At present, due to the complexity and nonlinearity, the thermal safety and
economic feasibility assessment and optimization of the Solid Oxide Fuel
Cell-Gas Turbine (SOFC-GT) system under variable loads is important to extend
the service life and reduce the cost. To solve these problems, this paper
proposes a top-level cyclic SOFC-GT system, which considers the design of
two-stage preheaters, as well as the impact of material reaction kinetics and
thermoelectric coupling characteristics on system performance. Furthermore, the
multi-criteria evaluation of the SOFC-GT system under variable loads has been
studied, with evaluation indicators primarily including thermodynamic and
economic indicators. Afterwards, a Spearman-based parametric sensitivity
analysis is used to explore the response trends of performance indicators within
the SOFC-GT system. Additionally, an intelligent learning method based on
convolutional neural network is designed to determine the dynamic behavior
between operational parameters and performance indicators. Finally,
Multi-Objective Particle Swarm Optimization (MOPSO) algorithm is introduced,
aiming to get the optimal combination of operational parameters. The research
results indicate that the reduction of fuel utilization, air excess ratio, and
bypass valve opening can effectively improve the net power output of SOFC-GT
system and significantly reduce the EPC. Meanwhile, the MOPSO algorithm has
effectively improve the performance of the SOFC-GT system. Compared to before
optimization, the net power and EPC have been optimized by 7.25% and 11.16%,
respectively.