Performance Analysis And Control Method Of Microbial Fuel Cell System

There are huge recyclable energy contained in biomass and sewage that can provide significant benefits today when energy is depleted and microbial fuel cell(MFC)provides a feasible way for the utilization of energy.It is of great significance to study the control method of microbial fuel cell in order to improve the working performance and efficiency of system.In this paper,the anode and cathode feed flow as the key point of the optimal control of microbial fuel cell is obtained through the analysis of the performance which studied at first,and the load current is taken as an important factor to test the effectiveness of the controller designed for the cathode and anode feed flow.The main contents of this paper are as follows:The significance and background of the research on the performance analysis and control method of microbial fuel cell are introduced.The mechanism,structure and the key indexes of microbial fuel cells are described,then the model of microbial fuel cell system is established and its performance of each parameter on the output is simulated and analyzed,and finally get the key point for optimal control of the system: the feed flow of the anode and cathode.Aiming at solve the overshoot,slow response and nonlinearity of microbial fuel cell,the neural network model predictive controller is proposed,which is mainly used to control the llow of anode feed.The training data is obtained by using the random signal excitation system model,and the neural network model by the training neural network is used as the reference model of the model predictive control,and the neural network model predictive controller for anode feed flow is obtained based on feedback correction and rolling optimization.The simulation results show that the neural network predictive control method proposed in this paper has small overshoot and has good robustness compared with the traditional PID control method.The MFC system was linearized at the steady point based on the characteristics of system running in the steady state operation point,the fractional PID controller was designed with the advantages of flexible and easy design.The fractional PID controller is designed for the anode and cathode models respectively,then apply it to the microbial fuel cell system by analyzing its performance.The simulation resultsshow that the fractional PID controller designed has the advantages of smaller overshoot and faster response speed compared to integer PID controller.A model predictive method based on multi model switching is proposed,in order to solve the problem that the output voltage of the system is unstable when the operating point of the microbial fuel cell changes.The linearized equation is obtained by linearizing the anode and cathode at steady point.According to the linearized equation design model predictive controller,and then applied it to the nonlinear microbial fuel cell system model.Then,the multi-model switching prediction controller under unsteady load current is proposed to solve the problem of unstable system voltage output when the steady-state operating point fluctuates.Switching to the corresponding model predictive controller use different switching strategies and track the running status of the system.The simulation results show that when the load current changes,the proposed multi-model switching prediction control can realize the switching between different steady state operating points,and it can realize the smooth output of the voltage easily.