Microbial Fuel Cell Research Paper
There is also no advanced monitoring method to monitor the minute changes of the parameters in the start-up process on-line.
This will lead to the loss of important data in the detection process and will consume a lot of manpower and material resources which will bring inconvenience to the intensive study of SMFC.
This article will provide important guidance for shortening start-up time and increasing power output.
Microbial fuel cell (MFC) is being viewed as a potential bio-electrochemical device capable of producing energy in the form bioelectricity apart from wastewater treatment [1–3] which has been widely investigated in recent years.  described enzymatic biofuel cell based on enzyme modified anode and cathode electrodes are both powered by ethanol and operate at ambient temperature.  reviewed recent articles about the application of MFCs to solid substrates treatment and valorisation and the contribution that BESs and MFC could give to the development of a more sustainable waste management.  investigated the influence of microelectrogenesis on PAHs degradation and detoxification operated by Pseudomonadaceae, Bacillaceae, Staphylococcaceae, and Enterobacteriaceae in water environment.
This study selected a single-chamber SMFC as a research object, using online monitoring technology to accurately measure the temperature, p H, and voltage of the microbial fuel cell during the start-up process.
In the process of microbial fuel cell start-up, the relationship between temperature, p H, and voltage was analysed in detail, and the correlation between them was calculated using SPSS software.
The experimental results show that, at the initial stage of SMFC, the purpose of rapid growth of power production can be achieved by a large increase in temperature, but once the temperature is reduced, the power production of SMFC will soon recover to the state before the temperature change.
But due to the lack of precise mathematical models of MFC and the above method only applicable to the condition of constant parameter and slow transformation, it is difficult to apply them to SMFC.
The neural network has strong nonlinear fitting ability, can map arbitrarily complex nonlinear relations, and has strong robustness, memory ability, nonlinear mapping ability, and strong self-learning ability .
The potential (biologically mediated) developed between the bacterial metabolic activity (series of oxidation-reduction reactions generating electrons (e)) and the electron acceptor conditions generate potential to make bioelectricity .