Granulometric composition predicting models after explosion in open-pit mining

Abstract

Introduction. Drilling and blasting operations are first in the workflow and significantly determine the economic efficiency of the entire mining and primary processing workflow in the enterprise. The cost of drilling and blasting operations is a significant part of total production costs of large mining companies. In this context, mining engineers today are facing a crucial technological problem, i.e. the reduction of the off-gauge fraction yield after the explosion. Research aims to develop the models which forecast the granulometric composition of the rock mass taken down as the original factor in reducing the economic waste of the entire workflow. Methodology. The parameters which influence the results of rock mass fragmentation and the techniques of rock mass granulometric composition forecasting in the course of drilling and blasting were analyzed. Results. The present paper gives a brief overview of the global mineral output; provides information on the extraction of key types of minerals (mineral fuel, ferrous metals, non-ferrous metals, precious metals, and construction materials), as well as revenues derived from their sale. On the example of domestic companies, approaches to the issue of forecasting the off-gauge fraction yield after the explosion. Summary. Direction for future actions in creating the model forecasting rock mass yield of a certain fraction after the explosion.

Specifics of MWD Data Collection and Verification during Formation of Training Datasets

“…Many researchers use this approach for various aspects of the mining industry: choice of mining equipment [2], assessment of geomechanical and geological properties of the rock mass [3][4][5], rock classification, determination of drilling and blasting parameters [6,7] and safety estimation of blasting operations [8,9], among others. A detailed review is presented in the paper [10].…”

“…The prediction of the particle size distribution of the muck pile based on the measurement while drilling (MWD) data is a relevant production task. Modern methods for predicting particle size distribution, based on theoretical and empirical models [7,11] and even machine learning methods [12,13], use only drilling and blasting parameters and existing rock classifications by strength and structural features, which sometimes cannot take into account unique structure and heterogeneity of the rock mass to be blasted. In contrast, MWD technology allows the collection of data from each drilled borehole and provides a dense sampling cloud for subsequent prediction of rock size distribution in the muck pile.…”

Prospects of Applying MWD Technology for Quality Management of Drilling and Blasting Operations at Mining Enterprises

“…Prediction of fragment size distribution in the blasted rock mass based on MWD data is a relevant production problem. Modern methods of fragment size prediction, based on theoretical and empirical models [76,77] and even machine learning methods [78,79], use only D&B parameters and existing classifications of rock strength and structural irregularities, which sometimes fail to take into account the entire unique structure and heterogeneity of the rock mass to be blasted. However, if prediction of fragment size distribution in the muck pile utilized MWD data collected from each blasthole, and then combined them into a block model, taking into account rock strength variable characteristics and structural irregularities, it might be able to provide a better estimation of the blast results.…”