## 4 Results and Discussion
The full 3D model of the installation is shown in Figure 11. Apart from the 3D view of this installation, the application showed the change of the parameters of the dynamic model in real time. Thus, according to Figure 2, we had a dynamic model of the process and physical models of the process parts. Figure 11 (left) shows the detailed part of the installation in its industrial 3D form. Aside from that, Figure 11 (right) shows the interaction between the user, the augmented reality system (control buttons), the real part of the equipment (laboratory stands that received control  
Figure 9: MQTT broker. signals and changed the physical state of the equipment or the dynamic model), and the virtual part of the equipment (the dynamic model and its detailed 3D image). It should be emphasized that there was a difference between the variables in Figure 1 (dynamic model), Figure 8 (data collection server), and Figure 11. This was due to the fact that Figure 8 shows a list of possible variables of the dynamic model. The names of the tags in it were derived automatically and were defined by the Aveva Dynamic Simulation software manufacturer. Figures 10 and 11 show the variables actually captured in the unit, and their nomenclature corresponds to the nomenclature of the industrial object. There was a difference between the parameters of the dynamic model, the augmented reality application, the parameters of the automation system, the parameters of the energy system, and the parameters accepted as standard in an industrial enterprise. This is a big concern. It is required to use a single standard for forming the variable name. However, currently such standards are not used, and the correlations between the variables are established by means of special linking tables. In this work, the following variables, shown as an example in Figure 8, were removed at the level of the dynamic model: special automation system operation variables (control loop starts (Master 1_B_out)), set point feed for temperature changes, flow (LVLSP) in streams and equipment, pressure (P and PB), temperature (T), flow rate (W) in the raw streams (S2, S5, S7, S9, and S11), in the source (SRC1 and SRC2), in the drum (V2), and in the final reservoirs (SNK1, SNK2). Additionally, the positions of valves XV1, 2, and 3, the status of motors M1, 2, 3, and 4, and other parameters of the power system (e.g., current and power voltage) were taken. Figure 8 shows a sample list of the variables. If necessary, one could display any parameter in the system (for one flow or type of equipment, there were about 100 parameters). The variables shown in Figure 11 for the engine are presented with the name used in production. These names must be clear to the users of the system. Therefore, I321 for the current, U321 for the voltage, and P321 for the power were used. It should be emphasized that these parameters were taken from the laboratory model of the pump shown in Figure 4 with the current, power, and voltage sensors mounted on them. Linking (linking the same and other variable names) was performed in the KEPServer (i.e., in the communication layer) of Figure 2.  
Table 3 shows the results of the channel test. The test protocol (Table 4) shows that data transfer via the augmented reality application to the physical devices and the dynamic model was possible, with MQTT being the primary protocol. However, the physical devices and the dynamic model at greater numbers did not support the MQTT protocol. For this purpose, the OPCUA protocol was required to be included in the system structure. Figure 11: View obtained via live streaming of the Microsoft Hololens application, showing the scene pump and model. \begin{table}
\begin{tabular}{l c c} \hline \hline
**Action** & **Expected Result** & **Test Result (Passed, Failed, or Blocked)** \\ \hline \hline \