Operando Failure Diagnosis and Performance Dynamics in Microbial Fuel Cells Treating Mine Waste

Abstract

Bench-scale microbial fuel cells (MFCs) treating mining wastewater frequently exhibit operational variability and uncharacterized degradation that obscure true biocatalytic performance. To decouple genuine biological treatment effects from mechanical failures, this paper presents an integrated diagnostic framework validated on two bench-scale systems treating heavy-metal-rich gold mine tailings. The first system evaluates Micractinium inermum algal bio-augmentation (System 1), while the second compares Psychrobacter alimentarius- and Trichococcus patagoniensis-dominated anodic consortia (System 2). To overcome single-reactor constraints, the framework integrates paired time-series statistical modeling, an adaptive percentile-floor change-point detector, equivalent-circuit modeling, and baseline-corrected spectroscopy (XRD/FTIR). Applying the framework to these systems uncovers previously masked dynamics: statistical analysis demonstrates that algal biocatalysis provides no voltage advantage under stable operation (+0.17%) but increases output by 27.54% under diurnal perturbation, while periodicity analysis links these diurnal shifts to the chamber photoperiod. Furthermore, heavy-metal remediation (up to 97.7%) is governed by system-level physicochemical mechanisms rather than algal-specific processes. The change-point detector successfully isolates distinct failure modes, distinguishing a recoverable excursion from terminal structural collapse. Finally, equivalent-circuit modeling reveals that the superior power density of Trichococcus consortia is driven by combined improvements in internal resistance and open-circuit voltage. Ultimately, pairing statistical controls with automated fault detection resolves operational ambiguity, offering a scalable baseline for health monitoring in bio-electrochemical wastewater treatment.