Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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Article Machine Learning Integrated Solvothermal Liquefaction of Lignocellulosic Biomass to Maximize Bio-Oil Yield(Elsevier Sci Ltd, 2025) Ocal, Bulutcem; Sildir, Hasan; Yuksel, AsliAccelerating consumption of limited fossil-based for economic growth and simultaneously mitigating greenhouse gas emissions create a dilemma that is waiting to be solved by researchers. In this context, solvothermal liquefaction of lignocellulosic biomass to produce bio-oil is a promising way to obtain green energy. However, maximizing bio-oil is challenging to optimize the operating parameters employing conventional techniques due to the complexity and non-linearity of the process. Lately, machine learning approaches have become powerful tools for addressing complex nonlinear problems by predicting process behavior and regulating operating parameters for optimization by learning from datasets. The current research demonstrates integrating experimental and a developed artificial neural network model to optimize solvothermal liquefaction of pinus brutia, based on temperature, water fraction, and biomass amount in maximizing bio-oil generation for the first time. The highest bio-oil yields were obtained at 31.40 %, 18.68 %, and 39.69 %, respectively, with 4 and 8 g biomass in the presence of water, ethanol, and water/ethanol mixture at 240 degrees C. Under the model conditions, the maximum biooil yield was experimentally verified at 46.20%, which was predicted at 48.8 %. Beyond providing accurate yield predictions, the approach highlights the potential of date-driven modeling to reduce experimental workload and cost while aiding parameter selection to improve efficiency. These outcomes emphasize the importance of machine learning integration into liquefaction process, providing remarkable results for future process design, optimization, and scalability. On the other hand, the study also includes characterization results (ultimate, proximate, FTIR, and GC-MS) of selected products and pinus brutia.Article Citation - WoS: 5Citation - Scopus: 7Polycentricity and Regional Economic Resilience: a Ridge Regression Approach(Elsevier Sci Ltd, 2025) Cifci, Burcu Degerli; Duran, Hasan EnginResilience and "polycentricity" have surged as popular concepts over the recent decades, although the link between the two has not yet been investigated empirically. Identification of this relationship and its theoretical justification are politically crucial to shed light on prospective policies for urbanization and regionalization. Thus, the aim of this study is to investigate the impact of polycentricity/monocentricity on the regional resilience of Turkish (Nuts-2) regions against the global financial crisis in 2008/09. This paper also identifies the channels through which it can influence resilience. Through the application of a rich set of empirical tools, including computation of monocentricity degree, resistance, recovery, and adaptability indexes based on national and regional business cycle turning points, LOESS, RIDGE regressions, and inferential mediation tests, three main conclusions were obtained. First, polycentric regions were evidently more resistant to the crisis compared to monocentric morphologies; the later were more industrialized and open to trade, which made them more vulnerable to the crisis. Second, polycentric spatial structures were found to recover more quickly compared to monocentric regions. Third, monocentric regions clearly adapt better to long-term trajectories. In sum, the wellknown strategy of the European Union rooted in "polycentric development" can still be valid for the purposes such as resisting to and recovering from economic disruptions. However, in the long-run, polycentrilization can hardly be seen as an optimal strategy, particularly in the context of adapting to the future trajectories.