Derakhshani Reza, Lankof Leszek, GhasemiNejad Amin, Zaresefat Mojtaba
Department of Earth Sciences, Utrecht University, Utrecht, The Netherlands.
Department of Geology, Shahid Bahonar University of Kerman, Kerman, Iran.
Sci Rep. 2024 Jun 20;14(1):14246. doi: 10.1038/s41598-024-64020-9.
This study explores the feasibility of utilizing bedded salt deposits as sites for underground hydrogen storage. We introduce an innovative artificial intelligence framework that applies multi-criteria decision-making and spatial data analysis to identify the most suitable locations for storing hydrogen in salt caverns. Our approach integrates a unified platform with eight distinct machine-learning algorithms-KNN, SVM, LightGBM, XGBoost, MLP, CatBoost, GBR, and MLR-creating rock salt deposit suitability maps for hydrogen storage. The performance of these algorithms was evaluated using various metrics, including Mean Squared Error (MSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), and Correlation Coefficient (R), compared against an actual dataset. The CatBoost model demonstrated exceptional performance, achieving an R of 0.88, MSE of 0.0816, MAE of 0.1994, RMSE of 0.2833, and MAPE of 0.0163. The novel methodology, leveraging advanced machine learning techniques, offers a unique perspective in assessing the potential of underground hydrogen storage. This approach is a valuable asset for various stakeholders, including government bodies, geological services, renewable energy facilities, and the chemical/petrochemical industry, aiding them in identifying optimal locations for hydrogen storage.
本研究探讨了利用层状盐矿床作为地下氢储存场所的可行性。我们引入了一种创新的人工智能框架，该框架应用多标准决策和空间数据分析来确定盐穴中储存氢气的最合适位置。我们的方法将一个统一平台与八种不同的机器学习算法——KNN、SVM、LightGBM、XGBoost、MLP、CatBoost、GBR和MLR——相结合，创建了用于氢储存的岩盐矿床适宜性地图。使用包括均方误差（MSE）、平均绝对误差（MAE）、平均绝对百分比误差（MAPE）、均方根误差（RMSE）和相关系数（R）在内的各种指标，与实际数据集相比，评估了这些算法的性能。CatBoost模型表现出卓越的性能，R为0.88，MSE为0.0816，MAE为0.1994，RMSE为0.2833，MAPE为0.0163。这种利用先进机器学习技术的新方法，在评估地下氢储存潜力方面提供了独特的视角。这种方法对于包括政府机构、地质服务机构、可再生能源设施以及化学/石化行业在内的各种利益相关者来说是一项宝贵资产，有助于他们确定氢储存的最佳位置。
