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Modeling Aeration Efficiency of Stepped Cascades by Using ANFIS
Author(s) -
Baylar Ahmet,
Hanbay Davut,
Ozpolat Emrah
Publication year - 2007
Publication title -
clean – soil, air, water
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.444
H-Index - 66
eISSN - 1863-0669
pISSN - 1863-0650
DOI - 10.1002/clen.200700019
Subject(s) - aeration , cascade , adaptive neuro fuzzy inference system , environmental engineering , flow (mathematics) , environmental science , process engineering , materials science , computer science , fuzzy logic , waste management , engineering , chemical engineering , mechanics , fuzzy control system , physics , artificial intelligence
The physical process of oxygen transfer or oxygen absorption from the atmosphere acts to replenish the used oxygen, a process termed re‐aeration or aeration. Aeration enhancement by macro‐roughness is well‐known in water treatment and one form is the aeration cascade. The macro‐roughness of the steps significantly reduces the flow velocities and leads to flow aeration along the stepped cascade. In this paper, the aeration efficiency in stepped cascade aerators was modeled by using the Adaptive Network Based Fuzzy Inference System (ANFIS). The obtained model was tested with experimental data. Test results showed that ANFIS can be used to estimate the aeration efficiency in stepped cascade aerators.