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2017 | 26 | 5 |

Tytuł artykułu

A data mining approach to the prediction of food-to-mass ratio and mixed liquor suspended solids

Warianty tytułu

Języki publikacji

EN

Abstrakty

EN
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

Słowa kluczowe

Wydawca

-

Rocznik

Tom

26

Numer

5

Opis fizyczny

p.2231-2238,fig.,ref.

Twórcy

autor
  • Faculty of Environmental, Geomatic and Energy Engineering, Kielce University of Technology Tysiaclecia Panstwa Polskiego Av. 7, 25-314 Kielce, Poland
  • Systems Research Institute PAN Newelska Street 6, 01-447 Warsaw, Polan

Bibliografia

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  • 7. ZEALAND J., RUSSELL M. Simple control equations using F:M ratio for operation of activated sludge aeration basins. Environmental Technology Letters 5 (1-11), 49, 1984.
  • 8. DYMACZEWSKI Z., JAROSZYŃSKI T., JEŻ-WALKOWIAK J., KAUFMAN-KOMOROWSKA M., MICHAŁKIEWICZ M., NIEDZIELSKI W., SOZAŃSKI M. Poradnik eksploatatora oczyszczalni ścieków, wydanie 3 rozszerzone. PZITS, Poznań, 2011.
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  • 12. RAVIKUMAR R., RENUKA K., SINDHU V., MALARMATHI K.B. Response Surface Methodology and Artificial Neural Network for Modeling and Optimization of Distillery Spent Wash Treatment Using Phormidium valderianum BDU 140441. Pol. J. Environ. Stud. 22 (4), 1143, 2013.
  • 13. BUIL H.M., DUONG H. T.G., NGUYEN C.D. Applying an Artificial Neural Network to Predict Coagulation Capacity of Reactive Dyeing Wastewater by Chitosan. Pol. J. Environ. Stud. 25 (2), 545, 2016.
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  • 15. SOBOTA J., SZETELA R. Jednowymiarowe modele natężenia i jakości ścieków dopływających do oczyszczalni komunalnych. Ochrona Środowiska 27 (1), 15, 2005.
  • 16. STUDZIŃSKI J., BARTKIEWICZ L., STACHURA M. Development of mathematical models for forecasting hydraulic loads of water and wastewater networks. EnviroInfo’2013: Environmental Informatics and Renewable Energies, 1, Aachen, 2013.
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  • 18. SHARDA V.N., PRASHER S.O., PATEL R.M., OJASVI P.R., PRAKASH C. Performance of Multivariate Adaptive Regression Splines (MARS) in predicting runoff in mid-Himalayan microwatersheds with limited data. Hydrol. Sci. J. 53 (6), 1165, 2008.
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Typ dokumentu

Bibliografia

Identyfikatory

Identyfikator YADDA

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