Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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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: 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 LLCBook Part Citation - Scopus: 1Biomass-Based Polygeneration Systems With Hydrogen Production: a Concise Review and Case Study(Springer Science and Business Media Deutschland GmbH, 2024) Hajimohammadi Tabriz,Z.; Mohammadpourfard,M.; Gökçen Akkurt,G.; Çağlar,B.This chapter discusses the importance of biomass-based polygeneration systems in producing hydrogen as a clean and safe energy carrier. The benefits of polygeneration systems, which can produce multiple products and minimize waste, are highlighted, and the need for clean and efficient hydrogen production is emphasized. This study gives a brief overview of hydrogen production from biomass-based polygeneration systems, which examines the systems in two main classifications: systems that use biomass as a potential and rich source of hydrogen and systems that exploit the energy content of biomass to run hydrogen production units. Furthermore, a new multigeneration system with hydrogen production has been introduced and thermodynamically evaluated. Also, its results have been obtained in a real situation. Overall, this chapter offers insights into the potential of biomass-based polygeneration systems in meeting energy demands while reducing environmental impact. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.Article 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 Engineers