WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7150
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Article Citation - WoS: 2Citation - Scopus: 2Incorporation of CuWO4 With Hollow Tubular g-C3N4: Harnessing the Potential in Photocatalytic Degradation, Hydrogen Production, and Supercapacitor Applications(Pergamon-Elsevier Science Ltd, 2026) Erdem, Nurseli Gorener; Caglar, Basar; Inan, Ece; Tuna, Ozlem; Ertis, Irem Firtina; Simsek, Esra BilginDriven by the urgent need for sustainable energy conversion and environmental remediation technologies, the development of multifunctional materials has gained growing interest. Herein, a bifunctional heterostructure was fabricated by depositing copper tungstate (CuWO4) spherical particles over hollow tubular graphitic carbon nitride (HTCN) using an ultrasonic-assisted thermal impregnation method. The photocatalytic activities were evaluated through tetracycline degradation and hydrogen evolution tests, while electrochemical measurements were conducted to assess the supercapacitor performance. CuWO4@HTCN composite achieved up to 83% degradation efficiency, a hydrogen evolution rate of 2538 mu mol g1 h-1, and a specific capacitance of 212 F g1, demonstrating its strong potential as a multifunctional material for solar-driven environmental and energy storage applications. The enhanced photocatalytic performance was attributed to extended visible light absorption ability, efficient charge separation, and suppressed electron-hole recombination resulting from the formation of a Z-scheme heterojunction. Furthermore, the superior capacitance behavior was ascribed to enhanced electrical conductivity and ion transport, enabled by the porous, nitrogen-rich HTCN structure. The increased HTCN content in the composite improved pore accessibility and active site availability while an excessive amount of CuWO4 reduced electrochemical performance. These results highlight the multifunctional applicability of CuWO4@HTCN composite in photocatalytic hydrogen production and supercapacitor systems, emphasizing their relevance for renewable energy technologies.Article Citation - WoS: 1Citation - Scopus: 1Design and Comprehensive Analysis of a Solar-Biomass Hybrid System With Hydrogen Production and Storage: Towards Self-Sufficient Wastewater Treatment Plants(Pergamon-Elsevier Science Ltd, 2025) Tabriz, Zahra Hajimohammadi; Kasaeian, Alibakhsh; Mohammadpourfard, Mousa; Shariaty-Niassar, MojtabaThis paper comprehensively investigates a novel solar-biomass hybrid system designed to produce power, heating, hydrogen, methane, and digestate. The system's design is grounded in regional weather patterns and site-specific resource availability. A comprehensive thermodynamic and exergoeconomic analysis, based on the first and second laws of thermodynamics, is performed alongside parametric studies to evaluate the influence of key parameters on system performance. Multi-objective optimization employs a genetic algorithm facilitated by an artificial neural network to reduce computational time and balance exergy efficiency and total cost. The Pareto front is generated, and the TOPSIS method is employed to identify the optimal trade-off point. The system integrates an auxiliary boiler powered by stored hydrogen and methane to maintain consistent operation during periods of low solar irradiance. Key findings include a base-case overall energy efficiency of 78.67 % and exergy efficiency of 60.41 %. The base-case unit cost of hydrogen is determined to be $3.174/kg, demonstrating competitive viability. Integrating the biomass subsystem with the solar plant resulted in a 40 % increase in exergy efficiency and a 35 % improvement in the total unit cost of products compared to a stand-alone solar system. Optimized system parameters yielded an exergy efficiency of 55.52 % and a total cost rate of 14.98 M $/year. These results confirm the potential of this hybrid system as a promising sustainable solution for developing self-sufficient wastewater treatment plants.Article Citation - WoS: 3Citation - Scopus: 4Towards Renewable Energy Islands in Türkiye: Potential and Challenges(Pergamon-elsevier Science Ltd, 2025) Karipoglu, Fatih; Denizli, OsmancanThe necessity of renewables is increasing day by day due to increasing energy demand. Therefore, novel approaches and methods for producing electricity in an environmentally friendly manner are valuable and critical. The seas have enormous potential in terms of wind, waves, solar, and hydrogen systems. The study presents the investigation of the potential dynamics for energy island formation on T & uuml;rkiye borders. Also, targets, legislation, and environmental and social concerns are discussed comprehensively. Results show that offshore wind and hydrogen are promising systems shortly while solar and wave energy needs more research for T & uuml;rkiye. The Marmara and Aegean Seas are considered technically feasible for offshore wind installations, while the Mediterranean and Aegean Seas have the highest technical solar potential. In addition, the highest wave power is detected along the line from I(center dot)zmir to Antalya Coast while hydrogen energy systems receive great interest with academic and industrial projects in the Marmara Coastline. Profiting from marine energy, marine spatial planning, and grid availability are detected as the shortcoming issues in T & uuml;rkiye. The study could give critical information to energy planners, and decision makers for potential projects.