Kinetic parameter sensitivity in microbial electrolysis cell performance modeling

Abstract

Microbial Electrolysis Cells (MEC) represent a promising technology for hydrogen production from wastewater, requiring low applied voltages compared to conventional water electrolysis. However, system performance remains limited due to complex microbial interactions, making mathematical modeling essential for process optimization. This work replicates and analyzes the multi-population dynamic model proposed by Pinto et al. (2011), focusing on sensitivity analysis of maximum substrate consumption rates (qmax) for electrogenic, fermentative, and acetoclastic methanogenic microorganisms. Fifteen simulations were conducted, varying each parameter within its respective uncertainty intervals, and sensitivity was quantified using normalized indices. Results revealed a clear hierarchical importance: qmax,e (electrogenic) showed the highest impact with sensitivity indices (Smean) ranging from 3.8 to 6.2 for competitive microbial populations; qmax,f (fermentative) demonstrated transient influence primarily during reactor startup (Smean ranging from 0.65 to 0.78); while qmax,m (methanogenic) affected only anodic methane production (Smean of approximately 0.96). Notably, electrochemical performance variables (current, H2 production) proved robust to all three parameters at steady state, indicating that once the electrogenic biofilm is established, the system exhibits significant operational stability. These findings provide practical guidance for MEC design and operation, identifying qmax,e as the critical parameter requiring precise estimation for accurate prediction of microbial competition dynamics.