Application of artificial intelligence-based methods in bioelectrochemical systems: Recent progress and future perspectives

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@Article{LI:2023:jenvman,

• author = "Chunyan Li and Dongchao Guo and Yan Dang and Dezhi Sun and Pengsong Li",
• title = "Application of artificial intelligence-based methods in bioelectrochemical systems: Recent progress and future perspectives",
• journal = "Journal of Environmental Management",
• volume = "344",
• pages = "118502",
• year = "2023",
• ISSN = "0301-4797",
• DOI = "doi:10.1016/j.jenvman.2023.118502",
• URL = "https://www.sciencedirect.com/science/article/pii/S0301479723012902",
• keywords = "genetic algorithms, genetic programming, Artificial intelligence, Machine learning, Bioelectrochemical system, Microbial fuel cell, Microbial electrolysis cell",
• abstract = "Bioelectrochemical Systems (BESs) leverage microbial metabolic processes to either produce electricity by degrading organic matter or consume electricity to assist metabolism, and can be used for various applications such as energy production, wastewater treatment, and bioremediation. Given the intricate mechanisms of BESs, the application of artificial intelligence (AI)-based methods have been proposed to enhance the performance of BESs due to their capability to identify patterns and gain insights through data analysis. This review focuses on the analysis and comparison of AI algorithms commonly used in BESs, including artificial neural network (ANN), genetic programming (GP), fuzzy logic (FL), support vector regression (SVR), and adaptive neural fuzzy inference system (ANFIS). These algorithms have different features, such as ANN's simple network structure, GP's use in the training process, FL's human-like thought process, SVR's high prediction accuracy and robustness, and ANFIS's combination of ANN and FL features. The AI-based methods have been applied in BESs to predict microbial communities, products or substrates, and reactor performance, which can provide valuable information and improve system efficiency. Limitations of AI-based methods for predicting and optimizing BESs and recommendations for future development are also discussed. This review demonstrates the potential of AI-based methods in optimizing BESs and provides valuable information for the future development of this field",

}

Genetic Programming entries for Chunyan Li Dongchao Guo Yan Dang Dezhi Sun Pengsong Li

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