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: 52Citation - Scopus: 57Optimizing Hydrogen Evolution Prediction: a Unified Approach Using Random Forests, Lightgbm, and Bagging Regressor Ensemble Model(Elsevier Ltd, 2024) Bakır,R.; Orak,C.; Yüksel,A.Hydrogen, as a clean and versatile energy carrier, plays a pivotal role in addressing global energy challenges and transitioning towards sustainable energy systems. This study explores the convergence of machine learning (ML) for photocatalytic hydrogen evolution from sucrose solution using perovskite-type catalysts, namely LaFeO3 (LFO) and graphene-supported LaFeO3 (GLFO). This study pioneers the practical application of ML techniques, including Random Forests, LightGBM, and Bagging Regressor, to predict hydrogen yields in the presence of these photocatalysts. LFO and GLFO underwent a thorough characterization study to validate their successful preparation. Noteworthy, the highest hydrogen yield from the sucrose model solution was achieved using GLFO as 3.52 mmol/gcat. The optimum reaction conditions were experimentally found to be pH = 5.25, 0.15 g/L of catalyst amount, and 7.5 mM of HPC (hydrogen peroxide concentration). A pivotal contribution of this research lies in the practical application of ML models, culminating in the development of an ensemble model. This collaborative approach not only achieved an overall R2 of 0.92 but also demonstrated exceptional precision, as reflected in remarkably low error metrics. The mean squared logarithmic error (MSLE) was 0.0032, and the mean absolute error (MAE) was 0.049, underscoring the effectiveness of integrating diverse ML algorithms. This study advances both the understanding of photocatalytic hydrogen evolution and the practical implementation of ML in predicting intricate chemical reactions. © 2024 Hydrogen Energy Publications LLCArticle Citation - WoS: 12Citation - Scopus: 12Enhancing a Bio-Waste Driven Polygeneration System Through Artificial Neural Networks and Multi-Objective Genetic Algorithm: Assessment and Optimization(Elsevier Ltd, 2024) Hajimohammadi Tabriz,Z.; Taheri,M.H.; Khani,L.; Çağlar,B.; Mohammadpourfard,M.This paper aims to study the feasibility of municipal sewage sludge utilization as an energy source in a polygeneration system. This system offers distinctive benefits such as contribution to the principled removal of sewage sludge, simultaneous utilization of raw and digested sludge in different parts of the system, and production of renewable hydrogen from bio-waste. 4E (energy, exergy, exergoeconomic, and environmental) analyses, are performed to understand the system performance comprehensively. Then, parametric studies are examined the impact of changing the values of main parameters on the system operation. Afterward, a multi-objective optimization based on a genetic algorithm is carried out to achieve optimal values, considering a trade-off between the exergy efficiency and the total cost rate. Meanwhile, this work harnesses the potential of artificial neural networks to expedite complex and time-consuming optimization processes. According to the results, the gasifier exhibits the highest rate of exergy destruction, and the primary cost of consumption is attributed to its heat supply. The multi-objective optimization findings show that the optimum point has an exergy efficiency of 38.26 % and a total cost rate of 58.17 M$/year. The hydrogen production rate, energy efficiency, and net power generation rate for the optimal case are determined as 1692 kg/h, 35.24 %, and 4269 kW, respectively. Also, the unit cost of hydrogen in the optimal case is obtained 1.49 $/kg which offers a cost-effective solution for hydrogen production. © 2024 Hydrogen Energy Publications LLCArticle Citation - WoS: 4Citation - Scopus: 5Experimental Investigation of a Unique Electro-Biomembrane Based Integrated System for Wastewater Treatment and Simultaneous Clean Water, Hydrogen and Energy Production(Institution of Chemical Engineers, 2024) Goren,A.Y.; Dincer,I.; Khalvati,A.This paper concerns the design, development, and building of a unique electro-bio-membrane reactor for concurrent bioH2 production, desalination, and energy production by microorganisms in