Conference paper · Journal article
Model Predictive Control of Stochastic Wastewater Treatment Process for Smart Power, Cost-Effective Aeration
Department of Applied Mathematics and Computer Science, Technical University of Denmark1
Dynamical Systems, Department of Applied Mathematics and Computer Science, Technical University of Denmark2
Krüger Veolia Water Technologies3
Urban Water Systems, Department of Environmental Engineering, Technical University of Denmark4
Department of Environmental Engineering, Technical University of Denmark5
CITIES - Centre for IT-Intelligent Energy Systems, Centers, Technical University of Denmark6
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.
| Language: | English |
|---|---|
| Publisher: | Elsevier BV |
| Year: | 2019 |
| Pages: | 622-627 |
| Proceedings: | 12th IFAC Symposium on Dynamics and Control of Process SystemsIFAC Symposium on Dynamics and Control of Process Systems, including Biosystems |
| ISSN: | 24058963 and 24058971 |
| Types: | Conference paper and Journal article |
| DOI: | 10.1016/j.ifacol.2019.06.132 |
| ORCIDs: | Stentoft, Peter Alexander , Guericke, Daniela , Mikkelsen, Peter Steen , Madsen, Henrik , Vezzaro, Luca and Møller, Jan Kloppenborg |
