Geometry prediction and design for energy storage salt caverns using artificial neural network
文献类型：期刊论文
| 作者 | Wang, Zhuoteng1; Chen, Jiasong2; Wang, Guijiu2; Li, Jinlong1; Li, Shuangjin3; Azhar, Muhammad Usman4; Ma, Shuang1; Xu, Wenjie1; Zhuang, Duanyang1; Zhan, Liangtong1 |
| 刊名 | ENERGY
 |
| 出版日期 | 2024-11-01 |
| 卷号 | 308页码:13 |
| 关键词 | Hydrogen storage Natural gas storage Salt cavern Construction design Parameter optimization Artificial neural network |
| ISSN号 | 0360-5442 |
| DOI | 10.1016/j.energy.2024.132820 |
| 英文摘要 | Salt cavern is one of the best storage for hydrogen, compressed air, and natural gas. However, the current physical/numerical simulation-based construction design cannot yield optimal solutions due to the complex correlation of multiple construction parameters. This paper proposes a novel machine-learning-enabled method for the geometry prediction and design optimization during salt cavern construction. A dataset of 1197 simulations of salt cavern construction is collected using our previously developed program. Construction parameters are included as inputs, and the simulated cavern geometries as outputs. A Gated Recurrent Unit model is selected and well-trained from 600 artificial neural network models. The model achieves a mean absolute error of 1.6m in the test dataset and of 2.83m for the geometric prediction of cavern JT52 in Jintan, meeting the field design requirements. Furthermore, an optimized construction design method is proposed by looping generating construction parameters, shape prediction, and deviation calculation until the deviation meets requirements. It succeed in designing an ideal ellipsoid cavern in approximately 51 min, with a capacity ratio fc 31 % larger than those of the field caverns. This approach demonstrates the potential for machine learning methods to serve as the third generation of construction design methods after physical and numerical modeling. |
| 资助项目 | National Natural Science Foundation of China[52109138] ; National Natural Science Foundation of China[52122403] ; National Natural Science Foundation of China[52208342] ; Basic Science Center Program for Multiphase Evolution in Hypergravity of the National Natural Science Foundation of China[51988101] ; Young Elite Scientists Sponsorship Program by CAST[2023QNRC001] ; Fundamental and Prospective Research Project of CNPC[2022DJ8303] ; Jiangxi Provincial Natural Science Foundation[20242ACB214008] ; Jiangxi Provincial Natural Science Foundation[20232BAB204072] |
| WOS研究方向 | Thermodynamics ; Energy & Fuels |
| 语种 | 英语 |
| WOS记录号 | WOS:001298979400001 |
| 出版者 | PERGAMON-ELSEVIER SCIENCE LTD |
| 源URL | [http://119.78.100.198/handle/2S6PX9GI/42335]  |
| 专题 | 中科院武汉岩土力学所 |
| 通讯作者 | Li, Jinlong; Li, Shuangjin |
| 作者单位 | 1.Zhejiang Univ, Ctr Hypergrav Expt & Interdisciplinary Res, MOE Key Lab Soft Soils & Geoenvironm Engn, Hangzhou 310058, Peoples R China 2.PipeChina Energy Storage Co, Jiangsu Gas Storage Branch Co, Zhenjiang 212004, Peoples R China 3.Hiroshima Univ, Grad Sch Adv Sci & Engn, Higashihiroshima 7398529, Japan 4.Univ Haripur, Dept Earth Sci, Haripur 22620, Pakistan 5.Chinese Acad Sci, Inst Rock & Soil Mech, State Key Lab Geomech & Geotech Engn, Wuhan 430071, Hubei, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wang, Zhuoteng,Chen, Jiasong,Wang, Guijiu,et al. Geometry prediction and design for energy storage salt caverns using artificial neural network[J]. ENERGY,2024,308:13. |
| APA | Wang, Zhuoteng.,Chen, Jiasong.,Wang, Guijiu.,Li, Jinlong.,Li, Shuangjin.,...&Chen, Yunmin.(2024).Geometry prediction and design for energy storage salt caverns using artificial neural network.ENERGY,308,13. |
| MLA | Wang, Zhuoteng,et al."Geometry prediction and design for energy storage salt caverns using artificial neural network".ENERGY 308(2024):13. |
入库方式： OAI收割
来源：武汉岩土力学研究所
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