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

Abstract 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.

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Integrated model predictive control of water resource recovery facilities and sewer systems in a smart grid: example of full-scale implementation in Kolding

Water Science & Technology ◽

10.2166/wst.2020.266 ◽

2020 ◽

Vol 81 (8) ◽

pp. 1766-1777 ◽

Cited By ~ 2

Author(s):

P. A. Stentoft ◽

L. Vezzaro ◽

P. S. Mikkelsen ◽

M. Grum ◽

T. Munk-Nielsen ◽

...

Keyword(s):

Smart Grid ◽

Model Predictive Control ◽

Power Consumption ◽

Water Resource ◽

Predictive Control ◽

Storage Capacity ◽

Resource Recovery ◽

Integrated Model ◽

Full Scale ◽

Sewer System

Abstract An integrated model predictive control (MPC) strategy to control the power consumption and the effluent quality of a water resource recovery facility (WRRF) by utilizing the storage capacity from the sewer system was implemented and put into operation for a 7-day trial period. This price-based MPC reacted to electricity prices and forecasted pollutant loads 24 hours ahead. The large storage capacity available in the sewer system directly upstream from the plant was used to control the incoming loads and, indirectly, the power consumption of the WRRF during dry weather operations. The MPC balances electricity costs and treatment quality based on linear dynamical models and predictions of storage capacity and effluent concentrations. This article first shows the modelling results involved in the design of this MPC. Secondly, results from full-scale MPC operation of the WRRF are shown. The monetary savings of the MPC strategy for the specific plant were quantified around approximately 200 DKK per day when fully exploiting the allowed storage capacity. The developed MPC strategy provides a new option for linking WRRFs to smart grid electricity systems.

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