WoS İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/394
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Article Citation - WoS: 14Citation - Scopus: 18Investigating the Effects of Feeding Properties on Rock Breakage by Jaw Crusher Using Response Surface Method and Gene Expression Programming(Elsevier, 2021-05) Koken, Ekin; Lawal, Abiodun IsmailThe present study investigates the effects of feeding properties on rock comminution by a laboratory-scale jaw crusher. For this purpose, detailed crushability tests were carried out on four different rock types to assess their degree of rock crushability (DRC). Various feeding sizes (9.5 - 19 mm) and quantities (500 - 1500 g) were adopted to reveal the choke feeding intensity during crushing actions. The efficiency of feeding properties was investigated through the response surface methodology (RSM). The RSM results demonstrated that the characterized feeding size (F-80, mm) dominates the general size reduction, whereas the feeding quantity (m(f), g) is associated with the crushing energy consumption and product flakiness. Therefore, the choke feeding intensity has a direct relation to the m(f) and F-80. In addition, novel gene expression programming (GEP) models were employed to generate empirical formulations to predict the DRC parameters. The established GEP models have a satisfactory estimation capability. Therefore, the DRC of the investigated rocks can be optimized through the proposed GEP models based on the coupling variables of m(f) and F-80. (C) 2021 The Society of Powder Technology Japan. Published by Elsevier B.V. and The Society of Powder Technology Japan. All rights reserved.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: 9A Comparative Study on Power Calculation Methods for Conveyor Belts in Mining Industry(Taylor & Francis Ltd, 2021-07-29) Koken, Ekin; Lawal, Abiodun Ismail; Onifade, Moshood; Ozarslan, AhmetThis paper covers different methods to evaluate the power consumption of several conveyor belt systems (CBSs) used in the Turkish Mining Industry (TMI). Based on each CBS's operational features, the power consumption (P-c, kW) was measured directly on motorised head-pulleys. The P-c was investigated through several conventional, statistical, and machine learning methods. This study shows that the DIN 22,101 could be the most convenient conventional method for the investigated CBSs. On the other hand, based on the nonlinear regression (NLR) and genetic expression programming (GEP) models, two new approaches were suggested for the design and optimisation of the P-c.