One of the most critical aspects of mine design is to determine the optimum cut-off grade. Despite Lane’s theory, which aims to optimize the cut-off grade by maximizing the net present value (NPV), which is now an accepted principle used in open pit planning studies, it is less developed and applied in optimizing the cut-off grade for underground polymetallic mines than open pit mines, as optimization in underground polymetallic mines is more difficult. Since there is a similar potential for optimization between open pit mines and underground mines, this paper extends the utilization of Lane’s theory and proposes an optimization model of the cut-off grade applied to combined mining-mineral processing in underground mines with multi-metals. With the help of 3D visualization model of deposits and using the equivalent factors, the objective function is expressed as one variable function of the cut-off grade. Then, the curves of increment in present value versus the cut-off grade concerning different constraints of production capacities are constructed respectively, and the reasonable cut-off grade corresponding to each constraint is calculated by using the golden section search method. The defined criterion for the global optimization of the cut-off grade is determined by maximizing the overall marginal economics. An underground polymetallic copper deposit in Tibet is taken as an example to validate the proposed model in the case study. The results show that the overall optimum equivalent cut-off grade, 0.28%, improves NPV by RMB 170.2 million in comparison with the cut-off grade policy currently used. Thus, the application of the optimization model is conducive to achieving more satisfactory economic benefits under the premise of the rational utilization of mineral resources.
At present, with the increase of production capacity and the promotion of production, the reserves
of most mining enterprises under the original industrial indexes are rapidly consumed, and the full
use of low-grade resources is getting more and more attention. If mining enterprises want to make
full use of low-grade resources simultaneously and obtain good economic benefits to strengthening
the analysis and management of costs is necessary. For metal underground mines, with the gradual
implementation of exploration and mining projects, capital investment and labor consumption are
dynamic and increase cumulatively in stages. Consequently, in the evaluation of ore value, we should
proceed from a series of processes such as: exploration, mining, processing and the smelting of
geological resources, and then study the resources increment in different stages of production and the
processing. To achieve a phased assessment of the ore value and fine evaluation of the cost, based on
the value chain theory and referring to the modeling method of computer integrated manufacturing
open system architecture (CIMOSA), the analysis framework of gold mining enterprise value chain is
established based on the value chain theory from the three dimensions of value-added activities, value
subjects and value carriers. A value chain model using ore flow as the carrying body is built based on
Petri nets. With the CPN Tools emulation tool, the cycle simulation of the model is carry out by the
colored Petri nets, which contain a hierarchical structure. Taking a large-scale gold mining enterprise
as an example, the value chain model is quantified to simulate the ore value formation, flow, transmission
and implementation process. By analyzing the results of the simulation, the ore value at different
production stages is evaluated dynamically, and the cost is similarly analyzed in stages, which can improve mining enterprise cost management, promote the application of computer modeling and
simulation technology in mine engineering, more accurately evaluate the economic feasibility of ore
utilization, and provide the basis for the value evaluation and effective utilization of low-grade ores.
Overseas mining investment generally faces considerable risk due to a variety of complex risk factors. Therefore, indexes are often based on conditions of uncertainty and cannot be fully quantified. Guided by set pair analysis (SPA) theory, this study constructs a risk evaluation index system based on an analysis of the risk factors of overseas mining investment and determines the weights of factors using entropy weighting methods. In addition, this study constructs an identity-discrepancycontrary risk assessment model based on the 5-element connection number. Both the certainty and uncertainty of the various risks are treated uniformly in this model and it is possible to mathematically describe and quantitatively express complex system decisions to evaluate projects. Overseas mining investment risk and its changing trends are synthetically evaluated by calculating the adjacent connection number and analyzing the set pair potential. Using an actual overseas mining investment project as an example, the risk of overseas mining investment can be separated into five categories according to the risk field, and then the evaluation model is quantified and specific risk assessment results are obtained. Compared to the field investigation, the practicability and effectiveness of the evaluation method are illustrated. This new model combines static and dynamic factors and qualitative and quantitative information, which improves the reliability and accuracy of risk evaluation. Furthermore, this evaluation method can also be applied to other similar evaluations and has a certain scalability.
This paper researches the application of grey system theory in cost forecasting of the coal mine. The grey model (GM(1.1)) is widely used in forecasting in business and industrial systems with advantages of minimal data, a short time and little fluctuation. Also, the model fits exponentially with increasing data more precisely than other prediction techniques. However, the traditional GM(1.1) model suffers from the poor anti-interference ability. Aimed at the flaws of the conventional GM(1.1) model, this paper proposes a novel dynamic forecasting model with the theory of background value optimization and Fourier-series residual error correction based on the traditional GM(1.1) model. The new model applies the golden segmentation optimization method to optimize the background value and Fourier-series theory to extract periodic information in the grey forecasting model for correcting the residual error. In the proposed dynamic model, the newest data is gradually added while the oldest is removed from the original data sequence. To test the new model’s forecasting performance, it was applied to the prediction of unit costs in coal mining, and the results show that the prediction accuracy is improved compared with other grey forecasting models. The new model gives a MAPE & C value of 0.14% and 0.02, respectively, compared to 1.75% and 0.37 respectively for the traditional GM(1.1) model. Thus, the new GM(1.1) model proposed in this paper, with advantages of practical application and high accuracy, provides a new method for cost forecasting in coal mining, and then help decision makers to make more scientific decisions for the mining operation.