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    Assessment of hydrogen production methods for global energy transition using aI enhanced quantum recommender fuzzy modelling
    (2024) Dinçer, Hasan; Yüksel, Serhat; Eti, Serkan; Acar, Merve
    The main performance indicators of hydrogen energy production should be improved. However, improving these factors also increase the operational costs of the companies. Because of this issue, there is a need for a priority analysis so that it can be possible to focus on more important factors. Accordingly, the purpose of this study is to evaluate hydrogen production methods for global energy transition. In this process, a four-stage model has been proposed by getting evaluations from three different experts. Firstly, artificial intelligence-based decision-making can be implemented for expert prioritization. In the second stage, recommender system is conducted with collaborative filtering to complete the missing evaluations. Thirdly, selected criteria are weighted by using M-SWARA with QPFRS. Finally, method alternatives for hydrogen production are ranked via quantum picture fuzzy rough sets adopted VIKOR. The biggest contribution for doing this study is that artificial intelligence technique is integrated into the model and experts' importance coefficients are can be computed. Additionally, by using the collaborative filtering technique, empty evaluations can be filled scientifically. This contributes to the quality of the analysis process in many ways. Thanks to this technique, experts are given the opportunity not to answer questions they are not very sure about. The findings indicate that renewable energy expansion, energy efficiency and sustainable development are the most important criteria for global energy transition in hydrogen production. On the other side, the ranking results give information that thermal processes including steam methane reforming and biomass gasification is the most appropriate method alternatives for hydrogen production. Based on these analysis results, it is strongly recommended that research and development activities should be improved to increase the efficiency and effectiveness of the renewable energy projects. With the help of this issue, it can be much easier to increase the performance of hydrogen production process.
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    Assessment of water electrolysis projects for green hydrogen production with a novel hybrid q-learning algorithm and molecular fuzzy-based modelling
    (2024) Dinçer, Hasan; Eti, Serkan; Acar, Merve; Yüksel, Serhat
    Determining the most important criteria for increasing the efficiency of water electrolysis investments provides businesses with a competitive advantage. Although there are many studies in the literature on this importance, there are very few studies determining the most important of these performance indicators. To satisfy this gap, the purpose of this study is to make assessment of water electrolysis projects for green hydrogen production via a novel model. First, the balanced expert evaluation matrices are obtained by q-learning algorithm. Secondly, the criteria for water electrolysis investments are prioritized using molecular fuzzy Bayesian network (BANEW). Thirdly, green hydrogen strategies for water electrolysis investments are ranked with molecular fuzzy multi-objective particle swarm optimization (MOPSO). The most important contribution of this study to the literature is the determination of the criteria that should be applied primarily for the performance increase of water electrolysis investments by creating a new model. The use of molecular fuzzy numbers is a very important contribution of the model to the literature. In this process, the use of three-dimensional geometric figures allows the reduction of uncertainties in decision-making processes. The findings indicate that lifespan of electrolysers and production capacity are the most essential criteria. Additionally, proton exchange membrane electrolysers and alkaline water electrolysis are found as the most critical green hydrogen strategies. Extending the life of electrolysers is crucial to increase sustainability in hydrogen production and reduce long-term costs. In this context, research incentives should be provided for the development of materials and technologies to increase the durability of electrolysers. Similarly, establishing quality standards to extend the life of electrolysers also contributes to achieving this goal.
