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

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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 languageEnglish
Title of host publicationProceedings of Dycops 2019
Number of pages6
Publication date2019
Publication statusPublished - 2019
Event12th IFAC Symposium on Dynamics and Control of Process Systems - Jurerê Beach Village Hotel, Florianópolis , Brazil
Duration: 23 Apr 201926 Apr 2019
Conference number: 12
https://dycopscab2019.sites.ufsc.br/

Conference

Conference12th IFAC Symposium on Dynamics and Control of Process Systems
Number12
LocationJurerê Beach Village Hotel
CountryBrazil
CityFlorianópolis
Period23/04/201926/04/2019
Internet address

Keywords

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

Cite this

@inproceedings{862052ed824e4c93a27dca5931b04625,
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}",
year = "2019",
language = "English",
booktitle = "Proceedings of Dycops 2019",

}

Stentoft, PA, Guericke, D, Munk-Nielsen, T, Mikkelsen, PS, Madsen, H, Vezzaro, L & Møller, JK 2019, Model Predictive Control of Stochastic Wastewater Treatment Process for Smart Power, Cost-Effective Aeration. in Proceedings of Dycops 2019 . 12th IFAC Symposium on Dynamics and Control of Process Systems, Florianópolis , Brazil, 23/04/2019.

Model Predictive Control of Stochastic Wastewater Treatment Process for Smart Power, Cost-Effective Aeration. / Stentoft, Peter Alexander; Guericke, Daniela; Munk-Nielsen, Thomas; Mikkelsen, Peter Steen; Madsen, Henrik; Vezzaro, Luca; Møller, Jan Kloppenborg.

Proceedings of Dycops 2019 . 2019.

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

TY - GEN

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.

KW - Predictive Control

KW - Stochastic Systems

KW - Genetic Algorithms

KW - Process Control

KW - Wastewater Treatment

KW - Smart Power Applications

M3 - Article in proceedings

BT - Proceedings of Dycops 2019

ER -