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Abstract The porous underground structures have recently attracted researchers’ attention for hydrogen gas storage due to their high storage capacity. One of the challenges in storing hydrogen gas in aqueous solutions is estimating its solubility in water. In this study, after collecting experimental data from previous research and eliminating four outliers, nine machine learning methods were developed to estimate the solubility of hydrogen in water. To optimize the parameters used in model construction, a Bayesian optimization algorithm was employed. By examining error functions and plots, the LSBoost method with R² = 0.9997 and RMSE = 4.18E-03 was identified as the most accurate method. Additionally, artificial neural network, CatBoost, Extra trees, Gaussian process regression, bagged trees, regression trees, support vector machines, and linear regression methods had R² values of 0.9925, 0.9907, 0.9906, 0.9867, 0.9866, 0.9808, 0.9464, and 0.7682 and RMSE values of 2.13E-02, 2.43E-02, 2.44E-02, 2.83E-02, 2.85E-02, 3.40E-02, 5.68E-02, and 1.18E-01, respectively. Subsequently, residual error plots were generated, indicating the accurate performance of the LSBoost model across all ranges. The maximum residual error was − 0.0252, and only 4 data points were estimated with an error greater than ± 0.01. A kernel density estimation (KDE) plot for residual errors showed no specific bias in the models except for the linear regression model. To investigate the impact of temperature, pressure, and salinity parameters on the model outputs, the Pearson correlation coefficients for the LSBoost model were calculated, showing that pressure, temperature, and salinity had values of 0.8188, 0.1008, and − 0.5506, respectively, indicating that pressure had the strongest direct relationship, while salinity had an inverse relationship with hydrogen solubility. Considering the results of this research, the LSBoost method, alongside approaches like state equations, can be applied in real-world scenarios for underground hydrogen storage. The findings of this study can help in a better understanding of hydrogen solubility in aqueous solutions, aiding in the optimization of underground hydrogen storage systems.

ABSTRACTA backpropagation neural network (BPNN) was used to predict salinity levels in the Chott Djerid shallow aquifers. A set of 51 water samples was collected from the Chott Djerid plio-quaternary aquifers for geochemical analysis. Major elements and nitrates were ascertained by using high performance liquid-ion chromatography. The BPNN was trained on a dataset of 51 water samples with variable geochemical parameters. Our results indicated a high accuracy when applying a model with 13 inputs, 1 hidden layer (6 neurons) and 1 output (TDS in mg/L). The collected data were split into 80% for training the model and 20% for testing and cross validation. The result was evaluated using various statistical performance criteria (i.e., MSE, RMSE, R2, SSE, SD, Accuracy, Sensitivity, specificity, and Kappa test); it showed that BPNN model properly predicted the salinity of the Chott Djerid plio-quaterny water samples (RMSE = 0.0402; R2 = 0.9721 and SSE = 0.0146). The BPNN was able to capture the complex relationship between salinity levels and other aquifer parameters. The potential application of BPNNs for predicting salinity levels in shallow aquifers was crucial in supporting decision-makers for water management; it provided valuable insights into the salinity fluctuation of the studied shallow aquifers.

Arsenic is found in significant quantities within the alluvial aquifers. Bangladesh heavily relies on the alluvial aquifers of the Bengal Basin as a source of irrigation and drinking water. Due to the flat topography, arsenic within an aquifer is not easily flushed out of the system. Additionally, continuous, unregulated pumping causes arsenic from deeper aquifers to migrate to shallower levels. This study simulates groundwater and contaminant transport in the alluvial aquifers of the Bengal Basin by comparison between two scenarios prior to human intervention with different sea levels, employing a combination of MODFLOW, MODPATH and MT3DMS. The simulations demonstrate that the hydraulic gradient and flow rates were higher during periods of considerably lower sea levels than they are at present. Additionally, it would require 5,600 years for the Holocene aquifer and 3,300 years for the Last Glacial Period aquifer to flush arsenic to the Bangladesh standard concentrations in drinking water in a 100-m-thick contaminated aquifer. This implies that if the sea level continues to rise with climate change, it will be difficult to remove arsenic from the alluvial aquifers in the Bengal Basin by natural flushing, which means artificial interventions need to be done for the interest of nation's health. HIGHLIGHTS A general model of regional groundwater flow is developed to estimate the remediation potential of arsenic in the Bengal Basin.; The hydraulic gradient and flow rates were higher during periods of considerably lower sea levels than they are at present.; It requires 5,600 and 3,300 years for Holocene and Last Glacial Period aquifers, respectively, to flush arsenic to the Bangladesh standard concentrations in drinking water.;

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Due to the concentrated carbon emission sources and the enormous carbon emissions in the Ningxia Hui Autonomous, it is necessary to evaluate carbon sequestration potential to support future CCUS planning and major demonstrations. Based on the basic geological conditions for carbon sequestration in Ningxia, this study used the volumetric method of the US Department of Energy (US-DOE) to assess the geological potential for deep saline aquifer sequestration in Ningxia. The results show that the predicted geological potential of carbon sequestration in the deep saline aquifers in Ningxia is approximately 86.742 billion tons, with the predicted technical capacity of about 42.334 billion tons. The main reservoirs are concentrated in the Yanchi area in eastern Ningxia and the Liupan Mountain Basin. A preliminary evaluation of the suitability of saline aquifer sequestration in Ningxia was conducted by considering many factors such as the overall sequestration potential, cap rock integrity, geological stability, and socio-environmental risks. The results indicate that the Yanchi area in eastern Ningxia has favorable reservoir and cap rock conditions, stable structures, and low population density, making it a potential region for CO2 sequestration in Ningxia.

Underground hydrogen storage is critical for renewable energy integration and sustainability. Saline aquifers and depleted oil and gas reservoirs represent viable large-scale hydrogen storage solutions due to their capacity and availability. This paper provides a comparative analysis of the current status of hydrogen storage in various environments. Additionally, it assesses the geological compatibility, capacity, and security of these storage environments with minimal leakage and degradation. An in-depth analysis was also conducted on the economic and environmental issues that impact the hydrogen storage. In addition, the capacity of these structures was also clarified, and it is similar to storing carbon dioxide, except for the cushion gas that is injected with hydrogen to provide pressure when withdrawing from the store to increase demand. This research also discusses the pros and cons of hydrogen storage in saline aquifers and depleted oil and gas reservoirs. Advantages include numerous storage sites, compatibility with existing infrastructure, and the possibility to repurpose declining oil and gas assets. Specifically, it was identified that depleted gas reservoirs are better for hydrogen gas storage than depleted oil reservoirs because hydrogen gas may interact with the oil. The saline aquifers rank third because of uncertainty, limited capacity, construction and injection costs. The properties that affect the hydrogen injection process were also discussed in terms of solid, fluid, and solid-fluid properties. In all structures, successful implementation requires characterizing sites, monitoring and managing risks, and designing efficient storage methods. The findings expand hydrogen storage technology and enable a renewable energy-based energy system.