FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4360884906> ?p ?o ?g. }

• W4360884906 endingPage "128183" @default.
• W4360884906 startingPage "128183" @default.
• W4360884906 abstract "This study explores the use of machine learning algorithms to predict hydrogen wettability in underground storage sites. The motivation for this research is the need to find a safe and efficient way to store hydrogen, which has become increasingly important as the world shifts toward using more clean energy sources. The study used four different machine learning algorithms including XGBoost, RF, LGRB, and Adaboost_DT to analyze 513 data points collected from previous literature. The input features included pressure, temperature, salinity, and substrate types, while the target output was hydrogen wettability. This study found that the XGBoost algorithm with four inputs produced the most precise predictions with the highest R2 value of 0.941, the lowest RMSE value of 4.455, and MAE of 2.861 for the overall databank. Based on SHAP values, the substrates are the most impactful variables of the XGBoost model. The predicted hydrogen column height was also evaluated for a specific storage site in Australia's basalt formation. At 308 K, the predicted hydrogen column height decreased from 1991 to 1319 m, while at 343 K, it decreased from 1510 to 784 m. These predictions were compared to the real column height that fell from 1660 to 928 m in the same pressure range. Overall, the study's findings provide a valuable guide for predicting wettability and evaluating hydrogen column height in specific storage sites. The use of machine learning algorithms can significantly reduce the time, cost, and unpredictability associated with traditional methods of assessing hydrogen wettability in underground storage sites." @default.
• W4360884906 created "2023-03-25" @default.
• W4360884906 creator A5005583322 @default.
• W4360884906 creator A5008497258 @default.
• W4360884906 creator A5035701638 @default.
• W4360884906 creator A5074629462 @default.
• W4360884906 creator A5085914326 @default.
• W4360884906 date "2023-08-01" @default.
• W4360884906 modified "2023-10-17" @default.
• W4360884906 title "Predicting the wettability rocks/minerals-brine-hydrogen system for hydrogen storage: Re-evaluation approach by multi-machine learning scheme" @default.
• W4360884906 cites W1988790447 @default.
• W4360884906 cites W2759565175 @default.
• W4360884906 cites W2784208206 @default.
• W4360884906 cites W2795411881 @default.
• W4360884906 cites W2911964244 @default.
• W4360884906 cites W2941989956 @default.
• W4360884906 cites W2950630445 @default.
• W4360884906 cites W2969537986 @default.
• W4360884906 cites W2973035317 @default.
• W4360884906 cites W2973817809 @default.
• W4360884906 cites W2981802428 @default.
• W4360884906 cites W2987471655 @default.
• W4360884906 cites W3014843024 @default.
• W4360884906 cites W3023882051 @default.
• W4360884906 cites W3032477746 @default.
• W4360884906 cites W3083205481 @default.
• W4360884906 cites W3084009773 @default.
• W4360884906 cites W3097723090 @default.
• W4360884906 cites W3102476541 @default.
• W4360884906 cites W3120083494 @default.
• W4360884906 cites W3120263324 @default.
• W4360884906 cites W3154048915 @default.
• W4360884906 cites W3164382478 @default.
• W4360884906 cites W3169582640 @default.
• W4360884906 cites W3197082922 @default.
• W4360884906 cites W3201550277 @default.
• W4360884906 cites W3201622017 @default.
• W4360884906 cites W3205997185 @default.
• W4360884906 cites W3210620395 @default.
• W4360884906 cites W3217014542 @default.
• W4360884906 cites W4200344755 @default.
• W4360884906 cites W4206652799 @default.
• W4360884906 cites W4210983444 @default.
• W4360884906 cites W4213038212 @default.
• W4360884906 cites W4220703952 @default.
• W4360884906 cites W4221124994 @default.
• W4360884906 cites W4224020120 @default.
• W4360884906 cites W4225154543 @default.
• W4360884906 cites W4280491920 @default.
• W4360884906 cites W4280633468 @default.
• W4360884906 cites W4283027656 @default.
• W4360884906 cites W4285007039 @default.
• W4360884906 cites W4285040342 @default.
• W4360884906 cites W4296919082 @default.
• W4360884906 cites W4310016399 @default.
• W4360884906 doi "https://doi.org/10.1016/j.fuel.2023.128183" @default.
• W4360884906 hasPublicationYear "2023" @default.
• W4360884906 type Work @default.
• W4360884906 citedByCount "3" @default.
• W4360884906 countsByYear W43608849062023 @default.
• W4360884906 crossrefType "journal-article" @default.
• W4360884906 hasAuthorship W4360884906A5005583322 @default.
• W4360884906 hasAuthorship W4360884906A5008497258 @default.
• W4360884906 hasAuthorship W4360884906A5035701638 @default.
• W4360884906 hasAuthorship W4360884906A5074629462 @default.
• W4360884906 hasAuthorship W4360884906A5085914326 @default.
• W4360884906 hasConcept C11413529 @default.
• W4360884906 hasConcept C119857082 @default.
• W4360884906 hasConcept C127413603 @default.
• W4360884906 hasConcept C134514944 @default.
• W4360884906 hasConcept C154945302 @default.
• W4360884906 hasConcept C178790620 @default.
• W4360884906 hasConcept C185592680 @default.
• W4360884906 hasConcept C192562407 @default.
• W4360884906 hasConcept C199289684 @default.
• W4360884906 hasConcept C2776957854 @default.
• W4360884906 hasConcept C33923547 @default.
• W4360884906 hasConcept C41008148 @default.
• W4360884906 hasConcept C42360764 @default.
• W4360884906 hasConcept C512968161 @default.
• W4360884906 hasConcept C68044625 @default.
• W4360884906 hasConceptScore W4360884906C11413529 @default.
• W4360884906 hasConceptScore W4360884906C119857082 @default.
• W4360884906 hasConceptScore W4360884906C127413603 @default.
• W4360884906 hasConceptScore W4360884906C134514944 @default.
• W4360884906 hasConceptScore W4360884906C154945302 @default.
• W4360884906 hasConceptScore W4360884906C178790620 @default.
• W4360884906 hasConceptScore W4360884906C185592680 @default.
• W4360884906 hasConceptScore W4360884906C192562407 @default.
• W4360884906 hasConceptScore W4360884906C199289684 @default.
• W4360884906 hasConceptScore W4360884906C2776957854 @default.
• W4360884906 hasConceptScore W4360884906C33923547 @default.
• W4360884906 hasConceptScore W4360884906C41008148 @default.
• W4360884906 hasConceptScore W4360884906C42360764 @default.
• W4360884906 hasConceptScore W4360884906C512968161 @default.
• W4360884906 hasConceptScore W4360884906C68044625 @default.
• W4360884906 hasLocation W43608849061 @default.
• W4360884906 hasOpenAccess W4360884906 @default.

• next