Mine Economic and Management
Hadi Fattahi; Hossein Ghaedi
Abstract
The maximum energy consumption of stone cutting machines is one of the important cost factors during the process of cutting construction stones. Accurately predicting and estimating the maximum energy consumption performance of the cutting machine, along with estimating the cutting costs, can help approach ...
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The maximum energy consumption of stone cutting machines is one of the important cost factors during the process of cutting construction stones. Accurately predicting and estimating the maximum energy consumption performance of the cutting machine, along with estimating the cutting costs, can help approach the optimal cutting operating conditions to reduce energy consumption and minimize machine depreciation. However, due to the uncertainty and complexity of building stone textures and properties, determining the maximum energy consumption of the device is a difficult and challenging task. Therefore, this paper employs the rock engineering system method to solve the aforementioned problem. To this end, 120 test samples were collected from a marble factory in the Mahalat region of Iran, representing 12 types of carbonate rocks. The input parameters considered for the analysis were the Mohs hardness, uniaxial compressive strength, Young's modulus, production rate, and Schimazek’s F-abrasiveness factors. In the study, 80% of the collected data, equivalent to 96 data points, were utilized to construct the model using the rock engineering system-based method. The obtained results were then compared with other regression methods including linear, power, exponential, polynomial, and multiple logarithmic regression methods. Finally, the remaining 20 percent of the data, comprising 24 data points, were used to evaluate the accuracy of the models. Based on the statistical indicators, namely root mean square error, mean square error, and coefficient of determination, it was found that the rock engineering system-based method outperformed other regression methods in terms of accuracy and efficiency when estimating the maximum energy consumption.
Exploitation
B. Sohrabian; R. Mikaeil; R. Hasanpour; Y. Ozcelik
Abstract
The quality properties of andesite (Unit Volume Weight, Uniaxial Compression Strength, Los500, etc.) are required to determine the exploitable blocks and their sequence of extraction. However, the number of samples that can be taken and analyzed is restricted, and thus the quality properties should be ...
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The quality properties of andesite (Unit Volume Weight, Uniaxial Compression Strength, Los500, etc.) are required to determine the exploitable blocks and their sequence of extraction. However, the number of samples that can be taken and analyzed is restricted, and thus the quality properties should be estimated at unknown locations. Cokriging has been traditionally used in the estimation of spatially cross-correlated variables. However, it can face unsolvable matrices in its algorithm. An alternative to cokriging is to transform variables into spatially orthogonal factors, and then to apply kriging to them. Independent Component Analysis (ICA) is one of the methods that can be used to generate these factors. However, ICA is applicable to zero lag distance so that using methods with distance parameter in their algorithms would be advantageous. In this work, Minimum Spatial Cross-correlation (MSC) was applied to six mechanical properties of Cubuk andesite quarry located in Ankara, Turkey, in order to transform them into approximately orthogonal factors at several lag distances. The factors were estimated at 1544 (5 m × 5 m) regular grid points using the kriging method, and the results were back-transformed into the original data space. The efficiency of the MSC-kriging was compared with Independent Component kriging (IC-kriging) and cokriging through cross-validation test. All methods were unbiased but the MSC-kriging outperformed the IC-kriging and cokriging because of having the lowest mean errors and the highest correlation coefficients between the estimated and the observed values. The estimation results were used to determine the most profitable blocks and the optimum direction of extraction.
