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

Piotr Ofman , Joanna Struk-Sokołowska

Abstract

Paper presents artificial neural network models (ANN) approximating concentration of selected nitrogen forms in wastewater after sequence batch reactor operating with aerobic granular activated sludge (GSBR) in the anaerobic and aerobic phases. Aim of the study was to determine parameters conditioning effectiveness of selected nitrogen forms removal in GSBR reactor process phases. Models of artificial neural networks were developed separately for N-NH4, N-NO3 and total nitrogen concentration in particular process phases of GSBR reactor. In total, 6 ANN models were presented in this paper. ANN models were made as multilayer perceptron (MLP), which were learned using the Broyden-Fletcher-Goldfarb-Shanno algorithm. Developed ANN models indicated variables the most influencing of particular nitrogen forms in aerobic and anaerobic phase of GSBR reactor. Concentration of estimated nitrogen form at the beginning of anaerobic or aerobic phase, depending on ANN model, in all ANN models influenced approximated value. Obtained determination coefficients varied from 0.996 to 0.999 and were depending on estimated nitrogen form and GSBR process phase. Hence, developed ANN models can be used in further studies on modeling of nitrogen forms in anaerobic and aerobic phase of GSBR reactors.
Author Piotr Ofman (FCEE / DTSEE)
Piotr Ofman,,
- Department of Technologies and Systems in Environmental Engineering
, Joanna Struk-Sokołowska (FCEE / DTSEE)
Joanna Struk-Sokołowska,,
- Department of Technologies and Systems in Environmental Engineering
Journal seriesWater, ISSN 2073-4441, (N/A 70 pkt)
Issue year2019
Vol11
No8
Pages1-11
Publication size in sheets0.5
ASJC Classification1104 Aquatic Science; 1303 Biochemistry; 3305 Geography, Planning and Development; 2312 Water Science and Technology
DOIDOI:10.3390/w11081594
URL https://www.mdpi.com/2073-4441/11/8/1594
Internal identifierROC 19-20
Languageen angielski
LicenseJournal (articles only); published final; Other open licence; with publication
Score (nominal)70
Score sourcejournalList
ScoreMinisterial score = 70.0, 12-02-2020, ArticleFromJournal
Publication indicators Scopus SNIP (Source Normalised Impact per Paper): 2017 = 1.007; WoS Impact Factor: 2018 = 2.524 (2) - 2018=2.721 (5)
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