A review of underground hydrogen storage systems: Current status, modeling approaches, challenges, and future prospective

International Journal of Hydrogen Energy, Год журнала: 2024, Номер 80, С. 449 - 474

Опубликована: Июль 17, 2024

Язык: Английский

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Exploring hydrogen geologic storage in China for future energy: Opportunities and challenges

Renewable and Sustainable Energy Reviews, Год журнала: 2024, Номер 196, С. 114366 - 114366

Опубликована: Март 15, 2024

Язык: Английский

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A critical review of physics-informed machine learning applications in subsurface energy systems

Geoenergy Science and Engineering, Год журнала: 2024, Номер 239, С. 212938 - 212938

Опубликована: Май 22, 2024

Язык: Английский

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Improving wettability estimation in carbonate formation using machine learning algorithms: Implications for underground hydrogen storage applications

International Journal of Hydrogen Energy, Год журнала: 2025, Номер 111, С. 781 - 797

Опубликована: Фев. 27, 2025

Язык: Английский

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Modelling underground hydrogen storage: A state-of-the-art review of fundamental approaches and findings

Gas Science and Engineering, Год журнала: 2023, Номер 121, С. 205196 - 205196

Опубликована: Дек. 16, 2023

This review presents a State-of-Art of geochemical, geomechanical, and hydrodynamic modelling studies in the Underground Hydrogen Storage (UHS) domain. Geochemical assessed reactivity hydrogen respective fluctuations losses using kinetic reaction rates, rock mineralogy, brine salinity, integration redox reactions. Existing geomechanics offer an array coupled hydro-mechanical models, suggesting decline failure during withdrawal phase aquifers compared to injection phase. Hydrodynamic evaluations indicate critical importance relative permeability hysteresis determining UHS performance. Solubility diffusion gas appear have minimal impact on UHS. Injection production cushion deployment, reservoir heterogeneity however significantly affect performance, stressing need for thorough experimental studies. However, most current efforts focuses assessing aspects which are crucial understanding viability safety In contrast, lesser-explored geochemical geomechanical considerations point potential research gaps. Variety software tools such as CMG, Eclipse, COMSOL, PHREEQC evaluated those underlying effects, along with few recent application data-driven based Machine Learning (ML) techniques enhanced accuracy. identified several unresolved challenges modelling: pronounced lack expansive datasets, leading gap between model predictions their practical reliability; robust methodologies capable capturing natural subsurface while upscaling from precise laboratory data field-scale conditions; demanding intensive computational resources novel strategies enhance simulation efficiency; addressing geological uncertainties environments, that oil simulations could be adapted comprehensive offers synthesis prevailing approaches, challenges, gaps domain UHS, thus providing valuable reference document further efforts, facilitating informed advancements this towards realization sustainable energy solutions.

Язык: Английский

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Artificial intelligence-driven assessment of salt caverns for underground hydrogen storage in Poland

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Июнь 20, 2024

Abstract 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 most suitable locations storing in caverns. Our approach integrates a unified platform with eight distinct machine-learning algorithms—KNN, SVM, LightGBM, XGBoost, MLP, CatBoost, GBR, MLR—creating rock deposit suitability maps The performance these algorithms was evaluated using various metrics, including Mean Squared Error (MSE), Absolute (MAE), Percentage (MAPE), Root Square (RMSE), Correlation Coefficient (R 2 ), compared against actual dataset. CatBoost model demonstrated exceptional performance, achieving R 0.88, MSE 0.0816, MAE 0.1994, RMSE 0.2833, MAPE 0.0163. novel methodology, leveraging advanced machine learning techniques, offers unique perspective assessing potential is valuable asset stakeholders, government bodies, geological services, renewable energy facilities, chemical/petrochemical industry, aiding them identifying optimal

Язык: Английский

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Predictive Modeling of Energy Poverty with Machine Learning Ensembles: Strategic Insights from Socioeconomic Determinants for Effective Policy Implementation

International Journal of Energy Research, Год журнала: 2024, Номер 2024(1)

