| Issue |
MATEC Web Conf.
Volume 120, 2017
International Conference on Advances in Sustainable Construction Materials & Civil Engineering Systems (ASCMCES-17)
|
|
|---|---|---|
| Article Number | 05001 | |
| Number of page(s) | 8 | |
| Section | Water and Environment | |
| DOI | https://doi.org/10.1051/matecconf/201712005001 | |
| Published online | 09 August 2017 | |
Contribution to optimize decision parameters in activated-sludge process using ANFIS model
1 USTHB, laboratory of faculty of chemistry, electrochemistry-corrosion, metallurgy and mineral chemistry, BP 32 El-Allia, pc 16111, Algeria
2 ENSTP, TPiTE Laboratory, Bp 32 cité sidi Garidi, Kouba, Algers, Algeria
3 ENP, Civil engineering, Material and Environment Laboratory, Bp 32 Hassen-Badi 16000, Algeria
* Corresponding author: a_lefkir06@yahoo.fr
The monitoring of activated sludge processes is difficult because of their slow dynamics, the complexity of their behavior. To obtain the desired level of performance in an activated sludge system, a proper balance must be maintained between aeration (energy consumption) and the quantity of sludge recirculation. The objective of this study is to determine the necessary amount of recirculated sludge in activated sludge process in the aim to reduce the energy consumption with respect of environmental standards. To achieving this objective, a comparative study between three models was performed. In order to reduce the complexity of activated-sludge process, the first is a linear model based on principal component analysis method (PCA), the second is an Adaptive Neural Fuzzy Inference System model (ANFIS), while the third is based on coupling of those two approaches. The best values of validation criterion obtained with the last method show the high performance of the hybrid model proposed…
© The Authors, published by EDP Sciences, 2017
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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