Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
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Article Citation - Scopus: 3Soft Computing Implementations for Evaluating Los Angeles Abrasion Value of Rock Aggregates From Kütahya, Turkey(Szechenyi Istvan University, 2024-02-28) Köken, E.The Los Angeles abrasion value (LAAV) of rocks is a critical mechanical aggregate property for designing road infrastructures and concrete quality. However, the determination of this critical aggregate property is labour-intensive and time-consuming and thus, in the literature, there are many predictive models to estimate the LAAV for different rock types. However, most of them are based on classical regression analyses, limiting their broader usage. In this study, several soft computing analyses are performed to develop robust predictive models for the evaluation of LAAV of rocks in the Ilıca region (Kütahya – Turkey). The main motivation for implementing soft computing analyses is that precise predictive models might be useful when exploring suitable rock types that are manufactured in crushing–screening plants. For this purpose, a comprehensive laboratory schedule was established to obtain some inputs for the evaluation of LAAV. As a result of the soft computing analyses, four robust predictive models are developed based on artificial neural networks (ANN), multiple adaptive regression spline (MARS), adaptive neuro-fuzzy inference system (ANFIS) and gene expression programming (GEP) methodologies. The performance of the proposed models is investigated by some statistical indicators such as R2 and RMSE values and scatter plots. As a result, the ANFIS-based predictive model turns out to be the best alternative to estimate the LAAV of the investigated rocks. © 2025 Elsevier B.V., All rights reserved.Conference Object Modelling of Rock Comminution Using Statistical and Soft Computing Analyses – A Case Study on a Laboratory-Scale Jaw Crusher(Baski, 2022) Köken, E.The present study encompasses a quantitative investigation on rock comminution using statistical and soft computing analyses. For this purpose, physical and mechanical rock aggregate properties were determined for nine different rock types (R1-R9) in Turkey. Then, crushability tests were performed to determine the size reduction ratio (SRR) using a laboratory-scale jaw crusher. Based on statistical and soft computing analyses, five different predictive models (M1 to M5) were established to estimate the SRR in this study. Consequently, the SRR values are associated with water absorption by weight (w<inf>a</inf>), dry unit weight (γ<inf>d</inf>), and aggregate impact value (AIV) of the investigated rocks. However, the individual use of these independent variables results in undulating SRR estimations. Therefore, among the established predictive models, the empirical formulation based on artificial neural networks (ANN) (M5) was found to be the most reliable model with a correlation of determination value (R2) of 0.88. However, the predictive models stated in this study should be implemented to several portable jaw crushers to observe the similarities or difficulties in quantifying SRR as a function of rock properties in future studies. © 2022 Elsevier B.V., All rights reserved.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.Conference Object Citation - Scopus: 2Comparison of Secondary Crushing Operations Through Cone and Horizontal Shaft Impact Crushers(International Multidisciplinary Scientific Geoconference, 2020-09-20) Köken, E.; Qu, JiliIndustrial size reduction processes such as crushing and grinding play vital roles in rock quarrying. The present study states real field data concerning secondary crushing operations through the cone and horizontal shaft impact (HSI) crushers. In this regard, a total of 44 case studies were collected from several rock quarries located in various parts of the world. Based on the field data, crushing performances of the cone and HSI crushers were compared by statistical analyses. The statistical analyses demonstrated that the specific energy consumption of HSI type crushers is relatively higher than those of cone crushers when comparing their production capacities. However, the difference in the specific energy consumption decreases with increasing the Los Angeles abrasion loss (LAA) of rocks. Specifically, the difference closes remarkably up when the LAA approaches 40%. It was also achieved that there is no remarkable superiority over the crushers with a specific energy consumption lower than 0.75 kWh/ton. Furthermore, the maximum feed size for cone and HSI crushers could be estimated at 12% of the mainframe opening and 39% of the rotor diameter, respectively. The ratios statistically found could be declared a start-up. These ratios are, therefore, beneficial for initial sizing related to secondary crushing operations. It was also claimed that for higher achievements in the production capacity (e.g.,> 8000 ton/day), cone crushers could be more feasible for handling rocks whose LAA is lower than 30%. Otherwise, the selection of secondary crushing equipment is associated with rock lithology, its reserve, economic constraints, targets, and marketplaces of rock aggregate manufacturers. © 2021 Elsevier B.V., All rights reserved.