A Novel Sustainable Approach for Site Selection of Underground Hydrogen Storage in Poland Using Deep Learning

被引:5
作者
Derakhshani, Reza [1 ,2 ]
Lankof, Leszek [3 ]
GhasemiNejad, Amin [4 ]
Zarasvandi, Alireza [5 ]
Zarin, Mohammad Mahdi Amani [6 ]
Zaresefat, Mojtaba [7 ]
机构
[1] Univ Utrecht, Dept Earth Sci, NL-3584 CB Utrecht, Netherlands
[2] Shahid Bahonar Univ Kerman, Dept Geol, Kerman 7616913439, Iran
[3] Polish Acad Sci, Mineral & Energy Econ Res Inst, Wybickiego 7A, PL-31261 Krakow, Poland
[4] Shahid Bahonar Univ Kerman, Fac Management & Econ, Dept Econ, Kerman 7616913439, Iran
[5] Shahid Chamran Univ Ahvaz, Fac Earth Sci, Dept Geol, Ahvaz 6135743136, Iran
[6] Shahid Bahonar Univ Kerman, Dept Comp Sci, Kerman 7616913439, Iran
[7] Univ Utrecht, Copernicus Inst Sustainable Dev, NL-3584 CB Utrecht, Netherlands
关键词
underground hydrogen storage; deep learning; site selection; convolutional neural networks; sustainable energy storage; SALT CAVERNS; SHAPE;
D O I
10.3390/en17153677
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
This research investigates the potential of using bedded salt formations for underground hydrogen storage. We present a novel artificial intelligence framework that employs spatial data analysis and multi-criteria decision-making to pinpoint the most appropriate sites for hydrogen storage in salt caverns. This methodology incorporates a comprehensive platform enhanced by a deep learning algorithm, specifically a convolutional neural network (CNN), to generate suitability maps for rock salt deposits for hydrogen storage. The efficacy of the CNN algorithm was assessed using metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Square Error (RMSE), and the Correlation Coefficient (R2), with comparisons made to a real-world dataset. The CNN model showed outstanding performance, with an R2 of 0.96, MSE of 1.97, MAE of 1.003, and RMSE of 1.4. This novel approach leverages advanced deep learning techniques to offer a unique framework for assessing the viability of underground hydrogen storage. It presents a significant advancement in the field, offering valuable insights for a wide range of stakeholders and facilitating the identification of ideal sites for hydrogen storage facilities, thereby supporting informed decision-making and sustainable energy infrastructure development.
引用
收藏
页数:13
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