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Three different methodologies are assessed which provide predictions of the hydraulic load to the treatment plant one hour ahead. The three models represent three different levels of complexity ranging from a simple regression model over an adaptive grey-box model to a complex hydrological and full dynamical wave model. The simple regression model is estimated as a transfer function model of rainfall intensity to influent flow. It also provides a model for the base flow. The grey-box model is a state space model which incorporates adaptation to the dry weather flow as well as the rainfall runoff. The full dynamical flow model is a distributed deterministic model with many parameters, which has been calibrated based on extensive measurement campaigns in the sewer system. The three models are compared by the ability to predict the hydraulic load one hour ahead. Five rain events in a test period are used for evaluating the three different methods. The predictions are compared to the actual measured flow at the plant one hour later. The results show that the simple regression model and the adaptive grey-box model which are identified and estimated on measured data perform significantly better than the hydrological and full dynamical flow model which is not identifiable and needs calibration by hand. For frontal rains no significant difference in the prediction performance between the simple regression model and the adaptive grey-box model is observed. This is due to a rather uniform distribution of frontal rains. A single convective rain justifies the adaptivity of the grey-box model for non-uniformly distributed rain, i.e. the predictions of the grey-box model were significantly better than the predictions of the simple regression model for this rain event. In general, models for model-based predictive control should be kept simple and identifiable from measured data.

The use of flow predictions and on-line nutrient sensors in BNR plants has given the basis for introduction of radical operational changes. Because of the detailed monitoring, the control actions are allowed to go closer to critical process limits while balancing between hydraulic and biological capacities. One of the new modes of operation, settling in the aeration tanks as an active control, is documented below. This new operation, Aeration Tank Settling (ATS) has been tested at full scale and shows a great potential for storm water control. With ATS control the hydraulic capacity is increased 25–75% on existing plants without reducing the organic capacity in periods ranging from 2 hours to 2 weeks. ATS control can be initiated directly by raising the inlet flow to the treatment plant, but even better by 1/2 - 1 hour prediction of the inlet flow. These predictions of flow are achieved from statistical grey-box handling of data from on-line rain gauges and measured inlet flow pattern during normal and stormwater conditions. Hourly predictions of concentrations in the inlet to the plant and selected points in the sewer system during dry weather and storm situations will improve the combined system control efficiency radically.

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On‐line measurements of turbidity, UV absorption and flow in the inlet to a Danish wastewater treatment plant are used to establish a dynamic model of the deposition of pollutants in the sewer system and the pollutant mass flow to the treatment plant. The modelling is made using the grey box approach, which is a statistical method that uses known physical relations to formulate the model. The dynamics of the sewer are modelled by means of continuous time stochastic differential equations combined with dry weather diurnal pollutant mass flows. Copyright © 2000 John Wiley & Sons, Ltd.