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    Financial multidimensional assessment of a green hydrogen generation process via an integrated artificial intelligence-based four-stage fuzzy decision-making model
    (2024) Yüksel, Serhat; Dinçer, Hasan; Acar, Merve; Ergün, Edanur; Eti, Serkan; Gökalp, Yaşar
    It is widely accepted that there is an urgent need to make green hydrogen (GH2) projects financially viable to reduce global warming. However, any form of improvements to these GH2 projects lead to substantial cost increase. Due to this cost increase, making many improvements negatively affects the financial profitability of hydrogen projects. This is why there is a need for new advanced financial priority analysis tools so that it is easier to develop GH2 projects globally. Accordingly, the aim of this study is to identify and then define the most important factors affecting GH2 generation projects. To achieve this aim, this work proposes a new fuzzy multi-criteria decision-making model based on artificial intelligence (AI). First, experts are weighted with AI technique. Second, the missing evaluations are filled via a recommender system. Third, criteria weights are calculated by the M-SWARA technique integrated with quantum picture fuzzy rough (QPFR) sets. Finally, GH2 energy generation processes are listed by the QPFR-VIKOR approach. Overall, the main contribution of this study is the generation of a comprehensive AI oriented fuzzy decision-making model to make a detailed evaluation with respect to the financial potential improvements of the GH2 generation projects. The main originality of this model is the consideration of AI to calculate the weights of the criteria. Similarly, another benefit of the proposed model, that increases its superiority against other models, is the completion of missing evaluations by experts thanks to the recommender system. It is concluded that the most important criterion affecting green hydrogen investments is organizational effectiveness.
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    Kinesio taping or sham taping in knee osteoarthritis? A randomized, double-blind, sham-controlled trial
    (Churchill Livingstone, 2015) Koçyi?it, Figen; Türkmen, Mehmet Beşir; Acar, Merve; Güldane, Nezahat; Köse, Tuğce; Kuyucu, Ersin; Erdil, Mehmet
    Purpose: To compare effects of kinesio taping with sham taping at the end of 3 consecutive taping periods in knee osteoarthritis. Methods: 41 patients diagnosed with knee osteoarthritis according to American College of Rheumatology were randomized to receive either KT or sham taping. Baseline evaluations included a visual analog scale (VAS) for activity and nocturnal pain, Lequesne index for functional assessment and Nottingham Health Profile (NHP) for the quality of life. Taping was applied every four days, three times, and all of the assessments were repeated at the end of the treatment period. Results: In both groups VAS for activity pain, VAS for nocturnal pain, Lequesne index score, NHP score decreased significantly. NHP energy scores were different significantly between the groups in favor of sham taping at the end of the 12-day period. Conclusion: Our findings indicate inconclusive evidence of a beneficial effect of kinesio taping over sham taping in knee osteoarthritis.
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    Technical assessment of solar energy storage investments with recommender system-enhanced quantum picture fuzzy rough sets
    (2024) Kou, Gang; Dinçer, Hasan; Yüksel, Serhat; Eti, Serkan; Acar, Merve
    The performance of solar energy storage projects should be improved by taking appropriate actions. However, there are very different criteria that affect the performance of these investments. Therefore, businesses need to focus on more important criteria to use the budget effectively and efficiently. This situation increases the need for a priority analysis for performance indicators of solar energy storage investments. Accordingly, the purpose of this study is to make evaluation for the technical assessment of solar energy storage investments. In this scope, a new four-stage model is introduced by considering different decision-making techniques and fuzzy sets. The first stage is related to the prioritizing the experts with artificial intelligence (AI)-based decision-making method. Secondly, the missing evaluations of solar energy storage investments are estimated with expert recommender system. In the following part, the criteria for the technical assessment of solar energy storage investments are weighted by quantum picture fuzzy rough sets (QPFRS) adopted M−SWARA. The final stage consists of ranking the solar energy storage alternatives with QPFR-VIKOR. The main contribution of this study is the generation of the decision matrix by the help of AI. This situation gives an opportunity to calculate the significance weights of the experts. Therefore, the analysis results can be more reliable and coherent. It is concluded that battery capacity is the most critical factor for the technical assessment of solar energy storage investments. On the other hand, pumped hydro for mechanic energy is found as the most significant solar energy storage alternative. Governments should provide the necessary incentives for the development of high-capacity battery technologies. In this context, tax reductions can be provided to companies that invest in production technologies. This contributes to the cost efficiency of businesses.