Effluent concentration prediction using an artificial neural network technique in dissolved aeration flotation systems
Author:
Funder
Dokuz Eylül Üniversitesi
T.C. Küçük ve Orta Ölçekli İşletmeleri Geliştirme ve Destekleme İdaresi Başkanlığı
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s13762-024-05740-3.pdf
Reference32 articles.
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2. Al-Dulaimi SL, Al-Yaqoobi AM (2021) Separation of oil/water emulsions by microbubble air flotation. IOP Conf Ser: Mater Sci Eng 1076:1–12. https://doi.org/10.1088/1757-899X/1076/1/012030
3. Alsulaili A, Refaie A (2021) Artificial neural network modeling approach for the prediction of five-day biological oxygen demand and treatment plant performance. Water Supply 21(5):1861–1877. https://doi.org/10.2166/ws.2020.199
4. Ansari FA, Nasr M, Rawat I, Bux F (2021) Artificial neural network and techno-economic estimation with algae-based tertiary Water treatment. J Water Process Eng 40:1–12. https://doi.org/10.1016/j.jwpe.2020.101761
5. Asgari G, Shabanloo A, Salari M, Eslami F (2020) Sonophotocatalytic treatment of AB113 dye and real textile water using ZnO/persulfate: modeling by response surface methodology and artificial neural network. Environ Res 184:1–18. https://doi.org/10.1016/j.envres.2020.109367
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