Prediction of Wastewater Quality at a Wastewater Treatment Plant Inlet Using a System Based on Machine Learning Methods - Publication - Bridge of Knowledge

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Prediction of Wastewater Quality at a Wastewater Treatment Plant Inlet Using a System Based on Machine Learning Methods

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.

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DOI:
Digital Object Identifier (open in new tab) 10.3390/pr10010085
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Category:
Articles
Type:
artykuły w czasopismach
Published in:
Processes no. 10, pages 85 - 99,
ISSN: 2227-9717
Language:
English
Publication year:
2022
Bibliographic description:
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// Processes -Vol. 10,iss. 1 (2022), s.85-99
DOI:
Digital Object Identifier (open in new tab) 10.3390/pr10010085
Verified by:
Gdańsk University of Technology

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