Multi-Criteria Evaluation and Operating Parameter Optimization of the SOFC-GT System under Variable Loads

<div class="section abstract"><div class="htmlview paragraph">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.</div></div>