Towards Model Predictive Control: Online Predictions of Ammonium and Nitrate Removal by using a Stochastic ASM

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Abstract

Online Model Predictive Control of WRRF requires simple and fast models to improve the operation of energy-demanding processes, such as aeration for nitrogen removal. Selected elements of the ASM1 modelling framework for ammonium and nitrate removal were included in discretely observed Stochastic differential equations. 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 model predictive control of the aeration process. Ultimately this can reduce electricity demand and cost for water resource recovery, allowing the prioritization of aeration in low electricity price periods.
Original languageEnglish
Title of host publicationProceedings of the 6th IWA/WEF Water Resource Recovery Modelling Seminar
Number of pages16
Publication date2018
Article numberwst2018527
Publication statusPublished - 2018
Event6th IWA/WEF Water Resource Recovery Modelling Seminar (WRRmod 2018) - Quebec, Canada
Duration: 10 Mar 201814 Mar 2018
Conference number: 6

Conference

Conference6th IWA/WEF Water Resource Recovery Modelling Seminar (WRRmod 2018)
Number6
CountryCanada
CityQuebec
Period10/03/201814/03/2018

Keywords

  • ASP
  • Grey-box model
  • MPC
  • Prediction
  • Stochastic Differential Equations

Cite this

@inproceedings{5dfbce2b1b5849189a5f2f6dee3160af,
title = "Towards Model Predictive Control: Online Predictions of Ammonium and Nitrate Removal by using a Stochastic ASM",
abstract = "Online Model Predictive Control of WRRF requires simple and fast models to improve the operation of energy-demanding processes, such as aeration for nitrogen removal. Selected elements of the ASM1 modelling framework for ammonium and nitrate removal were included in discretely observed Stochastic differential equations. 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 model predictive control of the aeration process. Ultimately this can reduce electricity demand and cost for water resource recovery, allowing the prioritization of aeration in low electricity price periods.",
keywords = "ASP, Grey-box model, MPC, Prediction, Stochastic Differential Equations",
author = "Stentoft, {Peter Alexander} and Thomas Munk-Nielsen and Luca Vezzaro and Henrik Madsen and Mikkelsen, {Peter Steen} and M{\o}ller, {Jan Kloppenborg}",
year = "2018",
language = "English",
booktitle = "Proceedings of the 6th IWA/WEF Water Resource Recovery Modelling Seminar",

}

Stentoft, PA, Munk-Nielsen, T, Vezzaro, L, Madsen, H, Mikkelsen, PS & Møller, JK 2018, Towards Model Predictive Control: Online Predictions of Ammonium and Nitrate Removal by using a Stochastic ASM. in Proceedings of the 6th IWA/WEF Water Resource Recovery Modelling Seminar., wst2018527, 6th IWA/WEF Water Resource Recovery Modelling Seminar (WRRmod 2018), Quebec, Canada, 10/03/2018.

Towards Model Predictive Control: Online Predictions of Ammonium and Nitrate Removal by using a Stochastic ASM. / Stentoft, Peter Alexander; Munk-Nielsen, Thomas; Vezzaro, Luca; Madsen, Henrik; Mikkelsen, Peter Steen; Møller, Jan Kloppenborg.

Proceedings of the 6th IWA/WEF Water Resource Recovery Modelling Seminar. 2018. wst2018527.

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

TY - GEN

T1 - Towards Model Predictive Control: Online Predictions of Ammonium and Nitrate Removal by using a Stochastic ASM

AU - Stentoft, Peter Alexander

AU - Munk-Nielsen, Thomas

AU - Vezzaro, Luca

AU - Madsen, Henrik

AU - Mikkelsen, Peter Steen

AU - Møller, Jan Kloppenborg

PY - 2018

Y1 - 2018

N2 - Online Model Predictive Control of WRRF requires simple and fast models to improve the operation of energy-demanding processes, such as aeration for nitrogen removal. Selected elements of the ASM1 modelling framework for ammonium and nitrate removal were included in discretely observed Stochastic differential equations. 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 model predictive control of the aeration process. Ultimately this can reduce electricity demand and cost for water resource recovery, allowing the prioritization of aeration in low electricity price periods.

AB - Online Model Predictive Control of WRRF requires simple and fast models to improve the operation of energy-demanding processes, such as aeration for nitrogen removal. Selected elements of the ASM1 modelling framework for ammonium and nitrate removal were included in discretely observed Stochastic differential equations. 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 model predictive control of the aeration process. Ultimately this can reduce electricity demand and cost for water resource recovery, allowing the prioritization of aeration in low electricity price periods.

KW - ASP

KW - Grey-box model

KW - MPC

KW - Prediction

KW - Stochastic Differential Equations

M3 - Article in proceedings

BT - Proceedings of the 6th IWA/WEF Water Resource Recovery Modelling Seminar

ER -

Stentoft PA, Munk-Nielsen T, Vezzaro L, Madsen H, Mikkelsen PS, Møller JK. Towards Model Predictive Control: Online Predictions of Ammonium and Nitrate Removal by using a Stochastic ASM. In Proceedings of the 6th IWA/WEF Water Resource Recovery Modelling Seminar. 2018. wst2018527