Improve microbial fuel cell efficiency using receding horizon predictive control

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.