Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
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Article Citation - WoS: 26Citation - Scopus: 25Prediction of Mechanical Properties of Coal From Non-Destructive Properties: A Comparative Application of MARS, ANN, and GA(Springer, 2021-10-06) Lawal, Abiodun Ismail; Oniyide, Gafar O.; Kwon, Sangki; Onifade, Moshood; Koken, Ekin; Ogunsola, Nafiu O.Rock properties are useful for safe operation and design of both surface and underground mines including civil engineering projects. However, the cost and time required to perform detailed assessments of rock properties are high. In addition, rock properties are required in numerical modeling. Different models have been proposed for quick and easy assessments of rock properties but majority of these models are regression-based, which are incapable of capturing inherent variabilities in rock properties. Therefore, this study proposed three different soft computing models (i.e., double input-single output ANN, multivariate adaptive regression spline, genetic algorithm) for accurate prediction of several mechanical properties of coal and coal-like rocks. The performances of the proposed models were statistically evaluated using various indices and they were found to predict rock properties suitably with very strong statistical indices. The proposed models were validated further using external datasets aside from those used in the model development to test the generalization potential of the models. The Pearson's correlation coefficients for the validation were close to 1, indicating that the proposed models can be used to assess geo-mechanical properties of coal, shale, and coal-bearing rocks.Article Citation - Scopus: 3Estimation of Deformation Modulus of Coals Using Artificial Neural Networks (ANN)(Szechenyi Istvan University, 2022-05-29) Köken, E.In this study, the Young modulus (E) of different coals was investigated using artificial neural networks (ANN). For this purpose, a comprehensive literature survey was carried out to compile such datasets available for the ANN analyses. As a result of the literature survey, a database composed of 81 datasets was formed. In the ANN analyses, uniaxial compressive strength (UCS) and dry density (ρ<inf>d</inf>) of coals were adopted as input parameters. The ANN analysis results demonstrated that the predictive model established in this study could be reliably used to estimate the E for different coals. The correlation of determination value (R2) for the developed model is 0.85, which shows its relative success. In this context, this study can be declared a case study showing the applicability of ANN for the evaluation of E for a wide range of coal types. However, the number of samples and independent variables should be increased to obtain more comprehensive models in future studies. © 2025 Elsevier B.V., All rights reserved.Article Gazlaştırma Tesisi Odun Atığı ve Sivas Kangal Linyit Kömürünün Boya Adsorpsiyonunda Kullanım Olanaklarının Araştırılması(Chamber of Mining Engineers of Turkey, 2020-12-01) Kırma, Ramazan; Sarikaya, Musa; Top, S.; Uçkun, Şükrü; Timür, İrfanIn this study, the usage possibilities of wood waste obtained from Gebze MDF and Particle Board Gasification Plant preliminary studies and Sivas Kangal lignite coal as absorbents were investigated. In this way, it was aimed both to evaluate the wastes and to prevent environmental pollution with materials that are cheaper and easier to obtain. The structure and surface properties of wood waste and coal samples crushed and ground to -75 µm size and used as adsorbent were investigated by XRD, SEM and BET analyses. In addition, samples have been characterized by elemental, ash, moisture, volatile matter and fixed carbon analyses. In the experiments, methylene blue (MM) with the formulation of C<inf>16</inf>H<inf>18</inf>CIN<inf>3</inf>S.xH<inf>2</inf>O was used. The effects of temperature, mixing time and concentration parameters on MM adsorption were investigated. Langmuir isotherms were created for different temperatures at optimum concentrations. As a result, it has been revealed that lignite coal and wood waste can be used as adsorbent. A 10 ppm MM for lignite coal and 3 ppm MM for wood waste were determined to be ideal concentrations for adsorption. © 2022 Elsevier B.V., All rights reserved.