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: 14Citation - Scopus: 15Predictive Modeling of Photocatalytic Hydrogen Production: Integrating Experimental Insights With Machine Learning on Fe/G-c3n4 Catalysts(Amer Chemical Soc, 2025) Arabaci, Bahriyenur; Bakir, Rezan; Orak, Ceren; Yuksel, AsliHydrogen emerges as a promising alternative to fossil fuels with its pollutant-free emissions, high energy density, versatility, and efficiency in generating power. In this study, photocatalytic hydrogen production from using 1000 ppm of model solution prepared with sucrose was investigated in the presence of Fe/g-C3N4 photocatalysts over Box-Behnken experimental design developed using the Minitab statistical software. The amount of hydrogen produced was optimized at different pH environments (3, 5, and 7) for 2 h reaction time with different amounts of metal loaded (10, 20, and 30 wt %), Fe/g-C3N4 (0.1, 0.2, and 0.3 g/L), and oxidant (H2O2; 0, 10, and 20 mM) concentrations. SEM, BET, XRD, FTIR, and PL analyses were employed for the characterization of synthesized photocatalysts. According to the response optimization, using Fe/g-C3N4, the optimal conditions for hydrogen production were found as 0.3 g/L catalyst loading, 18.8 mM H2O2, and 26.6% Fe loading by mass when the pH was 3 for the reaction medium. Furthermore, machine learning algorithms were employed to predict hydrogen evolution based on experimental parameters. Notably, ensemble models such as Voting Regressor combining the Bagging Regressor, Random Forest Regressor, LGBM Regressor, Extra Trees Regressor, XGB Regressor, and Gradient Boosting Regressor achieved superior performance with a mean squared error of 0.0068 and R-squared (R 2) of 0.9895. This integrated approach demonstrates the efficacy of machine learning in optimizing photocatalytic hydrogen generation processes.