Prediction of wastewater quality indicators at the inflow to the wastewater treatment plant using data mining methods

In the study, models developed using data mining methods are proposed for predicting wastewater quality indicators: biochemical and chemical oxygen demand, total suspended solids, total nitrogen and total phosphorus at the inflow to wastewater treatment plant (WWTP). The models are based on values m... Ausführliche Beschreibung

1. Person: Szeląg Bartosz verfasserin
Weitere Personen: Barbusiński Krzysztof verfasserin; Studziński Jan verfasserin; Bartkiewicz Lidia verfasserin
Quelle: In E3S Web of Conferences (01.01.2017)
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Format: Online-Artikel
Sprache: English
French
Veröffentlicht: 2017
Beschreibung: Online-Ressource
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  Creative Commons License Source: Directory of Open Access Journals (DOAJ).
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520 |a In the study, models developed using data mining methods are proposed for predicting wastewater quality indicators: biochemical and chemical oxygen demand, total suspended solids, total nitrogen and total phosphorus at the inflow to wastewater treatment plant (WWTP). The models are based on values measured in previous time steps and daily wastewater inflows. Also, independent prediction systems that can be used in case of monitoring devices malfunction are provided. Models of wastewater quality indicators were developed using MARS (multivariate adaptive regression spline) method, artificial neural networks (ANN) of the multilayer perceptron type combined with the classification model (SOM) and cascade neural networks (CNN). The lowest values of absolute and relative errors were obtained using ANN+SOM, whereas the MARS method produced the highest error values. It was shown that for the analysed WWTP it is possible to obtain continuous prediction of selected wastewater quality indicators using the two developed independent prediction systems. Such models can ensure reliable WWTP work when wastewater quality monitoring systems become inoperable, or are under maintenance. 
700 0 |a Barbusiński Krzysztof  |e verfasserin  |4 aut 
700 0 |a Studziński Jan  |e verfasserin  |4 aut 
700 0 |a Bartkiewicz Lidia  |e verfasserin  |4 aut 
773 0 8 |i In  |t E3S Web of Conferences  |g  (01.01.2017)  |w (DE-601)DOAJ000004839  |x 2267-1242 
856 4 0 |u http://dx.doi.org/10.1051/e3sconf/20172200174 
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856 4 0 |u https://doi.org/10.1051/e3sconf/20172200174 
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