This paper presents a numerical simulation of powder sintering. The numerical model presented in this paper uses the discrete element method, which suggests that the material can be modeled by a large set of discrete elements (particles) of a spherical shape that interact with each other. A methodology has been developed to determine the DEM parameters of bulk materials based on machine vision and a neural network algorithm. The approach is suitable for obtaining the exact values of the DEM parameters of the investigated bulk material by comparing the visual images of the material’s behavior at the experimental stand in reality and in the model. Simulation of sintering requires an introduction of cohesive interaction between particles representing interparticle sintering forces. Numerical sintering studies were supplemented with experimental studies that provided data for calibration and model validation. The experimental results have shown a significant capability of the designed numerical model in modeling sintering processes. Evolution of microstructure and density during sintering have been studied under the laboratory conditions.

The aim of this paper is to study the numerical modeling of the crushing process in a jaw crusher with complex motion jaws using Discrete Element Method (DEM). The main goals are to ensure the conformity of numerical simulation results to the experimental data, optimize the operating modes and the crusher design and develop automated verification and calibration methodology. It is offered to use automated selection of mathematical model parameters using large- or laboratory-scale experiments instead of deriving parameters from conventional intensive material tests (e.g. drop-weight test). Experimental data were obtained from a large-scale crusher designed and manufactured by the company “Mekhanobr-Tekhnika”. Both jaws have their drive shafts located asymmetrically; one jaw is driven in the upper part, the second at the base. The crushing process involves fragmentation of single smoothed rectangular granite particles of the same weight. During the experiment, the required parameters such as particle size distribution were obtained for further computer simulations. Based on the obtained experimental data, the Rocky DEM model was built. Simulation breakage parameters were calibrated using automated parameter selection methodology based on multiple set of parameter-controlled simulations. A good correlation of the experimental data and numerical simulation data was obtained. The methodology in combination with well-known capabilities of simulation modeling will help reduce the research time in the development of new mineral processing equipment.

Based on the literature review, the authors arrive at the conclusion that abrasivity of rocks is not only governed by their strength, but also by the structure and parameters of mineral inclusions, such as shape, size and amount. In terms of the Khibiny’s apatite–nepheline ore of various kinds with different strength and mineralogical composition, it has experimentally been estimated using the modified Baron–Kuznetsov procedure how the strength of rocks affects the wear rate of specimens made of steel 110G13L that is most often used for manufacturing rapidly wearing parts of mining equipment. It is found that for all ore kinds tested, the steel wear rates are approximately the same irrespective of the ore strength. It has been supposed that on abrasion of steel on hard rock (f ~ 10) composed of grains of hard tabular nepheline with thin partings of relatively soft fine-grain apatite, nepheline grains disengage slowly, the contact surface becomes smooth and steel wear rate is reduced consequently. On the other hand, on abrasion on weak rock (f ~ 0.5) composed mostly of apatite, fast failure of this type rock causes continuous exposure of rough-surface apatite grains, which accelerates steel wear resistance. On stee