TR-Dizin İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/396
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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: 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.