WoS İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/394
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Article Citation - WoS: 5Citation - Scopus: 6Size Reduction Characterization of Underground Mine Tailings: A Case Study on Sandstones(Springer, 2020-06-09) Koken, EkinThe production of construction and building materials starts with reducing the size of natural, industrial, and waste materials. In addition to strength and durability considerations of natural resources recommended by various institutions, size reduction characterization, specific to rock aggregates, has a vital role in their size-related quality. In this study, various sandstones extracted from underground mines located in northwestern Turkey were investigated for size reduction characterizations. Several mineralogical, textural, and physico-mechanical properties were determined for each rock type. Crushability tests were carried out using a laboratory-scale cone crusher for different feeding size fractions, namely + 11.20 - 16.00 mm (size I), + 9.52 - 16.00 mm (size II), and + 6.30 - 16.00 mm (size III). Based on the crushability tests, crushed particles were analyzed, focusing on production yield, size, and shape properties. Each crushability test was also explored for energy consumption arising from varying rock properties of the sandstones. The laboratory test results demonstrated that the degree of rock crushability (DRC) and specific energy consumption (E-cs, kJ/kg) were associated with the Brazilian tensile strength (BTS, MPa) and apparent porosity (n(e), %) of the sandstones. The results also showed that the degree of sorting in mineral constituents, quantified as the sorting coefficient (S-c), affected the DRC. However, mineralogical features of the sandstones have no significant impact on DRC andE(cs). Variations in feeding gradation, irrespective of whether mineralogical, textural, or physico-mechanical properties, have remarkable effects on product flakiness and yields for specific size fractions. In light of the findings obtained, the present study provides knowledge on how the sandstones behave under cone crushing operations.Article Citation - WoS: 5Citation - Scopus: 5Investigations on Fracture Evolution of Coal Measure Sandstones from Mineralogical and Textural Points of View(Springer India, 2020-04-09) Koken, EkinThe purpose of the present study is to investigate the variations in fracture evolution of sandstones arising from mineralogical and textural features. For this purpose, eight types of coal measure sandstones located in the Zonguldak Hardcoal Basin (ZHB) were considered. The mineralogical and textural characterizations of the rocks were carried out. Physico-mechanical properties were determined for each rock type. Based on quantitative strain-based methods, the crack initiation (sigma(ci)) and crack damage (sigma(cd)) thresholds of the sandstones were determined. The laboratory test results indicate that the sigma(ci)and sigma(cd)of the sandstones were found to be between 0.27-0.43 and 0.61-0.83 of the UCS, respectively. In general, the sigma(ci)and sigma(cd)correspond to 0.37 and 0.71 of the UCS, respectively. The sigma(ci)and sigma(cd)decrease with increasing the sorting coefficient (S-c), average grain size (d(50), mm), contents of feldspar (F, %), and lithic fragment (LF, %). On the other hand, increasing quartz content (Qtz, %) increases those variables. Remarkable changes were obtained in the sigma(ci)and sigma(cd)when effective porosity (n(e)) and pulse wave velocity (V-p) of the rocks exceed 3% and 3.00 km/s, respectively. As a result of mineralogical analyses and laboratory studies, statistical analyses were carried out. Accordingly, the sigma(ci)and sigma(cd)could be estimated reliably using several empirical relationships established in the present study. In order to represent the importance and utilization of rock mineralogy and texture for underground mining applications in the ZHB, several suggestions and considerations related to aV-cut gallery blasting operation were introduced.Article Citation - WoS: 10Citation - Scopus: 14Evaluation of Size Reduction Process for Rock Aggregates in Cone Crusher(Springer Heidelberg, 2020-06-04) Koken, EkinThe size reduction process of rocks in cone crushers is one of the most important issues, particularly for the secondary and tertiary stages of crushing operations. In this study, 17 different rock types were considered for the evaluation of their size reduction variations that occurred in a laboratory-scale cone crusher. Based on several mineralogical, physico-mechanical, and aggregate properties determined for each rock type, the crushability tests were performed. Before and after the crushability tests, particle size distribution (PSD) of the uncrushed (feed) and crushed (product) materials were determined by sieve analyses. On the basis of these PSDs, the degree of rock crushability (DRC) was attempted to quantify by simple approaches (i.e., size reduction ratio, SRR, and the theoretical square mesh aperture size that corresponds to the 10% of the cumulative undersize in the product, P-10 (mm)). The crushability test results