Machine-learning-based capacity prediction and construction parameter optimization for energy storage salt caverns

Jinlong Li, ZhuoTeng Wang, Shuai Zhang, Xilin Shi, Wenjie Xu, Duanyang Zhuang, Jia Liu, Qingdong Li and Yunmin Chen

Energy, 2022, vol. 254, issue PA

Abstract: 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 machine-learning-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 V is 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 × 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.

Keywords: Energy storage salt cavern; Solution mining construction; Capacity prediction; Artificial neural network; Machine learning (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544222011410
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:254:y:2022:i:pa:s0360544222011410

DOI: 10.1016/j.energy.2022.124238

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().