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Multi-objective optimization of biomass-based solid oxide fuel cell integrated with Stirling engine and electrolyzer

机译：斯特林发动机和电解池相结合的生物质固体氧化物燃料电池多目标优化

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摘要

The aim of this study is to increase the power generation/exergy efficiency and reduce total product cost/environmental contamination of solid oxide fuel cells. Accordingly, three integrated systems are proposed and analyzed from energy, exergy, exergoeconomic, and environmental viewpoints through the parametric study. The first model assesses the combination of a gasifier with a solid oxide fuel cell. In the second model, waste heat of the first model is reused in the Stirling engine to enhance the efficiency and power generation. The last model proposes reuse of the surplus power of the Stirling engine in a proton exchange membrane electrolyzer for hydrogen production. Considering total product cost, exergy efficiency, and hydrogen production rate as the objective functions, a multi-objective optimization is applied based on the genetic algorithm. The results indicate that at the optimum operating condition, the exergy efficiency of the model (a), (b), and (c) is 28.51%, 39.51%, and 38.03%, respectively. Corresponding values for the energy efficiency and the emission rate of the models are 31.13%, 67.38%, 66.41%, 1.147 t/MWh, 0.7113 t/MWh, 0.7694 t/MWh. At the optimum solution point, total product cost associated with the model (a), (b), and (c) is 19.33 $/GJ, 18.91 $/GJ, and 24.93 $/GJ, respectively. If the hydrogen production rate and total product cost considered as the objective functions, at optimum solution point, the rate of hydrogen production and overall product cost would be 56.5 kg/day and 41.76 $/GJ, respectively. Overall, the proposed integrated systems demonstrate decent functionality both in thermodynamic, environmental, and economic aspects.
机译：这项研究的目的是提高发电/火用效率并减少固体氧化物燃料电池的总产品成本/环境污染。因此，通过参数研究，从能源，火用，能效经济和环境的角度提出并分析了三个集成系统。第一个模型评估了气化炉与固体氧化物燃料电池的组合。在第二个模型中，第一个模型的废热在斯特林发动机中重复使用，以提高效率和发电量。最后一个模型提出在质子交换膜电解槽中重用斯特林发动机的剩余功率来生产氢气。以总产品成本，火用效率和制氢率为目标函数，基于遗传算法进行了多目标优化。结果表明，在最佳运行条件下，模型（a），（b）和（c）的能效效率分别为28.51％，39.51％和38.03％。模型的能效和排放率的对应值分别为31.13％，67.38％，66.41％，1.147吨/兆瓦时，0.7113吨/兆瓦时，0.7694吨/兆瓦时。在最佳解决方案点，与模型（a），（b）和（c）相关的总产品成本分别为19.33 $ / GJ，18.91 $ / GJ和24.93 $ / GJ。如果将制氢速率和总产品成本作为目标函数，则在最佳解决方案点，制氢速率和总产品成本分别为56.5千克/天和41.76 $ / GJ。总体而言，所提出的集成系统在热力学，环境和经济方面均显示出良好的功能。

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