Model Predictive Control of Stochastic Wastewater Treatment Process for Smart Power, Cost-Effective Aeration

Peter Alexander Stentoft; Daniela Guericke; Thomas Munk-Nielsen; Peter Steen Mikkelsen; Henrik Madsen; Luca Vezzaro; Jan Kloppenborg Møller

doi:10.1016/j.ifacol.2019.06.132

Model Predictive Control of Stochastic Wastewater Treatment Process for Smart Power, Cost-Effective Aeration

Peter Alexander Stentoft, Daniela Guericke, Thomas Munk-Nielsen, Peter Steen Mikkelsen, Henrik Madsen, Luca Vezzaro, Jan Kloppenborg Møller

Krüger Veolia Water Technologies

Research output: Contribution to journal › Conference article › Research › peer-review

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Abstract

Wastewater treatment is an essential process to ensure the good chemical and environmental status of natural water bodies. The energy consumption for wastewater treatment represents an important cost for water utilities. Meanwhile has the increasing fraction of renewable energy sources in the electricity market created the possibility of exploiting cheaper (and greener) electricity. This paper proposes model predictive control driven by stochastic differential equations and genetic optimization to prioritize aeration in periods with low electricity prices thereby reducing costs and empowering smart use of green electricity. This is without violation of legislation and equipment constraints. The method is tested with real plant data and electricity market prices to demonstrate efficiency and feasibility.

Original language	English
Book series	IFAC-PapersOnLine
Volume	52
Issue number	1
Pages (from-to)	622-627
ISSN	2405-8963
DOIs	https://doi.org/10.1016/j.ifacol.2019.06.132
Publication status	Published - 2019
Event	12th IFAC Symposium on Dynamics and Control of Process Systems - Jurerê Beach Village Hotel, Florianópolis , Brazil Duration: 23 Apr 2019 → 26 Apr 2019 Conference number: 12 https://dycopscab2019.sites.ufsc.br/

Conference

Conference	12th IFAC Symposium on Dynamics and Control of Process Systems
Number	12
Location	Jurerê Beach Village Hotel
Country/Territory	Brazil
City	Florianópolis
Period	23/04/2019 → 26/04/2019
Internet address	https://dycopscab2019.sites.ufsc.br/

Keywords

Predictive Control
Stochastic Systems
Genetic Algorithms
Process Control
Wastewater Treatment
Smart Power Applications

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1016/j.ifacol.2019.06.132

Stentoft et al Dycops finalAccepted author manuscript, 247 KB

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title = "Model Predictive Control of Stochastic Wastewater Treatment Process for Smart Power, Cost-Effective Aeration",

abstract = "Wastewater treatment is an essential process to ensure the good chemical and environmental status of natural water bodies. The energy consumption for wastewater treatment represents an important cost for water utilities. Meanwhile has the increasing fraction of renewable energy sources in the electricity market created the possibility of exploiting cheaper (and greener) electricity. This paper proposes model predictive control driven by stochastic differential equations and genetic optimization to prioritize aeration in periods with low electricity prices thereby reducing costs and empowering smart use of green electricity. This is without violation of legislation and equipment constraints. The method is tested with real plant data and electricity market prices to demonstrate efficiency and feasibility.",

keywords = "Predictive Control, Stochastic Systems, Genetic Algorithms, Process Control, Wastewater Treatment, Smart Power Applications",

author = "Stentoft, {Peter Alexander} and Daniela Guericke and Thomas Munk-Nielsen and Mikkelsen, {Peter Steen} and Henrik Madsen and Luca Vezzaro and M{\o}ller, {Jan Kloppenborg}",

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language = "English",

volume = "52",

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T1 - Model Predictive Control of Stochastic Wastewater Treatment Process for Smart Power, Cost-Effective Aeration

AU - Stentoft, Peter Alexander

AU - Guericke, Daniela

AU - Munk-Nielsen, Thomas

AU - Mikkelsen, Peter Steen

AU - Madsen, Henrik

AU - Vezzaro, Luca

AU - Møller, Jan Kloppenborg

N1 - Conference code: 12

PY - 2019

Y1 - 2019

N2 - Wastewater treatment is an essential process to ensure the good chemical and environmental status of natural water bodies. The energy consumption for wastewater treatment represents an important cost for water utilities. Meanwhile has the increasing fraction of renewable energy sources in the electricity market created the possibility of exploiting cheaper (and greener) electricity. This paper proposes model predictive control driven by stochastic differential equations and genetic optimization to prioritize aeration in periods with low electricity prices thereby reducing costs and empowering smart use of green electricity. This is without violation of legislation and equipment constraints. The method is tested with real plant data and electricity market prices to demonstrate efficiency and feasibility.

AB - Wastewater treatment is an essential process to ensure the good chemical and environmental status of natural water bodies. The energy consumption for wastewater treatment represents an important cost for water utilities. Meanwhile has the increasing fraction of renewable energy sources in the electricity market created the possibility of exploiting cheaper (and greener) electricity. This paper proposes model predictive control driven by stochastic differential equations and genetic optimization to prioritize aeration in periods with low electricity prices thereby reducing costs and empowering smart use of green electricity. This is without violation of legislation and equipment constraints. The method is tested with real plant data and electricity market prices to demonstrate efficiency and feasibility.

KW - Predictive Control

KW - Stochastic Systems

KW - Genetic Algorithms

KW - Process Control

KW - Wastewater Treatment

KW - Smart Power Applications

U2 - 10.1016/j.ifacol.2019.06.132

DO - 10.1016/j.ifacol.2019.06.132

M3 - Conference article

SN - 2405-8963

VL - 52

SP - 622

EP - 627

JO - IFAC-PapersOnLine

JF - IFAC-PapersOnLine

IS - 1

T2 - 12th IFAC Symposium on Dynamics and Control of Process Systems

Y2 - 23 April 2019 through 26 April 2019

ER -

Model Predictive Control of Stochastic Wastewater Treatment Process for Smart Power, Cost-Effective Aeration

Abstract

Conference

Keywords

UN SDGs

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