LAPSE:2023.3176
Published Article

LAPSE:2023.3176
Prediction of Wastewater Quality at a Wastewater Treatment Plant Inlet Using a System Based on Machine Learning Methods
February 22, 2023
Abstract
One of the important factors determining the biochemical processes in bioreactors is the quality of the wastewater inflow to the wastewater treatment plant (WWTP). Information on the quality of wastewater, sufficiently in advance, makes it possible to properly select bioreactor settings to obtain optimal process conditions. This paper presents the use of classification models to predict the variability of wastewater quality at the inflow to wastewater treatment plants, the values of which depend only on the amount of inflowing wastewater. The methodology of an expert system to predict selected indicators of wastewater quality at the inflow to the treatment plant (biochemical oxygen demand, chemical oxygen demand, total suspended solids, and ammonium nitrogen) on the example of a selected WWTP—Sitkówka Nowiny, was presented. In the considered system concept, a division of the values of measured wastewater quality indices into lower (reduced values of indicators in relation to average), average (typical and most common values), and upper (increased values) were adopted. On the basis of the calculations performed, it was found that the values of the selected wastewater quality indicators can be identified with sufficient accuracy by means of the determined statistical models based on the support vector machines and boosted trees methods.
One of the important factors determining the biochemical processes in bioreactors is the quality of the wastewater inflow to the wastewater treatment plant (WWTP). Information on the quality of wastewater, sufficiently in advance, makes it possible to properly select bioreactor settings to obtain optimal process conditions. This paper presents the use of classification models to predict the variability of wastewater quality at the inflow to wastewater treatment plants, the values of which depend only on the amount of inflowing wastewater. The methodology of an expert system to predict selected indicators of wastewater quality at the inflow to the treatment plant (biochemical oxygen demand, chemical oxygen demand, total suspended solids, and ammonium nitrogen) on the example of a selected WWTP—Sitkówka Nowiny, was presented. In the considered system concept, a division of the values of measured wastewater quality indices into lower (reduced values of indicators in relation to average), average (typical and most common values), and upper (increased values) were adopted. On the basis of the calculations performed, it was found that the values of the selected wastewater quality indicators can be identified with sufficient accuracy by means of the determined statistical models based on the support vector machines and boosted trees methods.
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Keywords
boosted trees, classification, support vector machines, wastewater quality, wastewater treatment plant
Subject
Suggested Citation
Wodecka B, Drewnowski J, Białek A, Łazuka E, Szulżyk-Cieplak J. Prediction of Wastewater Quality at a Wastewater Treatment Plant Inlet Using a System Based on Machine Learning Methods. (2023). LAPSE:2023.3176
Author Affiliations
Wodecka B: Faculty of Law and Social Sciences, Jan Kochanowski University, Uniwersytecka 15 St., 25-405 Kielce, Poland [ORCID]
Drewnowski J: Faculty of Civil and Environmental Engineering, Gdansk University of Technology, Narutowicza 11/12, 80-233 Gdansk, Poland
Białek A: Faculty of Environmental, Geomatic and Energy Engineering, Kielce University of Technology, Tysiąclecia Państwa Polskiego 7, 25-314 Kielce, Poland
Łazuka E: Faculty of Technology Fundamentals, Lublin University of Technology, Nadbystrzycka 38, 20-618 Lublin, Poland [ORCID]
Szulżyk-Cieplak J: Faculty of Technology Fundamentals, Lublin University of Technology, Nadbystrzycka 38, 20-618 Lublin, Poland [ORCID]
Drewnowski J: Faculty of Civil and Environmental Engineering, Gdansk University of Technology, Narutowicza 11/12, 80-233 Gdansk, Poland
Białek A: Faculty of Environmental, Geomatic and Energy Engineering, Kielce University of Technology, Tysiąclecia Państwa Polskiego 7, 25-314 Kielce, Poland
Łazuka E: Faculty of Technology Fundamentals, Lublin University of Technology, Nadbystrzycka 38, 20-618 Lublin, Poland [ORCID]
Szulżyk-Cieplak J: Faculty of Technology Fundamentals, Lublin University of Technology, Nadbystrzycka 38, 20-618 Lublin, Poland [ORCID]
Journal Name
Processes
Volume
10
Issue
1
First Page
85
Year
2022
Publication Date
2022-01-01
ISSN
2227-9717
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Original Submission
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PII: pr10010085, Publication Type: Journal Article
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LAPSE:2023.3176
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https://doi.org/10.3390/pr10010085
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Feb 22, 2023
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