Mechanistic and data-driven modeling of carbon respiration with bio-electrochemical sensors
Abstract
Bioelectrochemical sensor (BES) technologies have been developed to measure soluble carbon concentrations in wastewater. However, architectures and analytical methods developed in controlled laboratory environments fail to predict BES behavior during field deployments at water resource recovery facilities (WRRFs). Here, we examine the possibilities and obstacles associated with integrating BESs into environmental sensing networks and machine learning algorithms to monitor the biodegradable carbon dynamics and microbial metabolism at WRRFs. This approach highlights the potential of BESs to provide real-time insights into full-scale biodegradable carbon consumption across WRRFs.
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