INVESTIGATION OF THE RANDOM VALUE STATISTICAL DISTRIBUTIONS MODELS CHOICE INFLUENCE ON THE MINING OPERATIONS MODELING RESULTS

The importance of production planning for improving the performance indicators of a mining enterprise is indicated. The possibility of simulation modeling using for this aim is shown. It is shown that the created model has a large number of stochastic parameters. It is investigated that there is a problem of research lack about the choice influence of the mining modeling results with different statistical distributions. It is known that with an increase in stochastic deviations from the initial parameters, the productivity of queuing systems decreases. Purpose of work is to study this influence with four statistical distributions of a random quantity (uniform, normal, negative bi-nomial and Poisson distribution) for individual operations and their combinations. In addition, it is necessary to determine how much a change in one particular parameter will affect the overall result of the modeling. Materials and methods. In the previously created simulation model, a stochastic delay is added to the time of individual operations. The addition of such a delay with different sta-tistical distributions and with the same mathematical expectation is investigated. The simulation re-sults are compared with each other, for each individual operation the absolute and relative devia-tion of the results is shown. Further, a similar simulation is performed when all the simultaneously selected parameters changing. Result. It is shown that the magnitude of the deviation significantly differs among all deviations. It is shown that for various single changes in operations, the largest and smal-lest deviations can be given by different statistical distributions. To study the joint change with all parameters, 3 modeling scenarios are implemented: all uniform distributions (this case is used now), the scenario with the smallest deviation and the scenario with the largest deviation. It is shown that switching to another scenario leads to a significant change in the simulation. Conclusion. It is con-cluded that the used significant influence of statistical distributions choice to the accuracy of model-ing the operation of the mining machine is shown, especially when they are taken into account to-gether. The results can be used to clarify the influence of individual factors in the simulation model and improve the planning of potash mining operations, for individual mining machines too.

Organizational structures of mining companies: composition and development

The organizational structures of large companies from the same industry are typical. But the geographic, economic and political conditions of activity impose some particularities on them. In-depth analysis of the organizational structure allows us to notice these external factors and identify the internal characteristics and management model. The paper studies the organizational structures of large potash mining companies. The criteria for identifying the structural divisions of companies are determined, conclusions are drawn about the most typical organizational structures in the industry, the most likely ways of developing the corresponding management models are considered.

Hydrochemical Classification of Groundwater with Artificial Neural Networks

<p>Growing datasets of inorganic hydrochemical analyses together with large differences in the measured concentrations raise the demand for data compression while maintaining critical information. The data should subsequently be displayed in an orderly and understandable way. Here, a type of artificial neural network, Kohonen’s self-organizing map (SOM), is trained on inorganic hydrochemical data. Based on this network, clusters are built and associated to the salinity source distribution of the spatial variation at a former potash mining site. This combined two-step clustering approach managed to assign the groundwater analyses automatically to five different clusters, three geogenic and two anthropogenic, according to their inorganic chemical composition. The spatial distribution of the SOM clusters helps to understand the large scale hydrogeological context. This approach provides the hydrogeologist with a tool to quickly and automatically analyze large datasets and present them in a clear and comprehensible format.</p>