms involved in EBPR in pilot-and full-scale MBRs, using fluorescence in situ hybridization (FISH), as well as an overall assessment of other relevant microbial groups. The results showed that polyphosphate accumulating organisms (PAOs) were present at similar levels in all studied MBRs (10%±6%), even those without a defined anaerobic zone. Glycogen accumulating organisms were also detected, although rarely. The FISH results correlated well with the observed P removal performance of each plant. The results from this study suggest that a defined anaerobic zone is not necessarily required for putative PAO growth in MBRs, since polyphosphate storage may provide a selective advantage in fulfilling cell maintenance requirements in substrate-limited conditions (low F/M).

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The paper investigates the impact of the way oxygen transfer is modelled and the frequency of influent data on the dynamic calibration of a full-scale WWTP. Oxygen transfer was modelled in 2 ways: by means of a fast "virtual controller" tracking dissolved oxygen and by means of a linear correlation between K L a and air flow rate. Influent data was retrieved from correlations derived from either off-line data or on-line data. The correlations in the latter case were found to be better. With regard to model performance, it was found that the oxygen transfer model based on the linear relation between K L a and air flow rate was able to sufficiently capture DO dynamics. Using the off-line data based correlation for influent data resulted in a decent DO profile prediction, but the nitrification prediction was not accurate implying that a calibration effort is required. However, when using on-line influent data correlations, NH 4 predictions were found accurate without any need for calibration. Using more influent data resulted in a small deterioration of NH 4 predictions but resulted in better NO 3 predictions.