Digital technology is being introduced into all areas of human activity. However, there are a number of challenges in implementing these technologies. These include the delayed return on investment, the lack of visibility for decision-makers and, most importantly, the lack of human capacity to develop and implement digital technologies. Therefore, creating a digital training simulator for the industry is an actual task. This paper focuses on the first step in creating a digital training simulator for the industry: developing a dynamic process model. The process chosen is flotation, as it is one of the most common mineral processing methods. The simulation was performed in AVEVA Dynamic Simulation software. The model is based on a determination of reaction rate constants, for which, experiments were conducted on a laboratory pneumomechanical flotation machine with a bottom drive. The resulting model was scaled up to industrial size and its dynamic properties were investigated. In addition, the basic scheme of a computer simulator was considered, and the testing of the communication channels of a dynamic model with systems, equipment and software for digitalizing was conducted. The developed model showed acceptable results for its intended purpose, namely, an exact match to the technological process in terms of time. This helps to account for inertia and a fast response on all tested communication channels, as well as being acceptable for the real-time simulation speed of the solver.

This article is devoted to the prediction of the residual life based on an estimate of the technical state of the induction motor. The proposed system allows to increase the accuracy and completeness of diagnostics by using an artificial neural network (ANN), and also identify and predict faulty states of an electrical equipment in dynamics. The results of the proposed system for estimation the technical condition are probability technical state diagrams and a quantitative evaluation of the residual life, taking into account electrical, vibrational, indirect parameters and detected defects. Based on the evaluation of the technical condition and the prediction of the residual life, a decision is made to change the control of the operating and maintenance modes of the electric motors.

In this study, we utilized multiphase computational fluid dynamics (CFD), and discrete element method (DEM). Effect of the kinetic parameters of the roasting process in a fluidized bed was investigated. Our results indicate that it is possible to numerically integrate the coupled CFD-DEM system without significantly increasing computational overhead. It is also clear, however, that reactor operating conditions, reaction kinetics, and multiphase flow dynamics have major impacts on the roasting products exiting the reactor. We find that, with the same pre-exponential factors and mean activation energies, inclusion of distributed activation energies in the kinetics can shift the predicted average value of the exit gas-solidphase and its statistical distribution, compared to single-valued activation-energy kinetics. These findings imply that accurate resolution of the reaction activation energy distributions will be important for optimizing roasting processes.