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
Affiliations
Amin Mojiri
Department of Civil and Environmental Engineering, Graduate School of Advanced Science and Engineering, Hiroshima University, Higashihiroshima 739-8527, Japan
Noriatsu Ozaki
Department of Civil and Environmental Engineering, Graduate School of Advanced Science and Engineering, Hiroshima University, Higashihiroshima 739-8527, Japan
Reza Andasht Kazeroon
Faculty of Civil Engineering, University Technology Mara (UiTM), Shah Alam 40450, Malaysia
Shahabaldin Rezania
Department of Environment and Energy, Sejong University, Seoul 05006, Republic of Korea
Maedeh Baharlooeian
Department of Marine Biology, Faculty of Marine Science and Oceanography, Khorramshahr University of Marine Science and Technology, Khorramshahr 64199-34619, Iran
Mohammadtaghi Vakili
Green Intelligence Environmental School, Yangtze Normal University, Chongqing 408100, China
Hossein Farraji
School of Physical and Chemical Sciences, University of Canterbury, Christchurch 8140, New Zealand
Akiyoshi Ohashi
Department of Civil and Environmental Engineering, Graduate School of Advanced Science and Engineering, Hiroshima University, Higashihiroshima 739-8527, Japan
Tomonori Kindaichi
Department of Civil and Environmental Engineering, Graduate School of Advanced Science and Engineering, Hiroshima University, Higashihiroshima 739-8527, Japan
John L. Zhou
Centre for Green Technology, School of Civil and Environmental Engineering, University of Technology Sydney, 15 Broadway, Sydney, NSW 2007, Australia
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.
