基于SOFC余热利用的数据中心功冷联供系统研究  

Performance analysis of combined power and cooling system for data center based on SOFC waste heat utilization

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作  者:孙景怡 张开帆 孙柯[1] 于泽庭[1] 高辉 崔波 Sun Jingyi;Zhang Kaifan;Sun Ke;Yu Zeting;Gao Hui;Cui Bo(School of Energy and Power Engineering,Shandong University,Jinan 250061,China;Shandong Xinguang Energy-saving Technology Co.,ltd,Yantai 264000,China)

机构地区:[1]山东大学能源与动力工程学院,济南250061 [2]山东鑫光节能科技有限公司,烟台264000

出  处:《低温与超导》2025年第2期40-48,共9页Cryogenics and Superconductivity

基  金:国家自然科学基金(62192753)资助。

摘  要:为了充分利用固体氧化物燃料电池的排气余热,本文提出了一种应用于数据中心的新型功冷联供系统,构建其相应的数学模型,并探讨燃料电池进口温度等参数对系统性能的影响。研究结果表明,在给定条件下,该联供系统的总发电效率和总能利用效率分别为65.97%和67.48%;当燃料电池进口温度大于743.15 K时,联供系统总能利用效率增加速度减缓。此外,采用人工神经网络代理模型与遗传算法相结合的优化方法,对系统性能进行单目标优化,优化得到联供系统的总发电效率和总能利用效率分别提高1.4855%和1.7931%。This study presented a new combined power and cooling system for date centers to fully recover the wasted heat from solid oxide fuel cell,the mathematical model was developed to investigate the impact of parameters such as the fuel cell inlet temperature on system performance.The findings demonstrate that,under the designed conditions,the total power generation effi-ciency and total energy utilization efficiency of the integrated system reach 65.97%and 67.48%,respectively.When the fuel cell inlet temperature is higher than 743.15 K,the increase rate of the total energy utilization efficiency of the combined system is decreased.In addition,the single objective optimization was carried out by using the artificial neural network agent model cou-pled with a genetic algorithm to optimize the system performance,and the optimized results show the total power generation effi-ciency and total energy utilization efficiency of the combined system are increased by 1.4855% and 1.7931%,respectively.

关 键 词:功冷联供系统 数据中心节能 固体氧化物燃料电池 余热利用 单目标优化 

分 类 号:TB659[一般工业技术—制冷工程]

 

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