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在生物电化学系统中应用基于人工智能的方法:最近的进展和未来的前景

Chunyan Li¹, Dongchao Guo², Yan Dang¹

¹Beijing Key Lab for Source Control Technology of Water Pollution, College of Environmental Science and Engineering, Beijing Forestry University, Beijing, 100083, China; Engineering Research Center for Water Pollution Source Control & Eco-remediation, College of Environmental Science and Engineering, Beijing Forestry University, Beijing, 100083, China.

Journal of environmental management

|June 30, 2023

概括

人工智能 (AI) 方法通过分析复杂的数据来增强生物电化学系统 (BES),从而改善能源生产和废水处理. 这篇评论比较了像神经网络和模糊逻辑这样的AI算法,以优化BES性能.

科学领域:

环境科学环境科学
生物技术是生物技术.
计算机科学计算机科学

背景情况:

生物电化学系统 (BES) 使用微生物代谢来产生能量,处理废水和生物修复.
BES复杂的机制需要先进的分析方法来优化性能.
人工智能 (AI) 为复杂的生物系统中的模式识别和数据分析提供了强大的工具.

研究的目的:

在BES应用中审查和比较常用的AI算法.
分析AI在预测微生物群落,基板和反应器性能方面的应用.
讨论基于人工智能的BES优化的局限性和未来方向.

主要方法:

审查和对人工智能算法的比较分析,包括人工神经网络 (ANN),遗传编程 (GP),模糊逻辑 (FL),支持向量回归 (SVR) 和自适应神经模糊推理系统 (ANFIS).
检查AI应用在预测关键BES参数中的应用.
讨论AI算法特征,如ANN的网络结构,GP的培训实用程序,FL的推理,SVR的准确性,以及ANFIS的混合性质.

主要成果:

人工智能算法展示了通过数据驱动的洞察力提高BES效率的巨大潜力.
特定的AI方法提供了明显的优势:ANN (简单性),GP (培训),FL (类似人类的逻辑),SVR (精度/稳定性),ANFIS (混合效益).
人工智能应用成功地预测了微生物动力学,基质转换和反应器性能,改善了系统管理.

结论:

基于人工智能的方法对于优化BES性能和释放其在能源和环境应用中的全部潜力至关重要.
为推进BES技术,建议对人工智能的局限性和发展进行进一步的研究.
本综述为人工智能驱动BES领域的研究人员和从业人员提供了全面的概述.

关键词:

人工智能的人工智能是人工智能.生物电化学系统生物电化学系统机器学习是机器学习.微生物电解细胞的电解.微生物燃料电池是一种微生物燃料电池.

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