Contaminant Removal from Wastewater by Microalgal Photobioreactors and Modeling by Artificial Neural Network

Autor: Amin Mojiri, Noriatsu Ozaki, Reza Andasht Kazeroon, Shahabaldin Rezania, Maedeh Baharlooeian, Mohammadtaghi Vakili, Hossein Farraji, Akiyoshi Ohashi, Tomonori Kindaichi, John L. Zhou
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Zdroj: Water, Vol 14, Iss 24, p 4046 (2022)
Druh dokumentu: article
ISSN: 2073-4441
DOI: 10.3390/w14244046
Popis: 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.
Databáze: Directory of Open Access Journals