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Performance Assessment of Wastewater Treatment Plant with Machine Learning Tools

Goran Volf orcid id orcid.org/0000-0002-7058-9012 ; Građevinski fakultet Sveučilišta u Rijeci, Rijeka, Hrvatska
Nataša Atanasova ; LEQUIA, Universitat de Girona, Girona, Espana


Puni tekst: hrvatski pdf 771 Kb

str. 25-36

preuzimanja: 421

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Sažetak

Wastewater treatment plants (WWTPs) with activated sludge are complex and dynamic systems whose management can be improved by using different modelling and prediction approaches to their work. A machine learning tool for the development of model trees was used in this paper in order to develop a model for chemical oxygen demand (COD) in the wastewater effluent from the WWTP with activated sludge. The data measured in both the influent and the effluent of WWTP were used for modelling. For the COD model the machine learning tool Weka and the algorithm for the development of model trees M5P were used. The obtained model has a high descriptive power and correlation coefficient and thus can be used for modelling purposes. Also, the purpose of this paper is to show the benefits of using machine learning tools for developing WWTP models.

Ključne riječi

wastewater treatment plant with activated sludge, machine learning, model trees, modelling, COD

Hrčak ID:

192797

URI

https://hrcak.srce.hr/192797

Podaci na drugim jezicima: hrvatski

Posjeta: 705 *