Sustainability and resource management is one of the major concerns for scientific community now days. In this context, in many fields of research, the focus is centred on the re-utilization of used resources with no further damage to the environment. Microbial fuel cell (MFC) is one of the technologies, where electricity is generated from waste-water. MFC is an electrochemical device that converts organic matter directly into the electricity with high efficiency. MFCs offer certain advantages such as minimum sludge production, cost effective and operation at normal condition. Despite its wide range of potential applications and ease of feed stocks, commercialisation of this technology did not realized till now¹. The major limitations for the commercialization are the scale up of the process² and continuous operations. To perform continuous operation for longer time, it is extremely important to understand the dynamics of the system. Dynamics of the system can be understood by performing exhaustive experiments and analysing the data thus obtained. But performing exhaustive experiments is a time consuming as well expensive task. The other approach is to model the system to understand the dynamics.

In literature very few researcher worked on the modeling of continuous microbial fuel cell (CMFC). Although batch modeling of MFC have been reported earlier, a very few studies had focused on understanding the dynamics of the system. First dynamic study was carried out by Zhang et al³, and there model is based on electron transfer using mediator. Later, Picioreanu et al⁴ modeled the bio-film development on the anode electrode in MFC. Marcus et al⁵ and Pinto et al⁶ developed 1-D model for multispecies electron donor and acceptor for bio-film anode based on the material balance, Ohm’s law and Nernst-Monod kinetics to describe the rate of electron donor oxidation. In 2017, Esfandyari et al⁷, developed batch process model considering direct electron transfer through bio-film to the electron acceptor.

In this talk, we will present a continuous model developed for MFC and dynamic analysis of potential controlled variables. Dynamic analysis will provide deeper insights of the various physical phenomena of the microbial fuel cell.

In present work, model presented by Esfandyari et al⁷ which is a batch model is taken as the basis. Batch model developed in this work is validated with the work of Esfandyari⁷ and Picioreanu et al⁴ for typical dynamic responses. The batch model is then converted into the dual chamber continuous model. In continuous model, substrate (Lactate) and oxygen is continuously fed to the anode and cathode chamber respectively as shown in Figure 1. Coolant is supplied through the jacket to maintain the required operating temperature of the cell. Bacteria species Shewanella is used as the catalyst to oxidise electron donor. The electrons produced are then reaching the cathode electrode via external circuit producing the power. Protons migrate to the cathode through the proton exchange membrane. In the cathode chamber, transferred electrons and migrated protons are reacted with dissolved oxygen to produce water. To understand the dynamic of the MFC, the step change study of the important parameters i.e. substrate concentration, current produced and coolant flow have been simulated. The simulation result of this model is shown in Figure 2, where time variations of the current shows first order dynamic. The settling time observed to be approximately 20 days. It is also noted that the current obtained from the same size of fuel cell in continuous system is higher than the batch.

Once the impact of pH is accounted into the model, the dynamic analysis with respective various potential manipulated variables i.e. pH of the solution, flow rate of the substrate and coolant flow rate will be studied to get further insight of the microbial fuel cell. The model, thus developed will be used as a system for devising an effective control and optimization strategies for the microbial fuel cell.

References:

1. J. Chouler, G. Padgett, P. Cameron, K. Peruss, M. Titirici, I. Ieropoulos, and M. Lorenzo, Electrochimica Acta, 196, 89-98,(2016)
2. S. Choi, Biosensors and Bioelectronic, 69, 8-25 (2015).
3. X. Zhang and A. Halme, Biotechnology Letters, 17 (8), 809-814 (1995).
4. C. Picioreanua, I. Head, K. Katuri, M. van Loosdrecht, K. Scott, Water Research,41, 2921-2940 (2007).
5. A. Marcus, C. Torres, B. Rittmann, Biotechnology and Bioengineering, 98 (6), 1171-1182 (2007).
6. R. Pinto, B. Srinivasan, M. Manuel, B. Tartakovsky, Bioresource Technology, 101(14), 5256-5265 (2010).