demonstrated that the DRC in cone crusher could be quantified by focusing on the variations in the SRR and P-10. The SRR and P-10 are associated with three important rock properties, Shore hardness (SH), Los Angeles abrasion loss (LAA, %), and Brazilian tensile strength (BTS, MPa). The textural and mineralogical features of rocks also have substantial impacts on the DRC for several rock types. It was concluded that the combination of the SRR and P-10 could be considered together for the evaluation of DRC in cone crushers. Moreover, further research potentials on the DRC were also discussed in this study.Article Citation - WoS: 1Estimating the Power Draw of Grizzly Feeders Used in Crushing-Screening Plants Through Soft Computing Algorithms(Konya Teknik Univ, 2024-01-02) Koken, EkinIn this study, the power draw (P) of several grizzly feeders used in the Turkish Mining Industry (TMI) is investigated by considering the classification and regression tree (CART), random forest (RF) and adaptive neuro-fuzzy inference system (ANFIS) algorithms. For this purpose, a comprehensive field survey is performed to collect quantitative data, including power draw (P) of some grizzly feeders and their working conditions such as feeder width (W), feeder length (L), feeder capacity (Q), and characteristic feed size (F80). 80 ). Before applying the soft computing methodologies, correlation analyses are performed between the input parameters and the output (P). According to these analyses, it is found that W and L are highly associated with P. On the other hand, Q is moderately correlated with P. Consequently, numerous soft computing models were run to estimate the P of the grizzly feeders. Soft computing analysis results demonstrate no superiority between the performances of RF and CART models. The RF analysis results indicate that the W is necessary for evaluating P for grizzly feeders. On the other hand, the ANFIS-based predictive model is found to be the best tool to estimate varying P values, and it satisfies promising results with a correlation of determination value (R2) of 0.97. It is believed that the findings obtained from the present study can guide relevant engineers in selecting the proper motors propelling grizzly feeders.Article Citation - WoS: 1Citation - Scopus: 2Estimating Uniaxial Compressive Strength of Pyroclastic Rocks Using Soft Computing Techniques(Shahrood Univ Technology, 2024) Koken, EkinIn this study, several soft computing analyses are performed to build some predictive models to estimate the uniaxial compressive strength (UCS) of the pyroclastic rocks from central Anatolia, Turkey. For this purpose, a series of laboratory studies are conducted to reveal physico-mechanical rock properties such as dry density (rho d), effective porosity (ne), pulse wave velocity (Vp), and UCS. In soft computing analyses, rho d, ne, and Vp are adopted as the input parameters since they are practical and cost-effective non-destructive rock properties. As a result of the soft computing analyses based on the classification and regression trees (CART), multiple adaptive regression spline (MARS), adaptive neuro-fuzzy inference system (ANFIS), artificial neural networks (ANN), and gene expression programming (GEP), five robust predictive models are proposed in this study. The performance of the proposed predictive models is evaluated by some statistical indicators, and it is found that the correlation of determination (R2) value for the models varies between 0.82 - 0.88. Based on these statistical indicators, the proposed predictive models can be reliably used to estimate the UCS of the pyroclastic rocks.Article Citation - WoS: 1Citation - Scopus: 1Development of Comprehensive Predictive Models for Evaluating Böhme Abrasion Value (BAV) of Dimension Stones Using Non-Destructive Testing Methods(MDPI, 2024-12-25) Koken, EkinDue to the global demand for dimension stones, fast and reliable evaluation tools are essential for assessing the quality of dimension stones. For this reason, this study aims to develop comprehensive tools for estimating the abrasion resistance of various dimension stones from Turkey. Non-destructive rock properties, including dry density (rho d), water absorption by weight (wa), and pulse wave velocity (Vp), were determined to build a comprehensive database for soft computing analyses. Three predictive models were established using multivariate adaptive regression spline (MARS), M5P, and artificial neural networks (ANN) methodologies. The performance of the models was assessed through scatter plots and statistical indicators, showing that the ANN-based model outperforms those based on M5P and MARS. The applicability of the models was further validated with independent data from the existing literature, confirming that all models are suitable for estimating varying B & ouml;hme abrasion values (BAVs). A MATLAB-based software tool, called B & ouml;hme abrasion calculator (v1.00), was also developed, allowing users to estimate BAV values by inputting adopted non-destructive rock properties. This tool is available upon request, supporting the dimension stone industry and fostering future research in