Using Data Mining to Predict Sludge and Filamentous Microorganism Sedimentation

Krzysztof Chmielowski , Adam Czekański , Aleksandra Leśniańska

Abstract

This study attempted to develop statistical regression models for predicting the settleability of activated sludge based on the quality of incoming sewage and on the identified dominant filamentous species. As part of the analyses conducted for the purpose, classification models are presented that enable identification of the respective filamentous microorganisms, based on the working parameters of the bioreactor and the quality of the influent. The study calculations demonstrated that the modeling methods based on artificial neural networks, random forests, and boost trees can be applied for the identification of filamentous microorganisms Microthrix parvicella, Nostocoida sp., and Thiotrix sp. in activated sludge chambers in the STP located in Sitkówka-Nowiny. The best predictive capacity, covering identification of the above-mentioned filamentous bacterial species in activated sludge chambers, was observed for statistical models obtained by the random forest method.
Author Krzysztof Chmielowski (FoEEaLS / DoSEaWM)
Krzysztof Chmielowski,,
- Department of Sanitary Engineering and Water Management
, Adam Czekański
Adam Czekański,,
-
, Aleksandra Leśniańska
Aleksandra Leśniańska,,
-
Journal seriesPolish Journal of Environmental Studies, ISSN 1230-1485, e-ISSN 2083-5906, (N/A 40 pkt)
Issue year2019
Vol28
No5
Pages3105-3113
Publication size in sheets0.5
Keywords in Englishactivated sludge, hybrid models, settleability
ASJC Classification2300 General Environmental Science; 2304 Environmental Chemistry
DOIDOI:10.15244/pjoes/94050
URL http://www.pjoes.com/Using-Data-Mining-to-Predict-Sludge-nand-Filamentous-Microorganism-nSedimentation,94050,0,2.html
Internal identifierWIŚIG/2019/45
Languageen angielski
Score (nominal)40
Score sourcejournalList
Publication indicators WoS Citations = 0; Scopus SNIP (Source Normalised Impact per Paper): 2017 = 0.631; WoS Impact Factor: 2018 = 1.186 (2) - 2018=1.3 (5)
Citation count*1 (2020-04-10)
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* presented citation count is obtained through Internet information analysis and it is close to the number calculated by the Publish or Perish system.
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