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- Title
Contaminant Removal from Wastewater by Microalgal Photobioreactors and Modeling by Artificial Neural Network.
- Authors
Mojiri, Amin; Ozaki, Noriatsu; Kazeroon, Reza Andasht; Rezania, Shahabaldin; Baharlooeian, Maedeh; Vakili, Mohammadtaghi; Farraji, Hossein; Ohashi, Akiyoshi; Kindaichi, Tomonori; Zhou, John L.
- Abstract
The potential of microalgal photobioreactors in removing total ammonia nitrogen (TAN), chemical oxygen demand (COD), caffeine (CAF), and N,N-diethyl-m-toluamide (DEET) from synthetic wastewater was studied. Chlorella vulgaris achieved maximum removal of 62.2% TAN, 52.8% COD, 62.7% CAF, and 51.8% DEET. By mixing C. vulgaris with activated sludge, the photobioreactor showed better performance, removing 82.3% TAN, 67.7% COD, 85.7% CAF, and 73.3% DEET. Proteobacteria, Bacteroidetes, and Chloroflexi were identified as the dominant phyla in the activated sludge. The processes were then optimized by the artificial neural network (ANN). High R2 values (>0.99) and low mean squared errors demonstrated that ANN could optimize the reactors' performance. The toxicity testing showed that high concentrations of contaminants (>10 mg/L) and long contact time (>48 h) reduced the chlorophyll and protein contents in microalgae. Overall, a green technology for wastewater treatment using microalgae and bacteria consortium has demonstrated its high potentials in sustainable management of water resources.
- Subjects
PHOTOBIOREACTORS; WATER management; SEWAGE; WASTEWATER treatment; CHEMICAL oxygen demand; ACTIVATED sludge process
- Publication
Water (20734441), 2022, Vol 14, Issue 24, p4046
- ISSN
2073-4441
- Publication type
Article
- DOI
10.3390/w14244046