Yazar "Cosgun, Ahmet" seçeneğine göre listele

Listeleniyor 1 - 7 / 7
  • Küçük Resim Yok
    Öğe
    Analysis of lipid production from Yarrowia lipolytica for renewable fuel production by machine learning
    (Elsevier Sci Ltd, 2022) Cosgun, Ahmet; Gunay, M. Erdem; Yildirim, Ramazan
    In this work, biomass and lipid productivities of Yarrowia lipolytica were analyzed using machine learning techniques. A dataset containing 356 instances was constructed from the experimental results reported in 22 publications. The dataset was analyzed using decision trees to identify the features (descriptors) that lead to high biomass production, lipid content and lipid production. C/N ratio and fermentation time were found to be the most influential features for biomass production while the use of glucose and medium pH seemed to be more important for high lipid content. For the lipid production case, five generalizable paths leading to high values of this output were identified. One of those paths required pH to be < 6.3, high glucose and (NH4)(2)SO4 concentrations, lower concentration for yeast extract and the yeast strain not be H-222. Another one needed a pH greater than 6.3, a C/N ratio smaller than 75, a time greater than 14 h, and a strain other than W29. The same dataset was also explored deeper using association rule mining to determine the effects of individual features on output variables. It was then concluded that machine learning methods are very useful in determining the optimal conditions of biomass growth and lipid yield for Yarrowia lipolytica to produce renewable biofuels.
  • Küçük Resim Yok
    Öğe
    Explainable machine learning analysis of tri-reforming of biogas for sustainable syngas production
    (Pergamon-Elsevier Science Ltd, 2025) Cosgun, Ahmet; Gunay, M. Erdem; Yildirim, Ramazan
    In this work, tri-reforming (TRM) of biogas was investigated using a variety of machine learning (ML) tools for knowledge extraction. For this purpose, a comprehensive database including 1183 data entries with 41 descriptors and 3 performance measures (CH4 conversion, CO2 conversion, and H2/CO ratio) was compiled from 29 articles published between 2004 and 2024. Random forest (RF) models were constructed to predict the values of performance measures that can be obtained under unknown conditions; the models were usually quite successful in the majority of the cases with the training/testing R2 values of 0.99/0.87, 0.99/0.91 and 0.96/0.58 for CH4 conversion, CO2 conversion, and H2/CO ratio respectively. To bring some explainability to the predictive models, the SHapley Additive exPlanations (SHAP) analysis was performed to determine the importance of descriptors and their effects on the performance measures. Among many results, SHAP analysis of CH4 conversion revealed the most important variable to be the reaction temperature, followed by calcination time, H2O and O2 percentages in the reaction stream, and W/F ratio. Lastly, to improve the explainability of ML even more, DT classification analysis was successfully used to generate heuristic rules that describe the combinations of individual descriptors leading to different levels of the target variables.
  • Küçük Resim Yok
    Öğe
    Exploring the critical factors of algal biomass and lipid production for renewable fuel production by machine learning
    (Pergamon-Elsevier Science Ltd, 2021) Cosgun, Ahmet; Gunay, M. Erdem; Yildirim, Ramazan
    In this work, the algal biomass productivity and its lipid content were explored using a database containing 4670 instances extracted from the experimental results reported in 102 published articles. First, the influences of critical factors such as microalgae species, cultivation conditions, light intensity, CO2 amount, nutrient concentrations, reactor type, stress conditions, cell disruption methods, and lipid extraction solvents on the biomass and lipid production were reviewed. Then, the database was analyzed using machine learning techniques; decision trees were utilized to determine the combination of variables leading to high biomass and lipid content while association rule mining was used to find the specific conditions leading to very high biomass and lipid levels. Decision tree analysis discovered 11 different combinations of variables leading to high biomass productivity and 13 combinations for high lipid content; whereas, association rule mining analysis helped to identify the levels of specific factors for very high biomass and lipid production. It was then concluded that machine learning methods can help to determine the best conditions for optimum biomass growth and lipid yield for microalgae to manufacture renewable biofuels, and this can guide the planning of new experimental works. (C) 2020 Elsevier Ltd. All rights reserved.
  • Küçük Resim Yok
    Öğe
    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.
  • Küçük Resim Yok
    Öğe
    Machine learning for algal biofuels: a critical review and perspective for the future
    (Royal Soc Chemistry, 2023) Cosgun, Ahmet; Gunay, M. Erdem; Yildirim, Ramazan
    In this work, machine learning (ML) applications in microalgal biofuel production are reviewed. First, the basic steps of algal biofuel production are summarized followed by a bibliometric analysis to demonstrate the major research trends in the field. Also, the major challenges related to the commercialization of technology are identified. Then, ML applications for various steps in the value chain are reviewed and analyzed systematically. Finally, a future perspective on the contribution of ML in the field is provided. Our analysis indicates that ML applications should focus on screening and selecting suitable strains, preferably together with some other value-added products, requiring close collaborations among the researchers in the field to construct an extensive microalgal strain database. Optimization of cultivation conditions appears to be another area where ML can be helpful. Although most published ML works on cultivation are not usually suitable to extract generalizable knowledge (due to the nonstandard nature of strains, wastewater, and irradiation), standard testing and methodologies related to reporting protocols should also be built through collaboration to build comparable and generalizable ML models.
  • Küçük Resim Yok
    Öğe
    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.
  • Küçük Resim Yok
    Öğe
    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.