Modeling and Parameter Identification for a Biofilm in a Microbial Fuel Cell

Development of a mathematical model for a microbial denitrification co-culture system comprising acetogenic bacterium Sporomusa ovata and denitrifying bacterium Pseudomonas stutzeri

Previous study has shown that co-culturing acetogenic bacterium Sporomusa ovata (SO), with denitrifying bacterium Pseudomonas stutzeri (PS), is a promising strategy to enhance the microbial denitrification for nitrate-contaminated groundwater remediation. However, the mutual effects and reaction kinetics of these two bacteria in the co-culture system are poorly understood. In this study, a mathematical model for this co-culture system was established to fill this knowledge gap. Model simulation demonstrated that SO had a significant effect on the kinetics of denitrification by PS, while PS slightly affected the kinetics of acetate production by SO. The optimal initial HCO₃^-/NO₃^- ratio and SO/PS inoculation ratio were 0.77-1.48 and 67 for the co-culture system to achieve satisfied denitrification performance with less acetate accumulation. Finally, the minimum hydrogen supply was recommended when the initial bicarbonate and nitrate concentrations were assigned in the range of 2-20 mM and 2-4 mM for simulating the natural nitrate-contaminated groundwater treatment. These findings could provide useful insights to guide the operation and optimization of the denitrification co-culture system.

Reaction kinetics of anodic biofilms under changing substrate concentrations: Uncovering shifts in Nernst‐Monod curves via substrate pulses

In the present study, it is shown that the concentration dependency of undefined mixed culture anodic biofilms does not follow a single kinetic curve, such as the Nernst‐Monod curve. The biofilms adapt to concentration changes, which inevitably have to be applied to record kinetic curves, resulting in strong shifts of the kinetic parameters. The substrate concentration in a continuously operated bioelectrochemical system was changed rapidly via acetate pulses to record Nernst‐Monod curves which are not influenced by biofilm adaptation processes. The values of the maximum current density j_max and apparent half‐saturation rate constant K_s increased from 0.5 to 1 mA cm⁻² and from 0.5 to 1.6 mmol L⁻¹, respectively, within approximately 5 h. Double pulse experiments with a starvation phase between the two acetate pulses showed that j_max and K_s decrease reversibly through an adaptation process when no acetate is available. Pseudo‐capacitive charge values estimated from non‐turnover cyclic voltammograms (CV) led to the hypothesis that biofilm adaptation and the observed shift of the Nernst‐Monod curves occurred due to changes in the concentration of active redox proteins in the biofilm. It is argued that concentration‐related parameters of kinetic models for electroactive biofilms are only valid for the operating points where they have been determined and should always be reported with those conditions.

Modeling and optimization strategies towards performance enhancement of microbial fuel cells

Considering the complexity associated with bioelectrochemical processes, the performance of a microbial fuel cell (MFC) is governed by input operating parameters. For scaled-up applications, a MFC system needs to be modeled from engineering perspectives in terms of optimum operating conditions to get higher performance and energy recovery. Several conceptual numerical models to advanced computational simulation approaches have been developed to represent simple-form of a complex MFC system. Application of mathematical and computation models are explored to establish the relationship between operating input-variables and power output. The present review discusses about the complexity of system, modeling strategies used and reality of such modeling for scaling-up applications of MFCs. Additionally, the selection of an appropriate mathematical model reduces the computational duration and provides better understanding of the system process. It also explores the possibility and progress towards commercialization of MFCs and thus the need of development of model-based optimization and process-control approaches.