Research On Model Analysis And Control Method Of Microbial Fuel Cells

Microbial fuel cells(MFCs)have received extensive attention in recent years as a new type of energy recovery and wastewater treatment technology,The MFCs as a bioelectrochemical reactor can directly convert chemical energy into electrical energy,and produce clean energy while removing pollutants.However,the low biochemical reaction rate of MFCs results in low power output,which is temporarily not widely used in industrial production.Due to the complexity of the MFCs system model,serious timedelaying character,and many influencing factors,it is time-consuming and uneconomical to test performance only through experiments.Analyzing the entire system through mathematical models and understanding the operating conditions in depth is of great significance for improving the power generation performance of MFCs and breaking the bottleneck of MFCs development.In this paper,the data acquisition and modeling,model analysis,optimization and control,and system fault diagnosis of microbial fuel cells are studied from the top to the bottom by combining theory with experiment.The main contents of this paper are as follows:(1)Data acquisition and start-up process modeling of microbial fuel cellsIn order to solve the problem of complex factors and difficult modeling in the startup stage of microbial fuel cells,the start-up process of sediment microbial fuel cells was selected as the research object.The online monitoring technology was used to measure the temperature,p H and voltage change data during the startup process,and the relationship between them was analyzed in detail.In the later stage of start-up phase of SMFCs,a radial basis function neural network and an extreme learning machine neural network were used to model the nonlinear system,and the p H data are predicted.The experimental results show that the prediction results of extreme learning machine network are better.(2)Dynamic analysis of microbial fuel cells modelIn view of the complex model of microbial fuel cells and the unknown influence of controllable parameters on the system,according to the biochemical reaction mechanism of MFCs system,the electrochemical model of continuous stirred tank type two chamber MFCs was established under the environment of MATLAB,and the dynamic analysis was carried out.The effects of acetate feed concentration and current density on the performance of the cell were studied.(3)Analysis and optimization of microbial fuel cells modelAiming at the problems of strong nonlinearity,poor robustness and high uncertainty of the microbial fuel cells model,a comprehensive model optimization framework is designed.Firstly,the global sensitivity analysis and uncertainty analysis are combined to analyze the influence of uncertain parameters on the system,and the uncertain parameters are optimized according to the analysis results.Secondly,based on the optimized mathematical model,a simplified MFCs neural network model is proposed by combining variable selection with neural network.Experimental results show that the framework can effectively improve the system robustness and reduce the complexity of the model,which provides a new method for optimizing the system and improving the universality of the model.(4)Control of microbial fuel cells modelAiming at the problems of strong nonlinearity and difficult control of the microbial fuel cells model,the backstepping method is selected as the core control method to be applied in MFCs system.The simulation results show that the proposed control laws can make the system effectively track the control target.(5)Fault diagnosis of microbial fuel cells systemTo solve the problem of trouble shooting in microbial fuel cells system,a fault diagnosis method based on wavelet packet and self-organizing map network is proposed,and the effectiveness and accuracy of the algorithm are proved.