Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

Permanent URI for this collectionhttps://hdl.handle.net/11147/7148

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Subject: search.filters.subject.Artificial Neural NetworksWoS Q: search.filters.wosQuartile.Q1

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  • Article
    Citation - WoS: 1
    Citation - Scopus: 1
    Design 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, Mojtaba
    This 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: 3
    Citation - Scopus: 3
    Data Driven Modeling and Design of Cellulose Acetate-Polysulfone Blend Ultrafiltration Membranes Based on Artificial Neural Networks
    (Elsevier Ltd, 2025) Gungormus, E.
    This study aimed to develop and validate an Artificial Neural Networks (ANNs) model for the design and optimization of cellulose acetate-polysulfone blend ultrafiltration membranes, produced via the Non-Solvent Induced Phase Separation method. After some data science applications on a comprehensive dataset obtained from literature studies, the ultimate ANNs model exhibited superior predictive capabilities and effectively captured complex nonlinear relationships in the data. The optimum model configuration with a single hidden layer containing six neurons provided reliable predictions by avoiding overfitting and underfitting risks and significantly reducing error metrics. The model analyzed the effects of input variables on outputs, revealing that different stages of the membrane preparation process had varying impacts on performance metrics. This finding emphasized the importance of systematically optimizing the preparation process to enhance overall membrane performance. The model's predictions showed strong agreement with experimental data, further validating its accuracy. The optimum production conditions identified by the model offered significant improvements in membrane performance. Moreover, the model accelerated the membrane development process by reducing the required number of experimental trials and promoting efficient resource utilization. This approach contributed to both economic and environmental sustainability by reducing production costs and energy consumption. This study highlighted the significant potential of machine learning techniques for future innovations and advancements in this field by enabling precise, efficient, and sustainable membrane design and synthesis. © 2025 Elsevier Ltd.