Lina Wang, Weihao Guo, Zhiheng Zhang, Fu Wang, Jinliang Yuan
{"title":"基于极端学习机的双模运行可逆式固体氧化物燃料电池合成气成分性能分析与优化","authors":"Lina Wang, Weihao Guo, Zhiheng Zhang, Fu Wang, Jinliang Yuan","doi":"10.1016/j.jpowsour.2024.234982","DOIUrl":null,"url":null,"abstract":"<div><p>Reversible solid oxide fuel cell (rSOC) is an efficient means of converting chemical energy into electrical energy, offering a promising solution to the imbalance between energy production and consumption. The performance of rSOC in dual-mode operation, utilizing syngas as fuel, is significantly influenced by variations in fuel composition. This study aims to develop an rSOC model using Aspen Plus and the extreme learning machine (ELM) algorithm to evaluate the impact of different fuel compositions on stack performance in both solid oxide fuel cell (SOFC) and solid oxide electrolytic cell (SOEC) modes. Results indicate that the concentrations of H<sub>2</sub> and H<sub>2</sub>O are critical for optimal performance in dual-mode operation. Additionally, the water gas shift (WGS) reaction is employed to modify syngas composition for improved performance. When the molar fraction of H<sub>2</sub>/H<sub>2</sub>O is maintained between 50 % and 60 %, the rSOC achieves a maximum round-trip efficiency of 67.5 %. The optimal syngas composition, with H<sub>2</sub>/H<sub>2</sub>O/CO<sub>2</sub>/CO ratios of 50/5/35/10, can reach a maximum round-trip efficiency of 68.5 %. This study provides theoretical insights into the selection of syngas composition for rSOC in dual-mode operation.</p></div>","PeriodicalId":377,"journal":{"name":"Journal of Power Sources","volume":null,"pages":null},"PeriodicalIF":8.1000,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Performance analysis and optimization of syngas composition for reversible solid oxide fuel cells in dual-mode operation based on extreme learning machine\",\"authors\":\"Lina Wang, Weihao Guo, Zhiheng Zhang, Fu Wang, Jinliang Yuan\",\"doi\":\"10.1016/j.jpowsour.2024.234982\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Reversible solid oxide fuel cell (rSOC) is an efficient means of converting chemical energy into electrical energy, offering a promising solution to the imbalance between energy production and consumption. The performance of rSOC in dual-mode operation, utilizing syngas as fuel, is significantly influenced by variations in fuel composition. This study aims to develop an rSOC model using Aspen Plus and the extreme learning machine (ELM) algorithm to evaluate the impact of different fuel compositions on stack performance in both solid oxide fuel cell (SOFC) and solid oxide electrolytic cell (SOEC) modes. Results indicate that the concentrations of H<sub>2</sub> and H<sub>2</sub>O are critical for optimal performance in dual-mode operation. Additionally, the water gas shift (WGS) reaction is employed to modify syngas composition for improved performance. When the molar fraction of H<sub>2</sub>/H<sub>2</sub>O is maintained between 50 % and 60 %, the rSOC achieves a maximum round-trip efficiency of 67.5 %. The optimal syngas composition, with H<sub>2</sub>/H<sub>2</sub>O/CO<sub>2</sub>/CO ratios of 50/5/35/10, can reach a maximum round-trip efficiency of 68.5 %. This study provides theoretical insights into the selection of syngas composition for rSOC in dual-mode operation.</p></div>\",\"PeriodicalId\":377,\"journal\":{\"name\":\"Journal of Power Sources\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":8.1000,\"publicationDate\":\"2024-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Power Sources\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0378775324009340\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CHEMISTRY, PHYSICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Power Sources","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0378775324009340","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, PHYSICAL","Score":null,"Total":0}
Performance analysis and optimization of syngas composition for reversible solid oxide fuel cells in dual-mode operation based on extreme learning machine
Reversible solid oxide fuel cell (rSOC) is an efficient means of converting chemical energy into electrical energy, offering a promising solution to the imbalance between energy production and consumption. The performance of rSOC in dual-mode operation, utilizing syngas as fuel, is significantly influenced by variations in fuel composition. This study aims to develop an rSOC model using Aspen Plus and the extreme learning machine (ELM) algorithm to evaluate the impact of different fuel compositions on stack performance in both solid oxide fuel cell (SOFC) and solid oxide electrolytic cell (SOEC) modes. Results indicate that the concentrations of H2 and H2O are critical for optimal performance in dual-mode operation. Additionally, the water gas shift (WGS) reaction is employed to modify syngas composition for improved performance. When the molar fraction of H2/H2O is maintained between 50 % and 60 %, the rSOC achieves a maximum round-trip efficiency of 67.5 %. The optimal syngas composition, with H2/H2O/CO2/CO ratios of 50/5/35/10, can reach a maximum round-trip efficiency of 68.5 %. This study provides theoretical insights into the selection of syngas composition for rSOC in dual-mode operation.
期刊介绍:
The Journal of Power Sources is a publication catering to researchers and technologists interested in various aspects of the science, technology, and applications of electrochemical power sources. It covers original research and reviews on primary and secondary batteries, fuel cells, supercapacitors, and photo-electrochemical cells.
Topics considered include the research, development and applications of nanomaterials and novel componentry for these devices. Examples of applications of these electrochemical power sources include:
• Portable electronics
• Electric and Hybrid Electric Vehicles
• Uninterruptible Power Supply (UPS) systems
• Storage of renewable energy
• Satellites and deep space probes
• Boats and ships, drones and aircrafts
• Wearable energy storage systems