Whenever any type of ore deposit is developed, it comes with significant risks, such as uncertain reserves, harsh climate conditions, and other uncontrollable factors. To manage these risks effectively, companies need to quickly adapt to changing conditions. This paper presents a method for evaluating risks using a simulation model. The main objective is to identify factors of operational risk and determine the project parameters that have the greatest impact on the probability of a risk event. The method includes the classification of operational risks based on the way they arise; the creation of a risk decomposition matrix dividing risks by production tasks; and the construction of a mathematical model using the identified risk factors. The method was tested by developing a simulation model of an underground mine conveyor network in Anylogic (8.9.2) software. A simulation experiment showed that conveyor shutdowns could result in an 11.23% reduction in annual revenue. Based on the results, recommendations were made on how these risks can be reduced and on the need to implement a transport system to increase resilience.

The article discusses the requirements for propellers of bottom mining machines for the development of deep-sea deposits of such unique minerals as ferromanganese nodules, deep-sea polymetallic sulfides, cobalt-manganese crusts. The main factor is the minimal impact on the underwater ecosystem. Based on existing prototypes of mining machines and patents, an analysis of the advantages and disadvantages of existing conceptual mechanisms for the movement of mining machines was carried out, as a result of which it was determined that the most promising machines are those that do not contact the surface of the seabed, but such machines have a number of disadvantages. No less promising today are walking and tracked vehicles. The paper presents simplified formulas for determining the required minimum traction force when moving underwater mining machines, and simplified formulas for determining the speed of movement were obtained, which also clearly indicates the advantage of floating mining machines. According to the obtained graphical dependencies, a set of parameters plays an important role in the productivity of mining machines, including the speed of movement, overall dimensions, weight, and shape of the machine. The article also describes a complex for the extraction of minerals dispersed along the seabed, developed by the staff of the Department of Mechanical Engineering of St. Petersburg State University with various configuration options for the complex depending on the type of mineral, as well as the density of its distribution.

Any technological process including metallurgical processes is supported by the control system operation, accompanied by large information flows to be formed. However, the most part of this information is not used by specialists due to restricted capabilities of a human being that is, in fact, incapable of processing such information flows. It has been demonstrated that process control is significantly influenced by the human factor. This paper contains a methodology to process production information that permits the process personnel to use its potential in a more rational way to control the process. An approach has been reviewed to studying metallurgical processes using the analysis of indirect indicators of process management, namely the spectral density and auto-correlation function of main process parameters. A method to separate useful signals from noise has been studied. A method has been given to check the ACS management efficiency using primary material flows. The adopted methodology for processing experimental data permits interpreting the obtained results for their further practical application to develop new algorithms for process control and improve the existing control system.