E3S Web Conf.
Volume 22, 2017International Conference on Advances in Energy Systems and Environmental Engineering (ASEE17)
|Number of page(s)||8|
|Published online||07 November 2017|
Prediction of wastewater quality indicators at the inflow to the wastewater treatment plant using data mining methods
Kielce University of Technology, Faculty of Environmental, Geomatic and Energy Engineering, Tysiąclecia Państwa Polskiego Av. 7, Kielce, Poland
2 Silesian University of Technology, Institute of Water and Wastewater Engineering, Konarskiego Street 18, Gliwice, Poland
3 Systems Research Institute Polish Academy of Science, Newelska Street 6, Warszawa, Poland
* Corresponding author: firstname.lastname@example.org
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.
© The Authors, published by EDP Sciences, 2017
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (http://creativecommons.org/licenses/by/4.0/).
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