Modelling photoperiod in enhancing hydrogen production from Chlorella vulgaris sp. while bioremediating ammonium and organic pollutants in municipal wastewater

Fatima Musa Ardo; Kuan Shiong Khoo; Mohamad Zulfadhli Ahmad Sobri; Uganeeswary Suparmaniam; Baranitharan Ethiraj; Aliya Fathima Anwar; Sze Mun Lam; Jin Chung Sin; Muhammad Kashif Shahid; Sabah Ansar; Anita Ramli; Jun Wei Lim

doi:10.1016/j.envpol.2024.123648

Modelling photoperiod in enhancing hydrogen production from Chlorella vulgaris sp. while bioremediating ammonium and organic pollutants in municipal wastewater

Environ Pollut. 2024 Apr 1:346:123648. doi: 10.1016/j.envpol.2024.123648. Epub 2024 Feb 24.

Affiliations

¹ HICoE-Centre for Biofuel and Biochemical Research, Institute of Self-Sustainable Building, Department of Fundamental and Applied Sciences, Universiti Teknologi PETRONAS, 32610 Seri Iskandar, Perak Darul Ridzuan, Malaysia.
² Department of Chemical Engineering and Materials Science, Yuan Ze University, Taoyuan, Taiwan.
³ HICoE-Centre for Biofuel and Biochemical Research, Institute of Self-Sustainable Building, Department of Chemical Engineering, Universiti Teknologi PETRONAS, 32610 Seri Iskandar, Perak Darul Ridzuan, Malaysia.
⁴ Department of Biotechnology, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, India.
⁵ Department of Environmental Engineering, Faculty of Engineering and Green Technology, Universiti Tunku Abdul Rahman, Jalan Universiti, Bandar Barat, 31900 Kampar, Perak, Malaysia; College of Environmental Science and Engineering, Guilin University of Technology, Guilin, 541004, China.
⁶ Department of Petrochemical Engineering, Faculty of Engineering and Green Technology, Universiti Tunku Abdul Rahman, Jalan Universiti, Bandar Barat, 31900 Kampar, Perak, Malaysia; College of Environmental Science and Engineering, Guilin University of Technology, Guilin, 541004, China.
⁷ Research Institute of Environment & Biosystem, Chungnam National University, Yuseonggu, Daejeon 34134, Republic of Korea.
⁸ Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, King Saud University, P.O. Box 10219, Riyadh, 11433, Saudi Arabia.
⁹ HICoE-Centre for Biofuel and Biochemical Research, Institute of Self-Sustainable Building, Department of Fundamental and Applied Sciences, Universiti Teknologi PETRONAS, 32610 Seri Iskandar, Perak Darul Ridzuan, Malaysia; Centre for Herbal Pharmacology and Environmental Sustainability, Chettinad Hospital and Research Institute, Chettinad Academy of Research and Education, Kelambakkam, 603103, Tamil Nadu, India. Electronic address: junwei.lim@utp.edu.my.

PMID: 38408504
DOI: 10.1016/j.envpol.2024.123648

Abstract

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 μmolm^-2s^-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 cells' 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.

Keywords: Fermentation; Hydrogen; Machine learning; Microalgae; Polyurethane foam; Wastewater.

MeSH terms

Ammonium Compounds*
Biomass
Chlorella vulgaris*
Hydrogen
Microalgae*
Nitrogen
Photoperiod
Sewage
Wastewater

Substances

Wastewater
Sewage
Ammonium Compounds
Nitrogen
Hydrogen