Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

Permanent URI for this collectionhttps://hdl.handle.net/11147/7148

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Publisher: search.filters.publisher.Pergamon-elsevier Science LtdSubject: search.filters.subject.Hydrogen

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  • Article
    Citation - WoS: 3
    Citation - Scopus: 4
    Towards Renewable Energy Islands in Türkiye: Potential and Challenges
    (Pergamon-elsevier Science Ltd, 2025) Karipoglu, Fatih; Denizli, Osmancan
    The necessity of renewables is increasing day by day due to increasing energy demand. Therefore, novel approaches and methods for producing electricity in an environmentally friendly manner are valuable and critical. The seas have enormous potential in terms of wind, waves, solar, and hydrogen systems. The study presents the investigation of the potential dynamics for energy island formation on T & uuml;rkiye borders. Also, targets, legislation, and environmental and social concerns are discussed comprehensively. Results show that offshore wind and hydrogen are promising systems shortly while solar and wave energy needs more research for T & uuml;rkiye. The Marmara and Aegean Seas are considered technically feasible for offshore wind installations, while the Mediterranean and Aegean Seas have the highest technical solar potential. In addition, the highest wave power is detected along the line from I(center dot)zmir to Antalya Coast while hydrogen energy systems receive great interest with academic and industrial projects in the Marmara Coastline. Profiting from marine energy, marine spatial planning, and grid availability are detected as the shortcoming issues in T & uuml;rkiye. The study could give critical information to energy planners, and decision makers for potential projects.
  • Article
    Citation - WoS: 16
    Citation - Scopus: 17
    Integrating Experimental and Machine Learning Approaches for Predictive Analysis of Photocatalytic Hydrogen Evolution Using Cu/G-c3n4
    (Pergamon-elsevier Science Ltd, 2024) Arabaci, Bahriyenur; Bakir, Rezan; Orak, Ceren; Yuksel, Asli
    This study addresses environmental issues like global warming and wastewater generation by exploring waste-toenergy strategies that produce renewable hydrogen and treat wastewater simultaneously. Cu/g-C3N4 is used to evolve hydrogen from sucrose solution and the impact of reaction parameters such as pH (3, 5, and 7), Cu loading (5, 10, and 15 wt%), catalyst amount (0.1, 0.2, and 0.3 g/L), and oxidant (H2O2) concentration (0, 10, and 20 mM) on the evolved hydrogen amount is examined. Characterization study confirmed successful incorporation of Cu without significantly altering g-C3N4 properties. The highest hydrogen production (1979.25 mu mol g- 1 & sdot;h- 1) is achieved with 0.3 g/L catalyst, 20 mM H2O2, 5 % Cu loading, and pH 3. The experimental study concludes that Cu/g-C3N4 is an effective photocatalyst for renewable hydrogen production. In addition to the experimental investigations, various machine learning (ML) models, including Random Forest, Decision Tree, XGBoost, among others, are employed to analyze the impact of reaction parameters and forecast the quantities of produced hydrogen. Alongside these individual models, an ensemble approach is proposed and utilized. The R2 values of these ML models ranged from 0.9454 to 0.9955, indicating strong predictive performance across the board. Additionally, these models exhibited low error rates, further confirming their reliability in predicting hydrogen evolution.