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Artificial Neural Networks (ANN) Approach to Modelling of Selected Nitrogen Forms Removal from Oily Wastewater in Anaerobic and Aerobic GSBR Process Phases

Department of Environmental Engineering Technology and Systems, Faculty of Civil and Environmental Engineering, Bialystok University of Technology, Bialystok 15-341, Poland
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Presented at Innovations-Sustainability-Modernity-Openness Conference (ISMO’19), Bialystok, Poland, 22–23 May 2019.
Proceedings 2019, 16(1), 16; https://doi.org/10.3390/proceedings2019016016
Published: 13 June 2019
PDF [271 KB, uploaded 13 June 2019]

Abstract

The paper presents artificial neural network models approximating concentration of selected nitrogen forms in wastewater after sequence bath reactor with aerobic granular activated sludge (GSBR) anaerobic and aerobic phase. Developed models reflected all the changes in concentration of studied nitrogen forms (r = 0.996–0.999). In models approximating Total N and N-NH4, variable most influencing calculations was nitrogen form at the beginning of anaerobic or aerobic phase.
Keywords: GSBR; ANN model; nitrogen removal GSBR; ANN model; nitrogen removal
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Ofman, P.; Struk-Sokołowska, J. Artificial Neural Networks (ANN) Approach to Modelling of Selected Nitrogen Forms Removal from Oily Wastewater in Anaerobic and Aerobic GSBR Process Phases. Proceedings 2019, 16, 16.

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