TY  - JOUR
Y1  - 2024///
VL  - 346
A1  - Ardo, F.M.
A1  - Khoo, K.S.
A1  - Ahmad Sobri, M.Z.
A1  - Suparmaniam, U.
A1  - Ethiraj, B.
A1  - Anwar, A.F.
A1  - Lam, S.M.
A1  - Sin, J.C.
A1  - Shahid, M.K.
A1  - Ansar, S.
A1  - Ramli, A.
A1  - Lim, J.W.
JF  - Environmental Pollution
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85186399868&doi=10.1016%2fj.envpol.2024.123648&partnerID=40&md5=0dc159fcc6dfcab2d2f93284b1f23143
AV  - none
ID  - scholars19754
TI  - Modelling photoperiod in enhancing hydrogen production from Chlorella vulgaris sp. while bioremediating ammonium and organic pollutants in municipal wastewater
KW  - Chemical oxygen demand; Computer software; Hydrogen production; Machine learning; Microorganisms; Nitrogen; Polyurethanes; Rigid foamed plastics; Wastewater treatment
KW  -  Activated sludge systems; Chemical-oxygen demands; Chlorella vulgaris; Fermentative process; Light intensity; Machine-learning; Micro-algae; Municipal wastewaters; Performance; Polyurethane Foam
KW  -  Microalgae
KW  -  ammonia; chlorophyll a; chlorophyll b; hydrogen; polyurethan foam; ammonium derivative; hydrogen; nitrogen
KW  -  ammonium; bioremediation; chemical oxygen demand; fermentation; green alga; hydrogen; machine learning; organic pollutant; pollutant removal; wastewater; wastewater treatment
KW  -  activated sludge; analytic method; Article; bioremediation; chemical oxygen demand; Chlorella vulgaris; Clostridium beijerinckii; Clostridium butyricum; Clostridium tyrobutyricum; fermentation; flow rate; gas chromatography; growth rate; immobilization; light exposure; light intensity; machine learning; mathematical phenomena; microalga; municipal wastewater; nonhuman; persistent organic pollutant; photoperiodicity; photosynthesis; Ruminococcus; waste water management; biomass; microalga; photoperiodicity; sewage; wastewater
KW  -  Ammonium Compounds; Biomass; Chlorella vulgaris; Hydrogen; Microalgae; Nitrogen; Photoperiod; Sewage; Wastewater
N2  - Municipal wastewater is ubiquitously laden with myriad pollutants discharged primarily from a combination of domestic and industrial activities. These heterogeneous pollutants are threating the natural environments when the traditional activated sludge system fails sporadically to reduce the pollutants' toxicities. Besides, the activated sludge system is very energy intensive, bringing conundrums for decarbonization. This research endeavoured to employ Chlorella vulgaris sp. In converting pollutants from municipal wastewater into hydrogen via alternate light and dark fermentative process. The microalgae in attached form onto 1 cm3 of polyurethane foam cubes were adopted in optimizing light intensity and photoperiod during the light exposure duration. The highest hydrogen production was recorded at 52 mL amidst the synergistic light intensity and photoperiod of 200 Ī¼molmā??2sā??1 and 12:12 h (light:dark h), respectively. At this lighting condition, the removals of chemical oxygen demand (COD) and ammoniacal nitrogen were both achieved at about 80. The sustainability of microalgal fermentative performances was verified in recyclability study using similar immobilization support material. There were negligible diminishments of hydrogen production as well as both COD and ammoniacal nitrogen removals after five cycles, heralding inconsequential microalgal cellsā?? washout from the polyurethane support when replacing the municipal wastewater medium at each cycle. The collected dataset was finally modelled into enhanced Monod equation aided by Python software tool of machine learning. The derived model was capable to predict the performances of microalgae to execute the fermentative process in producing hydrogen while subsisting municipal wastewater at arbitrary photoperiod. The enhanced model had a best fitting of R2 of 0.9857 as validated using an independent dataset. Concisely, the outcomes had contributed towards the advancement of municipal wastewater treatment via microalgal fermentative process in producing green hydrogen as a clean energy source to decarbonize the wastewater treatment facilities. Ā© 2024 Elsevier Ltd
N1  - cited By 0
ER  -