Data-driven and validated dimensional analysis for rational scale-up of a dual-chamber microbial fuel cell system for water-energy nexus exploitation

Mathematical modelling of microbial fuel cells (MFC) facilitates their scale-up by maintaining dimensionless parameters across reactor volumes for consistent performance. This study developed data-driven correlations to predict areal power density for a batch-fed dual-chamber MFC using hybridised first-principle mechanistic model and Buckingham's Pi theorem. The established correlations were validated using experimentally-derived data for pre-enriched electroactive biofilm from mixed cultures. The biochemical model parameters are infilled with stoichiometric and thermodynamics estimations. Results showed that the correlations using logistic kinetics (Nash-Sutcliffe Efficiency, NSE = 0.59) outperformed Monod kinetics (NSE = 0.52) as the latter was not suitable for representing the first-order biochemical kinetics under limited substrate conditions. Sensitivity analysis on varying pH and bicarbonate concentration improved model predictions by +/- 50%, though relative absolute error was +/- 20% due to inherent error of estimated biochemical parameters. The application of hybridised approach for modelling MFC provides renewed perspectives for their rational design and scale-up applications.

Automated summary

Mathematical modelling is crucial for the scale-up of microbial fuel cells (MFC). In this study, data-driven correlations were developed to predict the areal power density of a batch-fed dual-chamber MFC. Results showed that logistic kinetics outperformed Monod kinetics, and sensitivity analysis improved model predictions.