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使用生物电化学传感器对碳呼吸进行机械和数据驱动的建模

Rishabh Puri¹, Seyed A Emaminejad², Roland D Cusick¹

¹Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, United States.

Current opinion in biotechnology

|July 21, 2024

概括

生物电化学传感器 (BES) 可以监测废水中的碳. 将BES与机器学习集成,可以实时了解可生物降解碳和水资源回收设施中的微生物代谢.

科学领域:

环境科学环境科学
分析化学分析化学
微生物学微生物学

背景情况:

生物电化学传感器 (BES) 技术用于测量废水中的可溶碳.
实验室开发的BES架构和方法不能准确预测水资源回收设施 (WRRF) 的现场性能.

研究的目的:

探索将BES集成到环境传感网络和机器学习算法中.
在WRRF中监测可生物降解碳动态和微生物代谢.
确定BES现场部署的可能性和障碍.

主要方法:

检查BES与环境传感网络的整合情况.
应用机器学习算法进行数据分析.
在现实世界WRRF设置中评估BES性能.

主要成果:

BES显示了实时监测可生物降解碳的潜力.
确定了整合方面的挑战和现场部署的机会.
机器学习提高了BES数据的预测能力.

结论:

当BES与机器学习集成时,可以提供有关废水处理过程的有价值的实时数据.
这种方法为WRRFs的全面生物降解碳消耗提供了见解.
需要进一步的研究来克服现场部署的障碍.

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