Artículo

Bibliometric analysis of artificial intelligence algorithms used for microbial fuel cell research

Resumen

Research regarding microbial fuel cells has been stimulated to reduce the operating costs and energy consumption of conventional wastewater treatment, and increase its profitability. However, these devices are challenging to study due to their complexity and sensitivity to both internal and external factors. Artificial intelligence (AI) has been used to analyze microbial fuel cells as an effective alternative to the use of mathematical models, which are still in development. In this study, the main goals were to perform the first bibliometric analysis of AI applied to microbial fuel cell research and to find the most popular algorithms used to date. Using the Web of Science database, a total of 102 articles published between 1999 and 2022 were retrieved. The cumulative number of articles has greatly increased over the past 10 years. About 55% were contributed by researchers from China and USA, leading among 20 countries. Some 50 algorithms were used, with principal component analysis and feed-forward neural networks being the most popular used to study and optimize microbial fuel cells.
Li, Jia-Xuan (57941377800); Qu, Yu-Dun (58017598400); Xia, Chang-Liang (57362505400); Zhang, Wei (57803502700); Wang, Song-Song (58823455300); Ou, Shuan-Ji (57215274090); Yang, Yang (57215300085); Qi, Yong (56824877900); Xu, Chang-Peng (55602094800)
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2024
10.1007/s11033-024-09810-0
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85201332114&doi=10.1007%2fs11033-024-09810-0&partnerID=40&md5=88bb1dbe9f9a3b224ca2e94cec2e7c46
The Affiliated Guangdong Second Provincial General Hospital of Jinan University, No. 466 Xingang Road, Haizhu District, Guangdong, Guangzhou, 510317, China; Department of Orthopaedics, Southern Medical University, Guangdong, Guangzhou, China; School of Medicine, XiaMen University, Fujian, Xiamen, China
All Open Access; Hybrid Gold Open Access
Scopus
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