Improve Microbial Fuel Cell Efficiency Using Receding Horizon Predictive Control
Jie Gao, Hai Gu, Yuwei Yang, Ping Yuan +1
AI summary
65% confidenceThis paper proposes a receding horizon predictive control method to improve the efficiency of microbial fuel cells, considering uncertainty and unknown parameters, and demonstrates its stability and performance through Lyapunov analysis and MATLAB/Simulink simulations.
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- System
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Abstract
One of the promising technologies in the field of clean and renewable energy is the microbial fuel cells, which in addition to generating electrical energy from the metabolism of microorganisms, can also be used to improve the environment in wastewater treatment. In fact, this paper designs an integrated control model that in the presence of uncertainty and unknown parameters can consider the effect of input variables for two-population in a chamber. In addition to maintaining closed loop stability, it has acceptable behavior in terms of time to reach steady state and reduce system error and provide satisfactory performance in terms of output energy. Lyapunov analysis ensures system stability and system control functions are demonstrated by MATLAB / Simulink simulations.
Key findings
- The proposed control method can maintain closed-loop stability and reduce system error.
- The method can provide satisfactory performance in terms of output energy.
- Lyapunov analysis ensures the stability of the system.
Keywords
Identifiers
- Journal
- Journal of New Materials for Electrochemical Systems
- Year
- 2022