WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

Permanent URI for this collectionhttps://hdl.handle.net/11147/7150

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Now showing 1 - 4 of 4
  • Article
    Citation - WoS: 2
    Citation - Scopus: 1
    A Novel Framework for Droplet/Particle Size Distribution in Suspension Polymerization Using Physics-Informed Neural Network (PINN)
    (Elsevier Science Sa, 2025) Turan, Meltem; Dutta, Abhishek
    A Machine Learning (ML) based neural network can capture the complex evolution of polymer chain distributions, accounting for factors such as initiation, propagation, and termination steps in a suspension polymerization process, by integrating stagewise molar balance model (MBM) and population balance model (PBM) with Physics-Informed Neural Network (PINN). The integrated PINN framework is proposed to efficiently solve these equations, incorporating known physical laws as constraints and minimizing errors in both the distribution and dynamics of the polymer chains. By optimizing the neural network parameters such as weight matrices and bias vector, the model reproduces the moments of the polymer molecular weight distribution in close alignment with numerical solutions, and it generates population balance solutions that exhibit excellent agreement with their analytical counterparts. Sensitivity analyses for the depth of the neural network architecture to quantify how structural choices affect model fidelity has been performed. The resulting MBM-PINN and PBM-PINN integrated framework demonstrates robustness and versatility in accurately capturing (96-97%) droplet/particle dynamics. The proposed methodology has the capability to provide a powerful tool for faster and scalable simulations of polymerization reactions, enabling better prediction of product properties which could be used for optimizing reaction conditions in industrial applications.
  • 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.
  • Conference Object
    Citation - WoS: 1
    Konteyner Görüntülerini Kullanarak Hasar Tespiti ve Sınıflandırması
    (IEEE, 2020) Imamoglu, Zeynep Ekici; Tuglular, Tugkan; Bastanlar, Yalin
    In the logistics sector, digital transformation is of great importance in terms of competition. In the present case, container warehouse entry / exit operations are carried out manually by the logistics personnel including container damage detection. During container warehouse entry / exit process, the process of detecting damaged containers is carried out by the personnel and several minutes are required to upload to the IT system. The aim of our work is to automate the detection of damaged containers. This way, the mistakes made by the personnel will be eliminated and the process will be accelerated. In this work, we propose to use a convolutional neural network (CNN) that takes the container images and classify them as damaged or undamaged. We modeled the problem as a binary classification and employed different CNN models. The result we obtained shows that there is no single best method for the classification. It is shown how the dataset was created and how the parameters used in the layered structures affect the models employed in this study.
  • Conference Object
    Citation - WoS: 2
    Stream Text Data Analysis on Twitter Using Apache Spark Streaming
    (Institute of Electrical and Electronics Engineers Inc., 2018) Hakdağlı, Özlem; Özcan, Caner; Oğul, İskender Ülgen
    With today's developing technology, people's access to information and its production have reached a very fast level. These generated and obtained information are instantly created, entered into data systems and updated. Sources of streaming data can be transformed into valuable analysis results when they are handled with targeted methods. In this study, a text data field is determined to perform analysis on instantaneous generated data and Twitter, the richest platform for instant text data, is used. Twitter instantly generates a variety of data in large quantities and it presents it as open source using an API. A machine learning framework Apache Spark's stream analysis environment is used to analyze these resources. Situation analysis was performed using Support Vector Machine, Decision Trees and Logistic Regression algorithms presented under this environment. The results are presented in tables.