Ventilation friction factor determination and comparison: Two case studies of potash mining

Bascompta, Marc; Pera, Lluís Sanmiquel; Anticoi, H.F.; Oliva, Josep

doi:10.17159/2411-9717/707/2019

Cited by 5 publications

(5 citation statements)

References 6 publications

(8 reference statements)

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“…Friction factors which are essential in the determination of the airways resistance require a data bank of the physical characteristics of the airways (Meyer, 1998 as referenced in [1]). Therefore, 32 (i.e.…”

Section: Data Collection and Methodsmentioning

confidence: 99%

“…The square law Equation (1) is the fundamental expression used to examine the airflow behaviour in underground mines [1] [7] which relates the theories of pressure, airflow, and resistance to the passage of air through an airway.…”

Section: Theoretical Equationsmentioning

confidence: 99%

“…The physical characteristics of the airways are essential to understand when determining the most effective airflow distribution, enhancing the volumetric effi-ciency and saving ventilation cost, because as mining activities advances, the production faces have progressed away from the main fans, which increased the resistance to airflow [1] [2] [3] [4]. Furthermore, airways physical characteristics such as length, area and roughness as well as obstacles (i.e.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Determination of Airways Resistance, Volumetric Efficiency and Development of Ventilation Model of Rosh Pinah Zinc Mine Namibia

Kalenga¹,

Akande²

2021

JMMCE

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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 airways, air velocity; air density, and roughness of the airways which were used to determine coefficient of frictions, friction factors and airway resistances. A ventilation model was developed. In order to improve the current ventilation model, airways resistance of the mine was determined and simulated in a modified model using VentSim TM software. An average total airways resistance of 0.32027 Ns 2 /m 8 has been achieved for Rosh Pinah mine. It should be pointed out that, as the mine advances its production faces deeper, this value would increases suddenly. Simulation revealed that as much as 34.4 m 3 /s of air can be received at the production faces, compared to the measured received amount of 19.3 m 3 /s. Therefore, volumetric efficiency of the mine was improved from 29.3% to 68.3%. It was also noticed that after importing the resistance values into the model together with other parameters, the model was greatly improved and no cause for concern.

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Section: Data Collection and Methodsmentioning

confidence: 99%

Section: Theoretical Equationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Determination of Airways Resistance, Volumetric Efficiency and Development of Ventilation Model of Rosh Pinah Zinc Mine Namibia

Kalenga¹,

Akande²

2021

JMMCE

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“…The airway profiles of the main network and their characteristics were also imported. Basic and simplistic friction factors and base options for the calculation of shock losses assuming a smooth transition and turning radius of tunnels [2,3] were selected for use. These options are important for the calculations of the network solution, and they were used as they represent a reference starting point and a baseline model scenario [4,5] (Table 1).…”

Section: Ventilation Model Design and Validation Processmentioning

confidence: 99%

Ventilation Design Modeling and Optimization for an Underground Bauxite Mine

Karagianni¹,

Benardos²

2021

International Conference on Raw Materials and Circular Economy

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Underground bauxite mining exploitation is a challenging environment for ventilation. In this paper, such a complex underground ventilation network is modeled in detail using the Ventsim software. The network and its characteristics are selected and developed through the software, followed by their calibration and validation against actual measurements of both airflow quantity and quality which have been carried out at the mine site. This model constitutes the base of the new ventilation design for the future mine expansion. The recommendations and the characteristics for the optimization of the new proposed design are finally presented in detail.

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“…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.…”

Section: Introductionmentioning

confidence: 99%

Forecasting the Friction Factor of a Mine Airway Using an Improvement Stacking Ensemble Learning Approach

Qi,

Gao,

Obracaj

et al. 2024

IEEE Access

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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 2 ). 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.

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Ventilation friction factor determination and comparison: Two case studies of potash mining

Cited by 5 publications

References 6 publications

Determination of Airways Resistance, Volumetric Efficiency and Development of Ventilation Model of Rosh Pinah Zinc Mine Namibia

Determination of Airways Resistance, Volumetric Efficiency and Development of Ventilation Model of Rosh Pinah Zinc Mine Namibia

Ventilation Design Modeling and Optimization for an Underground Bauxite Mine

Forecasting the Friction Factor of a Mine Airway Using an Improvement Stacking Ensemble Learning Approach

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