Mathematical modeling of the processes of estimating reserves of iron ore raw materials in conditions of uncertainty

Summary. This article proposed to estimate the technological parameters of mining and metallurgical industry (iron ore stocks), given the fuzzy set values in conditions of uncertainty using the balance sheet and industrial methods of calculation of reserves of ore. Due to the fact that the modeling of the processes of extraction of ore is associated with parameters of the equations that contain variables with different nature of uncertainty, it is better to provide all the information on a single formal language of fuzzy set theory. Thus, the proposed model calculation and evaluation of reserves of iron ore by different methods in conditions of uncertainty geological information on the basis of the theory of fuzzy sets. In this case the undefined values are interpreted as intentionally "fuzzy", since this approach largely corresponds to the real industrial situation than the interpretation of such quantities in terms of random. Taken into account the fact that the application of the probabilistic approach leads to the identification of uncertainty with randomness, but in practice, the basic nature of uncertainty in the calculation of reserves of iron ore is unclear. Under the proposed approach, each fuzzy parameter is a corresponding membership function, to determine which proposed using a General algorithm, as the result of algebraic operations on arbitrary membership function of the inverse numerical method. Because of the existence of many models describing the same production process in different methods (for example, the balance model or industrial model) and under different assumptions proposed to coordinate such models on the basis of the model of aggregation of heterogeneous information. For matching this kind of information, its generalization and adjustment of the outcome parameters, it is expedient to use the apparatus of fuzzy set theory that allows to obtain quantitative characteristics of imprecisely specified parameters and make the most informed decisions.