Generation of bio-energy after optimization and controlling fluctuations using various sludge activated microbial fuel cell

• Published in: Environmental science and pollution research international 2023-12, Vol.30 (60), p.125077-125087
• Main Authors: Mandal, Snigdha, Sundaramurthy, Suresh, Arisutha, Suresh, Rene, Eldon Raj, Lens, Piet N. L., Zahmatkesh, Sasan, Amesho, Kassian T. T., Bokhari, Awais
• Format: Article
• Language: English
• Subjects: Activated sludge; Animal wastes; Applications of Emerging Green Technologies for Efficient Valorization of Agro-Industrial Waste: A Roadmap Towards Sustainable Environment and Circular Economy; Aquatic Pollution; Atmospheric Protection/Air Quality Control/Air Pollution; Biochemical fuel cells; Bioelectric Energy Sources; Bioelectricity; Biological Oxygen Demand Analysis; Cattle manure; Chemical oxygen demand; Comparative studies; Controllers; Earth and Environmental Science; Ecotoxicology; Electric power generation; Electricity; Electrodes; Environment; Environmental Chemistry; Environmental Health; Fuel cells; Fuel technology; Microorganisms; Parameter identification; Response surface methodology; Sewage - chemistry; Sewage sludge; Slurries; Waste Water Technology; Wastes; Water Management; Water Pollution Control
• ISSN: 1614-7499; 0944-1344; 1614-7499
• DOI: 10.1007/s11356-023-26344-3

An aerobic microbial fuel cell (MFC) was designed to produce bio-electricity using cow manure-pretreated slurry (CM) and sewage sludge (SS). A comparative study of parametric effects on power generation for various parameters like feed ratio of wastes, pH of anode media, and electrode depth was conducted. This experiment aimed to identify the most important system parameters and optimize them to develop a suitable controller for a stable output. Power production reached its maximum at an electrode depth of 7 cm, a pH of 6, and a feed ratio of 2:1 in the CM + SS system before applying the controller. Response surface methodology (RSM) was practiced to explore the relationships between various parameters and the response using MINITAB software. The regression equation of the most productive system deduced from the RSM result has an R2 value of 85.3%. The results show that an ON/OFF controller works satisfactorily in this study. The highest energy-generating setup has a chemical oxygen demand (COD) removal efficiency of 45%. The morphology and content of the used wastes indicate that they can be recycled in other applications.