Artificial Neural Networks (ANN) Approach to Modelling of Selected Nitrogen Forms Removal from Oily Wastewater in Anaerobic and Aerobic GSBR Process Phases †
Abstract
:1. Introduction
2. Materials and Methods
3. Results
4. Discussion
5. Conclusions
- The values approximated by the ANN models showed a good fit to the values observed both in the anaerobic phase and in the aerobic phase of the GSBR reactor. Prediction quality for presented models varied form r = 0.996 to r = 0.999.
- The BOD load in the wastewater inflowing to GSBR reactor had the greatest impact on the calculation algorithms approximating concentration of N-NO3 in the anaerobic and aerobic phases.
- The concentration of N-NH4 and total nitrogen showed the greatest sensitivity to the concentration of those nitrogen forms in wastewater.
- Activated sludge technological parameters did not affect significantly ANN algorithm calculation in anaerobic and aerobic phase. This phenomenon could be resulting from aerobic granular sludge tolerance to toxic agents in wastewater, in this case, oily substances. Therefore, influence of oily substances on wastewater treatment process has not been observed during experiment.
Author Contributions
Conflicts of Interest
References
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ANN Topology | Estimated N form | Variables rank | ANN Prediction Quality (r) | ANN Error | ||||
---|---|---|---|---|---|---|---|---|
BOD Load | N Form | ASA | ASI | SRT | ||||
MLP 5-6-1 | N-NO3 in anaerobic phase | 82.37 | 71.84 | 3.33 | 1.68 | 1.20 | 0.999 | 1.373 |
MLP 5-7-1 | N-NH4 in anaerobic phase | 22.77 | 1215.16 | 5.47 | 3.85 | 7.70 | 0.999 | 0.061 |
MLP 5-9-1 | Total N in anaerobic phase | 84.34 | 87.47 | 3.06 | 3.69 | 1.06 | 0.999 | 2.757 |
MLP 5-9-1 | N-NO3 in aerobic phase | 46.80 | 46.42 | 6.97 | 1.36 | 2.97 | 0.998 | 1.115 |
MLP 5-4-1 | N-NH4 in aerobic phase | 59.43 | 69.98 | 7.23 | 3.60 | 4.49 | 0.998 | 0.139 |
MLP 5-3-1 | Total N in aerobic phase | 30.37 | 30.75 | 2.58 | 2.12 | 2.67 | 0.996 | 3.950 |
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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. https://doi.org/10.3390/proceedings2019016016
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(1):16. https://doi.org/10.3390/proceedings2019016016
Chicago/Turabian StyleOfman, Piotr, and Joanna Struk-Sokołowska. 2019. "Artificial Neural Networks (ANN) Approach to Modelling of Selected Nitrogen Forms Removal from Oily Wastewater in Anaerobic and Aerobic GSBR Process Phases" Proceedings 16, no. 1: 16. https://doi.org/10.3390/proceedings2019016016