Model predictive control of stochastic wastewater treatment process for smart power, cost-effective aeration

Research output: Contribution to journalJournal articleResearchpeer-review

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 languageEnglish
JournalIFAC-PapersOnLine
Volume52
Issue number1
Pages (from-to)622-627
ISSN2405-8963
DOIs
Publication statusPublished - 2019

Cite this

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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.",
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}",
year = "2019",
doi = "10.1016/j.ifacol.2019.06.132",
language = "English",
volume = "52",
pages = "622--627",
journal = "IFAC-PapersOnLine",
issn = "2405-8963",
publisher = "Elsevier",
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TY - JOUR

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

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.

U2 - 10.1016/j.ifacol.2019.06.132

DO - 10.1016/j.ifacol.2019.06.132

M3 - Journal article

VL - 52

SP - 622

EP - 627

JO - IFAC-PapersOnLine

JF - IFAC-PapersOnLine

SN - 2405-8963

IS - 1

ER -