this field.Article Citation - WoS: 6Citation - Scopus: 7Assessment of Los Angeles Abrasion Value (LAAV) and Magnesium Sulphate Soundness (MWL) of Rock Aggregates Using Gene Expression Programming and Artificial Neural Networks(Polska Akad Nauk, Polish Acad Sciences, 2023-07-24) Koken, EkinIt has been acknowledged that two important rock aggregate properties are the Los Angeles abrasion value (LAAV) and magnesium sulphate soundness (Mwl). However, the determination of these properties is relatively challenging due to special sampling requirements and tedious testing procedures. In this stu-dy, detailed laboratory studies were carried out to predict the LAAV and Mwl for 25 different rock types located in NW Turkey. For this purpose, mineralogical, physical, mechanical, and aggregate properties were determined for each rock type. Strong predictive models were established based on gene expression programming (GEP) and artificial neural network (ANN) methodologies. The performance of the proposed models was evaluated using several statistical indicators, and the statistical analysis results demonstra-ted that the ANN-based proposed models with the correlation of determination (R2) value greater than 0.98 outperformed the other predictive models established in this study. Hence, the ANN-based predictive models can reliably be used to predict the LAAV and Mwl for the investigated rock types. In addition, the suitability of the investigated rock types for use in bituminous paving mixtures was also evaluated based on the ASTM D692/D692M standard. Accordingly, most of the investigated rock types can be used in bituminous paving mixtures. In conclusion, it can be claimed that the proposed predictive models with their explicit mathematical formulations are believed to save time and provide practical knowledge for evaluating the suitability of the rock aggregates in pavement engineering design studies in NW Turkey.Article Citation - WoS: 1Assessment of Installed Power for Inclined Belt Conveyors Using Genetic Algorithm and Artificial Neural Networks(Konya Teknik Univ, 2022-06-01) Koken, EkinIn this study, the installed power (P inst , kW) of several inclined belt conveyors operating in the mining industry of Turkey was investigated through two soft computing algorithms (i.e., genetic expression programming (GEP) and artificial neural networks (ANN)). For this purpose, the most crucial belt (i.e., belt length (L), belt width (W), belt inclination (alpha)), operational (i.e., belt speed (Vb) b ) and throughput (Q)) and infrastructural (belt weight (Wb) b ) and idler weight (Wid)) id )) features of 42 belt conveyors were collected for each investigated belt conveyor. The collected data was transformed into a comprehensive dataset for soft computing analyses. Based on the GEP and ANN analyses, two robust predictive models were proposed to estimate the P inst . The performance of the proposed models was evaluated using several statistical indicators, and the statistical evaluations demonstrated that the models yielded a correlation of determination (R2) 2 ) greater than 0.95. Nevertheless, the ANN-based model has slightly overperformed in predicting the P inst values. In conclusion, the proposed models can be reliably used to estimate the P inst for the investigated conveyor belts. In addition, the mathematical expressions of the proposed models were given in the present study to let users implement them more efficiently.Article Citation - WoS: 10Citation - Scopus: 9Assessment of Deformation Properties of CoAl Measure Sandstones Through Regression Analyses and Artificial Neural Networks(Polska Akad Nauk, Polish Acad Sciences, 2023-07-24) Koken, EkinThe deformation properties of rocks play a crucial role in handling most geomechanical problems. However, the determination of these properties in laboratory is costly and necessitates special equipment. Therefore, many attempts were made to estimate these properties using different techniques. In this study, various statistical and soft computing methods were employed to predict the tangential Young Modulus (Eti, GPa) and tangential Poisson's Ratio (vti) of coal measure sandstones located in Zonguldak Hardcoal Basin (ZHB), NW Turkey. Predictive models were established based on various regression and artificial neural network (ANN) analyses, including physicomechanical, mineralogical, and textural properties of rocks. The analysis results showed that the mineralogical features such as the contents of quartz (Q, %) and lithic fragment (LF, %) and the textural features (i.e., average grain size, d50, and sorting coefficient, Sc) have remarkable impacts on deformation properties of the investigated sandstones. By comparison with these features, the mineralogical effects seem to be more effective in predicting the Eti and vti. The performance of the established models was assessed using several statistical indicators. The predicted results from the proposed models were compared to one another. It was concluded that the empirical models based on the ANN were found to be the most convenient tools for evaluating the deformational properties of the investigated sandstones.