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    Öğe
    Evaluation of the Effect of Using Different Types of Clinker Grinding Aids on Grinding Performance by Numerical Analysis
    (Mdpi, 2025) Kaya, Yahya; Kobya, Veysel; Eser, Murat; Mardani, Naz; Bilgin, Metin; Mardani, Ali
    To develop more environmentally friendly and sustainable cementitious systems, the use of grinding aids (GAs) during the clinker grinding process has increasingly gained attention. Although the mechanisms of the action of grinding aids (GAs) are known, the selection of an effective grinding aid (GA) can be difficult due to the complexity of appropriate selection criteria. For this reason, it is important to model the effect of GA properties on grinding performance. In this study, seven different types of GAs were used in four different dosages, and time-dependent grinding was performed. The Blaine fineness values of cements were compared after each grinding process. In addition, the modeling of these parameters using machine learning and ensemble learning methods was discussed. The Synthetic Minority Over-sampling Technique (Smote) was used to generate artificial data and increase the number of data for the grinding efficiency experiment. The data were modeled using methods such as Artificial Neural Networks (ANNs), Attentive Interpretable Tabular Learning (TabNet), Random Forests (RFs), and the XGBoost Regressor (XGBoost), and the ranking of the parameters affecting the Blaine properties was determined using the XGBoost method. The XGBoost method achieved the best results in the MAE, RMSE, and LogCosh metrics with values of 21.0384, 33.7379, and 15.4846, respectively, in the experimental modeling studies with augmented data. This study contributes to a better understanding of the relationship between GA selection and milling process performance.
  • Küçük Resim
    Öğe
    Research on Behavior of Two New Random Entity Mobility Models in 3-D Space
    (Springer Science and Business Media Deutschland GmbH, 2022) Bilgin, Metin; Eser, Murat
    Mobile data collectors that can be used without a central control mechanism currently have common use in many fields. Because they do not need a central unit, each node in a network can move independently. The field literature offers various group- or entity-based models to define the functioning of mobile data collectors. In this study, a random entity mobility model (REMM) research was performed. The study was based on the models random walk (RW) and random waypoint (RWP), used in several former studies mentioned in the literature. Furthermore, the models random point (RP) and random journey (RJ) proposed by Bilgin [1] for two-dimensional (2D) space were transferred to three-dimensional (3D–cubic) to be used in the study. Study findings obtained by defining a various number of fixed nodes in areas of various sizes were analyzed using 4 different metrics. It was observed that 4 different metric values decreased for 4 REMMs when the cubic area was enlarged by increasing the edge lengths (150–200–250 pixel) of the cubic. When the cubic’s edge length is 150-200-250 pixel, respectively, connected node ratio (CNR) metric value is 98.04%–95.8%–91.34% for RP and 96.83%-83.23%-70% for RJ. Provided that the cubic area remains constant, the increases in the number of nodes generally tend to increase, although there are slight fluctuations on the results. When the cubic edge is 200 and the node numbers are 4–6–8–10, the message delay is 13.345–16.566–27.386–40.050 seconds for RW and 6.579–9.124–11.431–13.456 seconds for RWP. In the comparisons made by taking the average of the values obtained according to the size of the cubic area and the number of nodes, the RP model reached the highest values for all metrics. For example, the visited node ratio (VNR) metric average for the cubic edge 200 pixels is 98.76% for RP and 94.68%–87.38%–94.78% for RW–RWP–RJ. The VNR metric for the cubic edge 250 is 96.55%–93.7%–87.45%–51.27% for the RP–RW–RWP–RJ. Similarly, the average values obtained for other metrics prove this situation. In addition, when the results of the study are examined, it has been measured that the RP model can deliver the message to the base with less delay than other models. The average delay for the cubic edge 150 is 2.933–27.667–23.236–5.698 second for the RP–RW–RWP–RJ and 2.846–24.337–10.148-4.293 second when the edge is 200. When the average results obtained were examined, the success ranking in the delay metric was RP–RJ–RWP and RW, while the other metrics were formed as RP–RJ–RW–RWP. Considering all the obtained results, it was seen that the proposed two models achieved better results than the existing models in 3D after 2D.