A Data Mining Approach to the Prediction of Food-to-Mass Ratio and Mixed Liquor Suspended Solids
Bartosz Szeląg1, Jan Studziński2
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1Faculty of Environmental, Geomatic and Energy Engineering, Kielce University of Technology
Tysiąclecia Państwa Polskiego Av. 7, 25-314 Kielce, Poland
2Systems Research Institute PAN Newelska Street 6, 01-447 Warsaw, Poland
Submission date: 2016-11-14
Final revision date: 2017-01-11
Acceptance date: 2017-01-12
Online publication date: 2017-09-28
Publication date: 2017-09-28
Pol. J. Environ. Stud. 2017;26(5):2231–2238
This paper presents methodology for constructing a statistical model to forecast food-to-mass ratio (F/M). In the model, wastewater inflow (Q), biochemical oxygen demand (BOD5) and mixed liquor suspended solids (MLSS) were modelled separately using artificial neural networks (ANN) and multivariate adaptive regression splines (MARS). To compute the value of MLSS, the quality indicators of influent wastewater and the operational parameters of the bioreactor were used. It was examined whether it is possible to predict wastewater quality indicators that determine the values of F/M and MLSS on the basis of the wastewater inflow to the treatment plant. Computations performed demonstrated that ANN predictions of MLSS and F/M showed smaller errors than those obtained using the MARS method. Moreover, all developed models of wastewater quality indicators were considered as satisfactory.