Microbial fuel cell soft sensor for real-time toxicity detection and monitoring.

Abstract

Real-time toxicity detection and monitoring using a microbial fuel cell (MFC) is often based on observing current or voltage changes. Other methods of obtaining more information on the internal state of the MFC, such as electrochemical impedance spectroscopy (EIS), are invasive, disruptive, time consuming, and may affect long-term MFC performance. This study proposes a soft sensor approach as a non-invasive real-time method for evaluating the internal state of an MFC biosensor during toxicity monitoring. The proposed soft sensor approach is based on estimating the equivalent circuit model (ECM) parameters in real time. A flow-through MFC biosensor was operated at several combinations of carbon source (acetate) and toxicant (copper) concentrations. The ECM parameters, such as internal resistance, capacitance, and open-circuit voltage, were estimated in real time using a numerical parameter estimation procedure. The soft sensor approach proved to be an adequate replacement for EIS measurements in quantifying changes in the biosensor internal parameters. The approach also provided additional information, which could lead to earlier detection of the toxicity onset.