This invention discloses a real-time optimization control method for an underground
coalbed methane SOFC combined heat and power
system. The method utilizes a distributed
fiber optic
temperature measurement network and electrochemical impedance
spectroscopy to acquire real-time three-dimensional temperature field distribution data and
electrode impedance data within the fuel
cell stack. Based on this, a dual-channel
hybrid neural network is used to fuse temporal and transient characteristics, outputting a quantitative
carbon deposition risk index and a
sulfur poisoning
risk index to characterize the
electrode health status. This dynamically corrects the
anode kinetics and
mass transfer parameters in the digital twin model. With
system efficiency, temperature uniformity, and health indicators as optimization objectives, a hierarchical collaborative control architecture is employed for multi-objective rolling optimization to generate optimal operating setpoints. Finally, the fuel pretreatment, fuel
cell stack operation, and
waste heat recovery processes are coordinated and regulated. This invention also supports incremental model updates and
predictive maintenance warnings. This method improves
system efficiency, safety, and lifespan, and is applicable to SOFC combined heat and power scenarios using complex fuels such as
sulfur-containing
coalbed methane.