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A quantitative technique was conducted at Rosh Pinah Zinc mine, Namibia with its main purpose to determine airways resistance which is a function of the parameters; roughness of the airways and the friction factor. The 32 branch points (i.e. a-ag) that stand for ventilation circuit have been selected. Data collected includes, length and width of ai...
Contexts in source publication
Context 1
... values were computed using the data collected and the equations earlier detailed. Table 7 compares the mean roughness, friction factors and resistance for each level of the mine. It should be pointed out that, as the mine advances its production faces deeper, these values increases suddenly. ...Context 2
... Table 8, Rosh Pinah mine friction factor value was determined by adding all individual mean friction factors in Table 7 and treated as single average value (0.01039 kg/m 3 ) of the mine. However, this value was compared with friction factors values 0.00762 kg/m for Potash Mine and 0.01200 kg/m metal mine publication respectively. ...Context 3
... model has maintained minimum air demand. The Levels airways resistances are shown in Table 7 and Table 9. Although, the benefits are the same as of option 4, booster fans can reduce the ability to control recirculation of air in underground mines. ...Context 4
... values were computed using the data collected and the equations earlier detailed. Table 7 compares the mean roughness, friction factors and resistance for each level of the mine. It should be pointed out that, as the mine advances its production faces deeper, these values increases suddenly. ...Context 5
... Table 8, Rosh Pinah mine friction factor value was determined by adding all individual mean friction factors in Table 7 and treated as single average value (0.01039 kg/m 3 ) of the mine. However, this value was compared with friction factors values 0.00762 kg/m for Potash Mine and 0.01200 kg/m metal mine publication respectively. ...Similar publications
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Citations
... The second method consists of in situ measurement of pressure drop and airflow in rectilinear sections of existing airways and determination of friction factors with air density correction taken into account [26], [27]. The results are subsequently employed to predict the resistances of similar planned airways. ...
The prediction of frictional air resistance using the inherent properties of roadways is of great significance for ventilation network computation and flow regulation in underground mines. This study proposes an improved stacked learning and error correction-based prediction model for the frictional air resistance of mine airways, called friction factor. A prediction set is established by selecting ten factors, including tunnel spatial features and support forms, with the ventilation resistance coefficient as the label. The improved stacked model consists of two layers. The first layer is the base learning module, which is composed of four components: Principal Components Analysis and Back Propagation (PCA-BP), GA-Projection Pursuit Regression (GA-PPR), Random Forest (RF), LightGBM (LGBM). The second layer is the meta-learning module, which is composed of the Ridge Regression (RR). Compared to traditional stacked models, the improved model first uses the Extreme Gradient Boosting (XG Boost) learner to evaluate the significance of input feature variables to eliminate redundancy and improve accuracy, thus enhancing prediction precision and computational efficiency. Then, the first-layer prediction results are weighted based on the errors of different prediction models in the training set using K-fold cross-validation. Box-Cox transformation is applied to the training set data from the first layer to the second layer to improve prediction normality and homogeneity. The error correction prediction model extracts the historical prediction errors from the meta-learning module and constructs an error prediction model using support vector machines (SVR), which are then combined with the meta-learning results to obtain the final prediction. The improved stacked model is compared with traditional ensemble learning models and single prediction models, and quantified using three metrics: root mean square error (RMSE), mean absolute error (MAE), and R-squared (R
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). The results demonstrate that the proposed improved model effectively enhances the prediction accuracy of the ensemble learning models, providing a new prediction method for the accurate acquisition of the friction factor of mine airways.
