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Theoretical Investigation and Optimization of Effective Microbial Parameters of Different Biocatalyst in Microbial Fuel Cells System Using Genetic Algorithm

Kalantar Neyestanaki, Mohammad | 2015

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 48197 (06)
  4. University: Sharif University of Technology
  5. Department: Chemical and Petroleum Engineering
  6. Advisor(s): Yaghmaie, Soheila
  7. Abstract:
  8. Microbial fuel cell is one of the new technologies in the field of power generation from the organic energy resource. The fuel cell system in a process similar to chemical fuel cell generate electrical energy through oxidation of organic composition by uses of electrogenic microorganisms as biocatalyst. In this study the main object is providing a comprehensive mathematical model for the fuel cell system in micro-liter size. To achieve this purpose, we start to investigate properties of fuel cell system in this size. And according to the geometry of system we choose chemotactic equation for estimating hydrodynamic behavior of microorganism and using combination of conductive base and mediator base electron transfer theory for simulating Electrochemical properties of micro-liter size microbial fuel cell. The result of the simulation become validated through comparison with experimental data. After validation of model we use this model to study effect of various operational situation of micro-liter microbial fuel cell process and through this study the main process controlling mechanism of micro-liter mfc estimated and the mechanism that cause most of the energy lost of micro-lite MFC determined. After validation of model the mathematical potential of the model used to estimate the manipulated properties of genetic engineered Species. For this purpose, we use genetic algorithm for optimization the result of the model with experimental data via minimizing the sum of the square deviation of model predict and experimental data
  9. Keywords:
  10. Microbial Fuel Cell ; Parameter Estimation ; Genetic Algorithm ; Modeling ; Simulation ; Microlitter

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