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

Amin Mojiri, Noriatsu Ozaki, Reza Andasht Kazeroon, Shahabaldin Rezania, Maedeh Baharlooeian, Mohammadtaghi Vakili, Hossein Farraji, Akiyoshi Ohashi, Tomonori Kindaichi, John L. Zhou

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


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.

Original languageEnglish
Article number4046
JournalWater (Switzerland)
Issue number24
StatePublished - Dec 2022


  • bacteria
  • caffeine
  • DEET
  • emerging contaminants
  • microalgae


Dive into the research topics of 'Contaminant Removal from Wastewater by Microalgal Photobioreactors and Modeling by Artificial Neural Network'. Together they form a unique fingerprint.

Cite this