Machine-learning-based capacity prediction and construction parameter optimization for energy storage salt caverns
文献类型：期刊论文
| 作者 | Li, Jinlong; Wang, ZhuoTeng; Zhang, Shuai; Shi, Xilin; Xu, Wenjie; Zhuang, Duanyang; Liu, Jia; Li, Qingdong; Chen, Yunmin |
| 刊名 | ENERGY
 |
| 出版日期 | 2022-09-01 |
| 卷号 | 254 |
| 关键词 | Energy storage salt cavern Solution mining construction Capacity prediction Artificial neural network Machine learning |
| ISSN号 | 0360-5442 |
| 英文摘要 | The construction design and control of energy storage salt caverns is the key to ensure their long-term storage capacity and operational safety. Current experimental and numerical design/optimizing methods are time-consuming and rely heavily on engineering experience. This paper proposes a machinelearning-based method for the rapid capacity prediction and construction parameter optimization of energy storage salt caverns. We propose a data generation method that uses 1253 sets of random construction parameters as input. The resulting capacity/efficiency-concerned effective volume (V) and maximum radius (rmax) obtained by our numerical program are the output. A back-propagation artificial neural network model for salt cavern construction prediction (BPANN-SCCP) is trained on the dataset. The cross-validated mean absolute percentage error (MAPE) of the BPANN-SCCP predicted Vis 1.838%, that of the predicted rmax is 3.144%. This accuracy meets the engineering design requirements, and the prediction efficiency is improved by about 6 x 107 times. Using this model, a design parameter optimization method is devised to optimize 3 sets of design parameters from a million random ones. The resulting caverns are regular in shape with larger capacity ratio than 3 field caverns in Jintan Salt Cavern Gas Storage, verifying the reliability of the proposed optimization method. (c) 2022 Elsevier Ltd. All rights reserved. |
| 学科主题 | Thermodynamics ; Energy & Fuels |
| 语种 | 英语 |
| WOS记录号 | WOS:000808479000004 |
| 出版者 | PERGAMON-ELSEVIER SCIENCE LTD |
| 源URL | [http://119.78.100.198/handle/2S6PX9GI/34825]  |
| 专题 | 中科院武汉岩土力学所 |
| 作者单位 | 1.Zhejiang University; 2.Chinese Academy of Sciences; Wuhan Institute of Rock & Soil Mechanics, CAS |
| 推荐引用方式 GB/T 7714 | Li, Jinlong,Wang, ZhuoTeng,Zhang, Shuai,et al. Machine-learning-based capacity prediction and construction parameter optimization for energy storage salt caverns[J]. ENERGY,2022,254. |
| APA | Li, Jinlong.,Wang, ZhuoTeng.,Zhang, Shuai.,Shi, Xilin.,Xu, Wenjie.,...&Chen, Yunmin.(2022).Machine-learning-based capacity prediction and construction parameter optimization for energy storage salt caverns.ENERGY,254. |
| MLA | Li, Jinlong,et al."Machine-learning-based capacity prediction and construction parameter optimization for energy storage salt caverns".ENERGY 254(2022). |
入库方式： OAI收割
来源：武汉岩土力学研究所
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