Abstract:
Loading and hauling operations are considered an important part of the production process cycle in open-pit mines and account for a significant portion of the total production cost. To evaluate technical options and facilitate decision-making during the design and feasibility study stage of mining projects, access to fast and efficient cost estimation models is essential, and their accuracy and reliability are among the critical factors in project success. In this research, a model for estimating the costs of these machines has been presented using univariate and multivariate regression analysis. Multivariate analysis was performed using the Principal Component Analysis (PCA) method. The studied machinery includes common loaders such as hydraulic and cable shovels, wheel loaders, draglines, and integrated haul trucks. Cost functions are separated based on the type of capital and operational costs. Each of the operational cost items is also presented as a separate function. The independent variable in the univariate analysis is the loading and hauling capacity, and in the multivariate analysis, depending on the type of equipment, it includes characteristic variables of each machine such as bucket capacity, loading/dumping height, digging depth, boom length, and engine power. The efficiency of each multivariate cost function was measured using the Mean Absolute Error (MAE), and their maximum was estimated at 17%.
Machine summary:
In this research, this important issue is addressed, and a comprehensive and up-to-date model for estimating capital and operational costs, as well as the main items of operational costs for the primary production machinery in open-pit mines, has been presented.
in 1997 [20], Noakes and Lanz in 1993 [21], Shafiee et al.
In this research, an attempt has been made to present an up-to-date model for the preliminary estimation of capital and operational costs of discontinuous loading and hauling machinery in open-pit mines, while addressing the shortcomings of previous models in terms of covering variables affecting the cost.
b Refer to the page image Figure 1- Data analysis method 2-2 Multivariate cost model The linear shape of the multivariate regression function can be shown as Equation 3; in which X1 to Xn are the independent variables explaining the dependent variable, cost, or Y.
Table 5 Coefficients of regression functions for estimating operational cost item costs (dollars per hour) Refer to page image 5- Multivariate Analysis Univariate functions are suitable in cases where cost estimation is performed for the initial stages of a project and access to machinery-related data is limited.
1. Mean Absolute Error Rate Refer to page imageThe results and functions obtained for different components of operational costs for hauling and loading machinery are also visible in Table 9.
(2015) "Parametric estimation of capital costs for establishing a coal mine: South Africa case study", The Journal of the Southern African Institute of Mining and Metallurgy, Vol. 115, pp.
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