Journal
JOURNAL OF ENVIRONMENTAL MANAGEMENT
Volume 223, Issue -, Pages 1061-1067Publisher
ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
DOI: 10.1016/j.jenvman.2018.06.092
Keywords
Machine learning; Cost modelling; Energy; Waste water treatment plants (WWTPs)
Categories
Funding
- National Research Fund (FNR) in Luxembourg [7871388-EdWARDS]
- Luxembourg Institute of Science and Technology
- European Union through INTERREG IVB North-West Europe Programme [192G]
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Understanding the energy cost structure of wastewater treatment plants is a relevant topic for plant managers due to the high energy costs and significant saving potentials. Currently, energy cost models are generally generated using logarithmic, exponential or linear functions that could produce not accurate results when the relationship between variables is highly complex and non-linear. In order to overcome this issue, this paper proposes a new methodology based on machine-learning algorithms that perform better with complex datasets. In this paper, machine learning was used to generate high-performing energy cost models for wastewater treatment plants, using a database of 317 wastewater treatment plants located in north-west Europe. The most important variables in energy cost modelling were identified and for the first time, the energy price was used as model parameter and its importance evaluated.
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