Computational Simulation of Mass Transport and Energy Transfer in the Microbial Fuel Cell System

This doctoral dissertation introduces the research in the computational modeling and simulation for the microbial fuel cell (MFC) system which is a bio-electrochemical system that drives a current by using bacteria and mimicking bacterial interactions found in nature. The numerical methods, research approaches and simulation comparison with the experiments in the microbial fuel cells are described; the analysis and evaluation for the model methods and results that I have achieved are presented in this dissertation.

The development of the renewable energy has been a hot topic, and scientists have been focusing on the microbial fuel cell, which is an environmentally-friendly and promising technology. The MFC full cell is a complex system which has different reactions, coupled with mass and electrons transport in bulk liquid. Therefore, this research contains interdisciplinary fields. The methods will be adopted includes: (1) numerical methods (finite volume method/finite difference method/ parallel computation/ multiple step times etc.); (2) computational fluid dynamics method (diffusion equation, Nernst-Planck equation etc.); (3) experimental electrochemical analysis methods; (4) the biological treatment process (biofilm growth, anaerobic/aerobic bacteria etc.). The uniqueness of this work: (1) a comprehensive computational bioelectrochemical fuel cell models was firstly constructed in the research; (2) the primary physical phenomena have been systematically analyzed in both steady and transient states; (3) The simulation evaluated the MFC system which are hardly obtained directly in the experiments. The computational work in MFC modeling achieved four goals: (1) Characterized the primary factors which affect the MFC performance and used them to describe a complex microbial fuel cell model; (2) Derived a series of appropriate electrochemical /biological /chemical reactions equations for the analysis of the mechanics in MFC; (3) Applied computational methods in the model construction and built a series of sub-models for the MFC system; (4) Simulated the models and compared with the experimental results, gave the analysis for the MFC phenomena which are used for optimizing the design of the MFC system.