a single reactor. The effects of varying biomass amounts (5–50 g) and inoculum amounts (250–1500 mL) on the bioH2 production efficiency are also investigated. The lowest cumulative bioH2 yield of 24.2 mL/g is obtained using a biomass amount of 5 g, while it is 44.7 mL/g at a biomass amount of 50 g. The highest H2 production from water electrolysis is also found as 0.719 mL/min at improved conditions. Furthermore, the highest power and current density values are 2794.5 mW/m2 and 2786.1 mA/m2 at 1500 mL-inoculum, biomass amount of 30 g, initial pH of 5.5, and temperature of 37 °C in the dark fermentation (DF) cell. Moreover, the desalination efficiency increases from 41.6 to 65.8% with decreasing inoculum amounts from 1500 to 250 mL. © 2024 The Institution of Chemical EngineersArticle Citation - WoS: 14Citation - Scopus: 17Cleaner Production of Biohydrogen Using Poplar Leaves: Experimental and Optimization Studies(Elsevier Sci Ltd, 2024) Goren, A. Yagmur; Kenez, Muratcan; Dincer, Ibrahim; Khalvati, AliBiohydrogen (bioH2) is recognized as a potential carbon-neutral energy vector, and developing novel methods has received increasing attention with a prime goal of producing H2 more efficient and cost effective manner. This study aimed to develop a unique reactor to investigate dark fermentative H2 production from poplar biomass using commercially available and inexpensive microorganism cultures. Therefore, six factors of the Box-Behnken design (BBD) were performed to evaluate the individual and combined effects of operational param-eters: acid concentration (2-10%), biomass concentration (2-10 g), initial pH (5-8), temperature (30-40 degrees C), mixing ratio (150-350 rpm), and microorganism concentration (2-6 g) on bioH2 production. Among the oper-ational parameters, the acid concentration was the most effective parameter on bioH2 production. The bioH2 production increased from 11.33 to 18.15 mg/g biomass with increasing acid concentration from 6 to 10%. Moreover, the optimum levels of operational variables were as follows: acid concentration of 9.9%, biomass amount of 2 g, pH of 6.56, temperature of 35 degrees C, mixing ratio of 345 rpm, and microorganism amount of 4.5 g for the highest bioH2 production of 20 mg/g-biomass according to the experimental design. Consequently, the bioH2 production performance of the dark fermentation process showed that bioH2 production from poplar biomass using commercially available microorganisms had a competitive advantage.Article Citation - WoS: 49Citation - Scopus: 51Energy, Exergy, Exergoeconomic, and Exergoenvironmental (4e) Analysis of a New Bio-Waste Driven Multigeneration System for Power, Heating, Hydrogen, and Freshwater Production: Modeling and a Case Study in Izmir(Elsevier, 2023) Tabriz, Zahra Hajimohammadi; Mohammadpourfard, Mousa; Gökçen Akkurt, Gülden; Heris, Saeed ZeinaliToday, the world is facing numerous challenges such as the increasing demand for energy, fossil fuels reduction, the growth of atmospheric pollutants, and the water crisis. In the present research, a new multigeneration system based on urban sewage bio-waste has been designed and evaluated for power, hydrogen, freshwater, and heating production. This system, which consists of biomass conversion subsystem, hydrogen production unit, Brayton cycle, atmospheric water harvesting unit, steam Rankine cycle, and organic Rankine cycles, has been evaluated from a thermodynamic point of view, and the energy, exergy, exergoeconomic, and exergoenvironmental analyses have been carried out on it. In the current study, the atmospheric water harvesting unit, as an attractive and environmentally friendly technology, is integrated with this Biomass-based multigeneration. A case study has been conducted on this system using the information collected from cigli wastewater treatment plant located In Izmir province, Turkey, and the results indicate that such a system, in addition to receiving sewage sludge from the treatment plant unit as a polluting waste, can produce added value products. The modeling results show that in the base conditions and with a feed rate of 7.52 kg/s, the total power generated by this system is 17750 kW, the hydrogen production rate is 3180 kg/h, the freshwater production rate is more than 18 l/h, and the energy and exergy efficiencies are 35.48% and 40.18%, respectively. According to the exergoeconomic and exergoenvironmental evaluations, the unit cost of total products and the unit emission of carbon dioxide are calculated as 13.05 $/GJ and 0.2327 t/MWh, respectively. Also, the results of parametric studies show that increasing the rate of Biomass improves the overall energy efficiency and production rates and also reduces the unit emission of carbon dioxide, but on the other hand, it causes a decrease in exergy efficiency and an increase in the unit cost of total products.