Yazar "Oral, Burcu" seçeneğine göre listele

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    Machine learning for a sustainable energy future
    (Royal Soc Chemistry, 2025) Oral, Burcu; Cosgun, Ahmet; Kilic, Aysegul; Eroglu, Damla; Gunay, M. Erdem; Yildirim, Ramazan
    Energy production is one of the key enablers for human activities such as food and clean water production, transportation, telecommunication, education, and healthcare; however, it is also the main cause of global warming. Hence, sustainable energy is critical for most United Nations (UN) Sustainable Development Goals (SDGs), and it is directly targeted in SDG7. In this review, we analyze the potential role of machine learning (ML), another enabler technology, in sustainable energy and SGDs. We review the use of ML in energy production and storage as well as in energy forecasting and planning activities and provide our perspective on the challenges and opportunities for the future role of ML. Although there are strong challenges for both sustainable energy supply (like conflict between the urgent energy needs and global warming) and ML applications (like high energy consumption in ML applications and risk of increasing inequalities among people and nations), ML may make significant contributions to sustainable energy efforts and therefore to the achievement of SDGs through monitoring and remote sensing to collect data, planning the worldwide efforts and improving the performance of new and more sustainable energy technologies.
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    Machine Learning-Based Analysis of Sustainable Biochar Production Processes
    (Springer, 2024) Cosgun, Ahmet; Oral, Burcu; Gunay, M. Erdem; Yildirim, Ramazan
    Biochar production from biomass sources is a highly complex, multistep process that depends on several factors, including feedstock composition (e.g., type of biomass, particle size) and operating conditions (e.g., reaction temperature, pressure, residence time). However, the optimal set of variables for producing the maximum amount of biochar with the required characteristics can be determined by using machine learning (ML). In light of this, the purpose of this paper is to examine ML applications in biochar processes for the production of sustainable fuels. First, recent developments in the field are summarized, and then, a detailed review of ML applications in biochar production is presented. Following that, a bibliometric analysis is done to illustrate the major trends and construct a comprehensive perspective for future studies. It is found that biochar yield is the most common target variable for ML applications in biochar production. It is then concluded that ML can help to detect hidden patterns and make accurate predictions for determining the combination of variables that results in the desired properties of biochar which can be later used for decision-making, resource allocation, and fuel production.
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    Machine learning-based exploration of biochar for environmental management and remediation
    (Academic Press Ltd- Elsevier Science Ltd, 2024) Oral, Burcu; Cosgun, Ahmet; Guenay, M. Erdem; Yildirim, Ramazan
    Biochar has a wide range of applications, including environmental management, such as preventing soil and water pollution, removing heavy metals from water sources, and reducing air pollution. However, there are several challenges associated with the usage of biochar for these purposes, resulting in an abundance of experimental data in the literature. Accordingly, the purpose of this study is to examine the use of machine learning in biochar processes with an eye toward the potential of biochar in environmental remediation. First, recent developments in biochar utilization for the environment are summarized. Then, a bibliometric analysis is carried out to illustrate the major trends (demonstrating that the top three keywords are heavy metal, wastewater, and adsorption) and construct a comprehensive perspective for future studies. This is followed by a detailed review of machine learning applications, which reveals that adsorption efficiency and capacity are the primary utilization targets in biochar utilization. Finally, a comprehensive perspective is provided for the future. It is then concluded that machine learning can help to detect hidden patterns and make accurate predictions for determining the combination of variables that results in the desired properties which can be later used for decision-making, resource allocation, and environmental management.