Опубликована: Янв. 1, 2024

This study aims to identify the key predictors of multidimensional energy poverty index (MEPI) by employing advanced machine learning (ML) ensemble methods. Traditional research often relies on conventional statistical techniques, which limits understanding complex socioeconomic factors. To address this gap, we propose an approach using three distinct ML models: extreme gradient boosting (XGBoost)‐random forest (RF), XGBoost‐multiple linear regression (MLR), and XGBoost‐artificial neural network (ANN). These models are applied a comprehensive dataset encompassing various indicators. The findings demonstrate that XGBoost‐RF achieves exceptional accuracy reliability, with root mean squared error (RMSE) 0.041, R ‐squared ( 2 ) 0.975, Pearson correlation coefficient 0.992. XGBoost‐MLR shows superior generalizability, maintaining consistent 0.845 across both testing training phases. XGBoost‐ANN model balances complexity predictive capability, achieving RMSE 0.056, 0.954 in phase, 0.799 training. Significantly, identifies “Education,” “Food Consumption Score (FCS),” “Household Food Insecurity Access Scale (HFIA),” “Dietary Diversity (DDS)” as critical MEPI. results highlight intricate relationship between factors related food security education. By integrating insights from these policy initiatives, offers promising new addressing poverty. It highlights importance education, security, crafting effective interventions.

Язык: Английский

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Evaluating machine learning models comprehensively for predicting maximum power from photovoltaic systems

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Март 28, 2025

This paper presents a machine learning (ML) model designed to track the maximum power point of standalone Photovoltaic (PV) systems. Due nonlinear nature generation in PV systems, influenced by fluctuating weather conditions, managing this data effectively remains challenge. As result, use ML techniques optimize systems at their MPP is highly beneficial. To achieve this, research explores various algorithms, such as Linear Regression (LR), Ridge (RR), Lasso (Lasso R), Bayesian (BR), Decision Tree (DTR), Gradient Boosting (GBR), and Artificial Neural Networks (ANN), predict The utilizes from unit's technical specifications, allowing algorithms forecast power, current, voltage based on given irradiance temperature inputs. Predicted also used determine boost converter's duty cycle. simulation was conducted 100 kW solar panel with an open-circuit 64.2 V short-circuit current 5.96 A. Model performance evaluated using metrics Root Mean Square Error (RMSE), Coefficient Determination (R2), Absolute (MAE). Additionally, study assessed correlation feature importance evaluate compatibility factors impacting predictive accuracy models. Results showed that DTR algorithm outperformed others like LR, RR, R, BR, GBR, ANN predicting (Im), (Vm), (Pm) system. achieved RMSE, MAE, R2 values 0.006, 0.004, 0.99999 for Im, 0.015, 0.0036, Vm, 2.36, 0.871, Pm. Factors size training dataset, operating conditions system, type, preprocessing were found significantly influence prediction accuracy.

Язык: Английский

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A machine learning strategy for enhancing the strength and toughness in metal matrix composites

International Journal of Mechanical Sciences, Год журнала: 2024, Номер 281, С. 109550 - 109550

Опубликована: Июль 8, 2024

Язык: Английский

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Explosive utilization efficiency enhancement: An application of machine learning for powder factor prediction using critical rock characteristics

Heliyon, Год журнала: 2024, Номер 10(12), С. e33099 - e33099

Опубликована: Июнь 1, 2024

Maximizing the use of explosives is crucial for optimizing blasting operations, significantly influencing productivity and cost-effectiveness in mining activities. This work explores incorporation machine learning methods to predict powder factor, a measure assessing effectiveness explosive deployment, using important rock characteristics. The goal enhance accuracy factor prediction by employing methods, namely decision tree models artificial neural networks. analysis finds key factors that have substantial impact on hence enabling more accurate planning execution operations. uses data from 180 blast rounds carried out at dolomite mine south-south Nigeria. It incorporates measures such as root mean square error (RSME), absolute (MAE), R-squared (R2), variance accounted (VAF) determine best predicting factor. results indicate model (MD4) outperforms alternative approaches, networks Gaussian Process Regression (GPR). In addition, research presents an efficient network equation (MD2) estimating values optimum demonstrating outstanding fragmentation. conclusion, this provides significant information improving prediction, which especially advantageous small-scale

Язык: Английский

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