A combined support vector regression with a firefly algorithm for prediction of energy consumption in wastewater treatment plants
© 2024 The Authors This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (CC BY 4.0), which permits copying, adaptation and redistribution, provided the original work is properly cited (http://creativecommons.org/licenses/by/4.0/).
| Veröffentlicht in: | Water science and technology : a journal of the International Association on Water Pollution Research. - 1986. - 90(2024), 10 vom: 01. Nov., Seite 2747-2763 |
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| Weitere Verfasser: | , , , , |
| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2024
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| Zugriff auf das übergeordnete Werk: | Water science and technology : a journal of the International Association on Water Pollution Research |
| Schlagworte: | Journal Article Algeria energy consumption modeling firefly algorithm support vector regression wastewater treatment Wastewater |
| Zusammenfassung: | © 2024 The Authors This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (CC BY 4.0), which permits copying, adaptation and redistribution, provided the original work is properly cited (http://creativecommons.org/licenses/by/4.0/). Wastewater treatment plants (WWTPs) comprise energy-intensive processes, serving as primary contributors to overall WWTP costs. This research study proposes a novel approach that integrates support vector regression (SVR) with the firefly algorithm (FFA) for the prediction of energy consumption in a WWTP in Chlef City, Algeria. The database comprises a comprehensive set of 1,653 samples, capturing diverse information categories. It includes chemical and physical characteristics, encompassing chemical oxygen demand, 5-day biochemical oxygen demand, potential of hydrogen, water temperature, total suspended sediment in water and basin, influent N-NH3 concentration, number of aerators, and operating time. Additionally, the hydraulic and energy-related parameters are represented by the flow entered at the station and the energy consumed by aerators, respectively. Finally, meteorological data, comprising rainfall, temperature, relative humidity, and the aridity index, are part of the dataset required for analysis. In this regard, 15 different models that correspond to 15 different combinations of input parameters are assessed in this study. The results show that the SVR-FFA-15 can render an improvement in the prediction accuracy of energy consumption in WWTPs. This study provides a useful tool for managing the energy consumption of wastewater treatment and makes insightful recommendations for future energy savings |
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| Beschreibung: | Date Completed 29.11.2024 Date Revised 29.11.2024 published: Print-Electronic Citation Status MEDLINE |
| ISSN: | 0273-1223 |
| DOI: | 10.2166/wst.2024.375 |