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However, the low output power density of MFC is generally not enough to drive common electronic devices continuously and extremely hinder its practical application [11, 12].In recent years, researchers in various countries have studied MFC in terms of microorganisms, electrodes, configurations, matrices, operating conditions, and electrochemical properties and found that although microorganisms are the core of MFC, nonbiological factors are more important than biological factors in the production of electricity .
The cathode operating conditions play an important part in the overall performance of the MFC .
Cathodic p H microenvironment is one of the crucial factors affecting the metabolic activity of the substrate, affecting the electron and H reaction mechanism.
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.
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 .
Microorganisms extract energy required to build biomass (anabolic process) from redox reactions (catabolism) through electron do- nor/acceptor conditions .
Therefore, we will focus on the relationship between cathode p H, temperature, and voltage during the start-up of a microbial fuel cell.
Due to the successful application of RBF neural network and ELM neural network in other biochemistry fields, we choose them to regress the nonlinear system in the later stage of SMFC start-up, and the regression network was used to predict the p H of that period.
Therefore, it is necessary to find a suitable neural network to fit the nonlinear model between various parameters in the start-up phase.
As far as we know, there is no study on the relationship between p H, power generation and temperature during the start-up of microbial fuel cells.