·The calibration of the mine to crusher model was based on rock fragmentation from the blasting phase through the primary crushing phase from an analysis of pictures of the fragmented pile A reduction in cost was determined for an optimum 90% passing size P90 The calibration was performed with technical and economic parameters from 2 years
·Backbreak massive rock fragmentation and high intensity ground vibration are all symptoms of improper blasting As a result production costs increase significantly while productivity decreases
The rock fragmentation in the pit was estimated as smaller sizes while that on the ROM pad was widely spread Figure 15 shows the characteristics of the estimated size distribution of the rock fragments of the muckpile in pit and rockpile on the ROM pad compared to the expected rock fragment size from the blast size requirement for the primary
·Blasting is essential in mining for rock fragmentation significantly impacting downstream processes overall productivity and the efficiency of resource extraction This study uses machine learning models including decision tree regressor random forest regressor support vector regressor and Extreme gradient boosting machine to predict the blast
The rock characteristics largely affect the energy needed for their fragmentation from blasting subsequent fragmentation of oversized blocks by hydraulic hammers to crushing in the process of stone refining The explosive energy during blasting is used up on fragmentation vibration seismic waves and forward movement of
·The degree of rock fragmentation influences the productivity of excavator and loader the throughput of crusher and mill the energy consumption of comminution equipment and the price of crushed end products such as the industrial minerals and the construction aggregates Sanchidrián et al 2012
·primary crusher for example 75% of itprimary crusher for example 75% of it HOW TO QUANTIFY THE FRAGMENTATION The oversize content is a very good complement to the k50 approach for the prediction of rock fragmentation by blasting The unique feature of this model is that the input
·The optimization of blasting operations greatly benefits from the prediction of rock fragmentation The main factors that affect fragmentation are rock mass characteristics blast geometry and explosive properties This paper is a step towards the implementation of machine learning and deep learning algorithms for predicting the extent of fragmentation in percentage
·Regarding such a situation this article describes the effects of rock fragmentation by blasting on the energy consumption productivity minerals recovery operational costs in the whole size reduction chain from mining to mineral processing and the sustainability of mining industry a coarser fragmentation a smaller crusher flow and
·Digital image processing method was used for the assessment of the particle size distribution of blasted rock The fragmentation results then were paired with the crusher throughput values and
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