Towards model predictive control: online predictions of ammonium and nitrate removal by using a stochastic ASM.
Water Sci Technol
; 79(1): 51-62, 2019 Jan.
Article
de En
| MEDLINE
| ID: mdl-30816862
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 â¼10% and â¼20% 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.
Texte intégral:
1
Collection:
01-internacional
Base de données:
MEDLINE
Sujet principal:
Pollution de l'eau
/
Élimination des déchets liquides
/
Composés d'ammonium
/
Modèles chimiques
/
Nitrates
Type d'étude:
Prognostic_studies
/
Risk_factors_studies
Langue:
En
Journal:
Water Sci Technol
Sujet du journal:
SAUDE AMBIENTAL
/
TOXICOLOGIA
Année:
2019
Type de document:
Article
Pays d'affiliation:
Danemark