# Digitalization in Open-Pit Mining: A New Approach in Monitoring and Control of Rock Fragmentation
## 2 Materials and Methods  
The proposed method of the study was based on two main assumptions:  
1. When evaluating the grading composition, the parameter of the lump form and its distribution should be taken into account in addition to the average lump size, etc. This will significantly increase the information component of the technological processes;
2. Analytical algorithms linking the parameters of rock fragmentation and technological processes and equipment should be developed for each technological operation separately, as well as to analyze and implement interrelationships with subsequent and previous stages. Figure 1 shows a scheme of technological operations of a mining production with a detailed section of the ore dressing process. As a rule, it includes the following stages: geological research, blasting, trommelling, crushing, grinding, and flotation. In this study, the validity of the hypothesis was tested experimentally in the four stages shown in Figure 1. Figure 2 presents the scheme of information-analytical model for these processes. As indicated in Figure 1, this parameter is related to the measurement of the rock fragmentation and takes into account the rock pieces form. It should be emphasized that the authors of the work chose the most characteristic parameters of each technological operation in order to simplify the information model of these processes. In addition to the information component, the model presents the analytical component (A). This is due to the relationship between blasting parameters--explosive weight, borehole diameter and depth, borehole grid, and fragmentation obtained as a result of blasting. There are a significant number of approaches to optimize drilling and blasting parameters to obtain the desired rock fragmentation during the blasting phase [32; 33]. The dotted lines and red color show that the analytical algorithms of each operation should move to multistage analytics of all operations at once. In the context of present study, this approach will additionally provide the optimization of technological processes and apparatuses. This will allow, in turn, to more accurately formulate the inverse task of adjusting  
Figure 1: Scheme of technological processes at a mining production. Figure 2: Scheme of the information-analytical model. I and A are the two components of this model. Component I describes the information model and a list of the main variables. Additional parameters of the existing model are highlighted in red with N*. Black, solid lines highlight operations that are typical for mining enterprises. the parameters of technological parameters. As the increased information component will redefine existing and indicate new dependences between input and output parameters of all production stages. In this case, the increase in profits for a company that applies this model to its process control systems for mining operations is evident. The following three forms of pieces were defined: cubic (all sides are equal), square (one of the sides is twice as big as the others), and spilt (one of the sides is three or more times bigger than the others). Thedivision into forms is approximate and rather crude. In reality, it is better to take more variants of forms. However, demonstrating the division into forms even at the elementary level with an indication of the possible effectiveness will be evident when using a broader classification of forms. In this case, division into three simple classes will greatly simplify the experiments. To determine the lumps form and obtain their distribution in the rock mass, we propose to use the theory of neural networks and the recognition algorithm [34; 35] shown in Figure 3.  
It should be noted that such an algorithm can be applied to image the entire rock mass. In this instance, we will obtain a picture of the lumps' distribution by fragment form for the whole size range.