The research considers conducting orthogonal experiment (OT) as one of the stages in developing a new discrete element method (DEM) parameters calibration approach. The measured responses in experiment are the parameters obtained by DEM animation processing using machine vision system (MVS). The variable factors in experiment are DEM parameters. A brief overview of an existing calibration approaches given in the article. The choice of OT as a design of experiment tool among other mathematical tools discussed. Experiments conducted using specially developed rig where bulk material's flow captured as DEM animation. DEM animation converted to video and then processed using MVS that allow register the values of such parameters as angle of repose or expiration time (measured responses). The results of the OT show that it is possible to identify four measured responses with the most valuable correlation coefficient. DEM parameters with the biggest influence on the measured responses identified for each of the obtained regression. Obtained results are useful in learning or iterative algorithms development for DEM parameters calibration.

In this paper, we consider the need to create an algorithmized digital platform for the development of electric charging networks of road transport and to optimize the load on the current power grid complex. In modern conditions, this platform can become an example of a mechanism for public-private partnerships and the development of environmentally friendly transport. The purpose of the platform is to provide users with up-to-date information about the state of the electric vehicle battery, reduce the cost of creating a network charging infrastructure for electric vehicle transport, automated resource accounting for power supply companies, and other services. The use of information technologies to combine the processes of economic relations in the field of energy sales services will attract funds for infrastructure development by increasing the load factor of generating capacities and unloading power centers. In the article, the authors propose a digital platform architecture using modern technologies such as IIoT, Vehicle to Grid (V2G), Machine learning, Big Data.

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.