利用人工智能和 BES/MFC 降低 BOD 测量的预测时间。

Volgograd State University, Volgograd, Russia.

Imperial College London, London, UK.

Environ Monit Assess. 2023 Aug 5;195(9):1018. doi: 10.1007/s10661-023-11576-0.

Biochemical oxygen demand (BOD) is one of the most important water/wastewater quality parameters. BOD is the amount of oxygen consumed in 5 days by microorganisms that oxidize biodegradable organic materials in an aerobic biochemical manner. The primary objective of this research is to apply microbial fuel cells (MFCs) to reduce the time requirement of BOD measurements. An artificial neural network (ANN) has been created, and the predictions we obtained for BOD measurements were carried out within 6-24 h with an average error of 7%. The outcomes demonstrated the viability of our AI MFC/BES BOD sensor in real-life scenarios.

生化需氧量（BOD）是最重要的水质/废水质量参数之一。BOD 是指在有氧生物化学条件下，微生物在 5 天内氧化可生物降解有机物质所消耗的氧气量。本研究的主要目的是应用微生物燃料电池（MFC）来减少 BOD 测量的时间要求。我们创建了一个人工神经网络（ANN），并在 6-24 小时内对 BOD 测量进行了预测，平均误差为 7%。结果表明，我们的 AI MFC/BES BOD 传感器在实际场景中是可行的。