Towards model predictive control: online predictions of ammonium and nitrate removal by using a stochastic ASM.

Online model predictive control (MPC) of water resource recovery facilities (WRRFs) requires simple and fast models to improve the operation of energy-demanding processes, such as aeration for nitrogen removal. Selected elements of the activated sludge model number 1 modelling framework for ammonium and nitrate removal were included in discretely observed stochastic differential equations in which online data are assimilated to update the model states. This allows us to produce model-based predictions including uncertainty in real time while it also reduces the number of parameters compared to many detailed models. It introduces only a small residual error when used to predict ammonium and nitrate concentrations in a small recirculating WRRF facility. The error when predicting 2 min ahead corresponds to the uncertainty from the sensors. When predicting 24 hours ahead the mean relative residual error increases to ∼10% and ∼20% for ammonium and nitrate concentrations respectively. Consequently this is considered a first step towards stochastic MPC of the aeration process. Ultimately this can reduce electricity demand and cost for water resource recovery, allowing the prioritization of aeration during periods of cheaper electricity.

Conservation principles suspended solids distribution modelling to support ATS introduction on a recirculating WWTP.

A model for the description of the SS distribution in a full-scale recirculating activated sludge WWTP was developed. The model, based on conservation principles, uses on-line plant data as model inputs, and provides a prediction of the SS load in the inlet to the secondary clarifiers and the SS distribution in the WWTP as outputs. The calibrated model produces excellent predictions of the SS load to the secondary clarifiers, an essential variable for the operation of the aeration tank settling (ATS) process. A case study illustrated how the calibrated SS distribution model can be used to evaluate the potential benefit of ATS implementation on a full-scale recirculating WWTP. A reduction of the maximum SS peak load to the secondary clarifiers with 24.9% was obtained with ATS, whereas the cumulative SS load to the clarifiers is foreseen to be reduced with 22.5% for short rain events (4 hours duration) and with 16.6% for long rain events (24 hours duration). The SS distribution model is a useful tool for off-line studies of the potential benefits to be obtained by introducing ATS on a recirculating WWTP. Finally, the successful operation of the ATS process on the full-scale plant is illustrated with data.