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 Pvc/Pan-immobilized H2TiO3 Adsorbent: a Tailored Titanium-Based Lithium-Ion Sieve for High-Performance Lithium Recovery(Royal Soc Chemistry, 2025) Recepoglu, Yasar Kemal; Ipek, Onur; Yuksel, AsliThe increasing demand for lithium, driven by the rapid development of electric vehicles and energy storage systems, has created a pressing need for efficient and sustainable lithium recovery technologies. Conventional methods often face challenges related to selectivity, environmental impact, and scalability, necessitating the development of alternative materials. In this study, a polyvinyl chloride/polyacrylonitrile (PVC/PAN)-immobilized titanium-based lithium-ion sieve (HTO) was synthesized for lithium recovery from aqueous media, including geothermal brine. The objective was to obtain a selective, reusable, and mechanically stable adsorbent suitable for industrial-scale applications. The synthesized PVC/PAN-HTO composite was characterized by FT-IR, BET, XRD, and SEM techniques. Batch adsorption studies showed that the optimum lithium recovery occurred at pH 12, with efficiencies of 98.7% in model lithium solutions and 91.6% in geothermal water using a 4 g L-1 adsorbent dosage. Adsorption kinetics followed a pseudo-second-order model, and the Langmuir isotherm provided the best fit, indicating monolayer adsorption with a maximum capacity of 5.79 mg g-1. Thermodynamic analysis confirmed that the adsorption process is spontaneous and exothermic. Reusability tests demonstrated stable performance over three adsorption-desorption cycles, confirming the potential of PVC/PAN-HTO for practical lithium extraction applications.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.Article Citation - WoS: 16Citation - Scopus: 17Integrating Experimental and Machine Learning Approaches for Predictive Analysis of Photocatalytic Hydrogen Evolution Using Cu/G-c3n4(Pergamon-elsevier Science Ltd, 2024) Arabaci, Bahriyenur; Bakir, Rezan; Orak, Ceren; Yuksel, AsliThis study addresses environmental issues like global warming and wastewater generation by exploring waste-toenergy strategies that produce renewable hydrogen and treat wastewater simultaneously. Cu/g-C3N4 is used to evolve hydrogen from sucrose solution and the impact of reaction parameters such as pH (3, 5, and 7), Cu loading (5, 10, and 15 wt%), catalyst amount (0.1, 0.2, and 0.3 g/L), and oxidant (H2O2) concentration (0, 10, and 20 mM) on the evolved hydrogen amount is examined. Characterization study confirmed successful incorporation of Cu without significantly altering g-C3N4 properties. The highest hydrogen production (1979.25 mu mol g- 1 & sdot;h- 1) is achieved with 0.3 g/L catalyst, 20 mM H2O2, 5 % Cu loading, and pH 3. The experimental study concludes that Cu/g-C3N4 is an effective photocatalyst for renewable hydrogen production. In addition to the experimental investigations, various machine learning (ML) models, including Random Forest, Decision Tree, XGBoost, among others, are employed to analyze the impact of reaction parameters and forecast the quantities of produced hydrogen. Alongside these individual models, an ensemble approach is proposed and utilized. The R2 values of these ML models ranged from 0.9454 to 0.9955, indicating strong predictive performance across the board. Additionally, these models exhibited low error rates, further confirming their reliability in predicting hydrogen evolution.Article Citation - WoS: 15Citation - Scopus: 16A Machine Learning Ensemble Approach for Predicting Solar-Sensitive Hybrid Photocatalysts on Hydrogen Evolution(IOP Publishing, 2024) Bakır, Rezan; Orak, Ceren; Yuksel, AsliHydrogen, as the lightest and most abundant element in the universe, has emerged as a pivotal player in the quest for sustainable energy solutions. Its remarkable properties, such as high energy density and zero emissions upon combustion, make it a promising candidate for addressing the pressing challenges of climate change and transitioning towards a clean and renewable energy future. In an effort to improve efficiency and reduce experimental costs, we adopted machine learning techniques in this study. Our focus turned to predictive analyses of hydrogen evolution values using three photocatalysts, namely, graphene-supported LaFeO3 (GLFO), graphene-supported LaRuO3 (GLRO), and graphene-supported BiFeO3 (GBFO), examining their correlation with varying levels of pH, catalyst amount, and H2O2 concentration. To achieve this, a diverse range of machine learning models are used, including Random Forest (RF), Decision Tree (DT), Support Vector Machine (SVM), XGBoost, Gradient Boosting, and AdaBoost-each bringing its strengths to the predictive modeling arena. An important step involved combining the most effective models-Random Forests, Gradient Boosting, and XGBoost-into an ensemble model. This collaborative approach aimed to leverage their collective strengths and improve overall predictability. The ensemble model emerged as a powerful tool for understanding photocatalytic hydrogen evolution. Standard metrics were employed to assess the performance of our ensemble prediction model, encompassing R squared, Root Mean Squared Error (RMSE), Mean Squared Error (MSE), and Mean Absolute Error (MAE). The yielded results showcase exceptional accuracy, with R squared values of 96.9%, 99.3%, and 98% for GLFO, GBFO, and GLRO, respectively. Moreover, our model demonstrates minimal error rates across all metrics, underscoring its robust predictive capabilities and highlighting its efficacy in accurately forecasting the intricate relationships between GLFO, GBFO, and GLRO values and their influencing factors.Article Citation - WoS: 4Citation - Scopus: 5Breakthrough Curve Analysis of Phosphorylated Hazelnut Shell Waste in Column Operation for Continuous Harvesting of Lithium From Water(Elsevier, 2024) Recepoğlu, Yaşar Kemal; Arar, Ozguer; Yuksel, AsliIn batch-scale operations, biosorption employing phosphorylated hazelnut shell waste (FHS) revealed excellent lithium removal and recovery efficiency. Scaling up and implementing packed bed column systems necessitates further design and performance optimization. Lithium biosorption via FHS was investigated utilizing a continuous-flow packed-bed column operated under various flow rates and bed heights to remove Li to ultra-low levels and recover it. The Li biosorption capacity of the FHS column was unaffected by the bed height, however, when the flow rate was increased, the capacity of the FHS column decreased. The breakthrough time, exhaustion time, and uptake capacity of the column bed increased with increasing column bed height, whereas they decreased with increasing influent flow rate. At flow rates of 0.25, 0.5, and 1.0 mL/min, bed volumes (BVs, mL solution/mL biosorbent) at the breakthrough point were found to be 477, 369, and 347, respectively, with the required BVs for total saturation point of 941, 911, and 829, while the total capacity was calculated as 22.29, 20.07, and 17.69 mg Li/g sorbent. In the 1.0, 1.5, and 2.0 cm height columns filled with FHS, the breakthrough times were 282, 366, and 433 min, respectively, whereas the periods required for saturation were 781, 897, and 1033 min. The three conventional breakthrough models of the Thomas, Yoon-Nelson, and Modified Dose-Response (MDR) were used to properly estimate the whole breakthrough behavior of the FHS column and the characteristic model parameters. Li's extremely favorable separation utilizing FHS was evidenced by the steep S-shape of the breakthrough curves for both parameters flow rate and bed height. The reusability of FHS was demonstrated by operating the packed bed column in multi-cycle mode, with no appreciable loss in column performance.