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: 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 LLC