BIBLIOMETRIC ANALYSIS OF THE APPLICATION OF ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING IN MICROBIAL FUEL CELL OPTIMIZATION: EMERGING TRENDS AND RESEARCH OPPORTUNITIES

The document examines the application of artificial intelligence (AI) and machine learning (ML) in optimizing microbial fuel cells (MFCs), a promising technology for energy generation and wastewater treatment. It highlights how AI enhances the energy efficiency of MFCs by optimizing operational parameters, monitoring microbial communities, and designing advanced materials. A significant growth in scientific output on the topic is observed, with a 250% increase in publications over the last decade. China leads in the number of articles published, while the United States and the United Kingdom stand out for the impact of their research. The bibliometric analysis reveals that most publications are experimental studies, although there is an opportunity for review and synthesis studies. Additionally, maps of scientific collaboration, co-authorship, and keyword co-occurrence are presented, identifying emerging trends. AI has successfully increased energy efficiency by 20% and reduced costs through the use of new materials. However, industrial scalability remains a challenge. Finally, future research directions are identified, including the development of intelligent biofilms and the integration of MFCs into smart energy grids.