Assessment and Control of Ore Dilution in Long Hole Mining: Case Studies

Abstract

Unplanned ore dilution has a direct and large influence on the cost of a stope, and ultimately on the profitability of a mining operation. This paper presents the results of an examination of factors influencing ore dilution in a blasthole stoping environment. For the study, a comprehensive database was established, incorporating information related to the design, construction, excavation and cavity surveys of 172 sequentially mined long hole stope case histories from two orezones. Through a review of the case studies, it was demonstrated that, in addition to stope dimension, the amount of unplanned dilution differed according to stope type. Five stope types were identified, based on their position within a tabular blasthole mining sequence. Measured overbreak varies with stope type, with secondary stopes generating a greater volume of hanging-wall dilution than do primary stopes. Furthermore, a case study is presented to demonstrate the role cablebolts installed in the stope hanging-wall play to control ore dilution. The study illustrates relationships between measured hangingwall overbreak, cablebolt orientation and stope type.

Strategies for surface crown pillar design using numerical modelling – A case study

“…Similar trends were previously reported in the field by Henning & Mitri (2008); the measured 172 stope overbreak histories at the Bousquet #2 mine showed a positive correlation between stope volume and stope exposure time.…”

“…The overbreak that occurs in lower-level stopes extends the relaxation/unstressed zone, showing well-known stress shadow effect, which could enlarge the HW overbreak volume in the current level. Henning & Mitri (2008) analyzed HW dilution data measured by the Cavity Monitoring System (CMS) at the Bousquet #2 mine. It was found primary stopes above different heights of backfilled zones exhibit the different overbreak volume due to differing stress settings.…”

Strategies for surface crown pillar design using numerical modelling – A case study

“…Similar trends were previously reported in the field by Henning & Mitri (2008); the measured 172 stope overbreak histories at the Bousquet #2 mine showed a positive correlation between stope volume and stope exposure time.…”

“…The overbreak that occurs in lower-level stopes extends the relaxation/unstressed zone, showing well-known stress shadow effect, which could enlarge the HW overbreak volume in the current level. Henning & Mitri (2008) analyzed HW dilution data measured by the Cavity Monitoring System (CMS) at the Bousquet #2 mine. It was found primary stopes above different heights of backfilled zones exhibit the different overbreak volume due to differing stress settings.…”

Dilution prediction in underground open stope mining using gene expression programming and backpropagation artificial neural network algorithms

“…The stope's geotechnical properties, such as the rock quality designation (RQD), modified stability number ( N normal′), rockmass rating (RMR) and Q-rating (Q and Q′), which generally measure the quality of the rockmass, have been found to have an indirect correlation with dilution (Clark, 1998; Mathews et al, 1980; Potvin, 1989), with parameters such as rock Joint spacing (Js), Joint alteration (Ja), Joint water (Jw), Joint roughness (Jr) and stress reduction factor (SRF) moderating the rockmass classification according to the relationship expressed in equation (1) (Henning and Mitri, 2008; Hughes, 2011; Sutton, 1998; Urli, 2015): where J w / SRF represents the stress factor, in the absence of which, equation (1) then measures a moderated but still effective value of Q, known as Q-prime (Q′). Extant literature on dilution and overbreak in both mining and tunnelling has shown that rockmass with higher quality ( N normal′, Q, RQD) classification/rating are generally less susceptible to dilution and vice versa (Mathews et al, 1980; Papaioanou and Suorineni, 2016; Sutton, 1998; Suorineni, 2010).…”

“…Input assumptions such as mining dilution and recovery are essential in estimating the mining efficiency of planned mining blocks, called stopes. In particular, dilution accounts for the additional percentage of subeconomic or waste material mined in the course of extraction of the planned stope and is dependent, to a large extent, on rockmass quality, stope geometry and design, amongst other factors (Henning, 2007;Henning and Mitri, 2008;Mathews et al, 1980;Sutton, 1998). Figure 1 provides a simplistic overview of planned and unplanned dilution for a stope in open stope mining.…”

“…The stope's geotechnical properties, such as the rock quality designation (RQD), modified stability number (N ′ ), rockmass rating (RMR) and Q-rating (Q and Q ′ ), which generally measure the quality of the rockmass, have been found to have an indirect correlation with dilution (Clark, 1998;Mathews et al, 1980;Potvin, 1989), with parameters such as rock Joint spacing (Js), Joint alteration (Ja), Joint water (Jw), Joint roughness (Jr) and stress reduction factor (SRF) moderating the rockmass classification according to the relationship expressed in equation ( 1) (Henning and Mitri, 2008;Hughes, 2011;Sutton, 1998;Urli, 2015):…”

Strategies for surface crown pillar design using numerical modelling – A case study

“…Similar trends were previously reported in the field by Henning & Mitri (2008); the measured 172 stope overbreak histories at the Bousquet #2 mine showed a positive correlation between stope volume and stope exposure time.…”

“…The overbreak that occurs in lower-level stopes extends the relaxation/unstressed zone, showing well-known stress shadow effect, which could enlarge the HW overbreak volume in the current level. Henning & Mitri (2008) analyzed HW dilution data measured by the Cavity Monitoring System (CMS) at the Bousquet #2 mine. It was found primary stopes above different heights of backfilled zones exhibit the different overbreak volume due to differing stress settings.…”

Dilution prediction in underground open stope mining using gene expression programming and backpropagation artificial neural network algorithms

“…The stope's geotechnical properties, such as the rock quality designation (RQD), modified stability number ( N normal′), rockmass rating (RMR) and Q-rating (Q and Q′), which generally measure the quality of the rockmass, have been found to have an indirect correlation with dilution (Clark, 1998; Mathews et al, 1980; Potvin, 1989), with parameters such as rock Joint spacing (Js), Joint alteration (Ja), Joint water (Jw), Joint roughness (Jr) and stress reduction factor (SRF) moderating the rockmass classification according to the relationship expressed in equation (1) (Henning and Mitri, 2008; Hughes, 2011; Sutton, 1998; Urli, 2015): where J w / SRF represents the stress factor, in the absence of which, equation (1) then measures a moderated but still effective value of Q, known as Q-prime (Q′). Extant literature on dilution and overbreak in both mining and tunnelling has shown that rockmass with higher quality ( N normal′, Q, RQD) classification/rating are generally less susceptible to dilution and vice versa (Mathews et al, 1980; Papaioanou and Suorineni, 2016; Sutton, 1998; Suorineni, 2010).…”

“…Input assumptions such as mining dilution and recovery are essential in estimating the mining efficiency of planned mining blocks, called stopes. In particular, dilution accounts for the additional percentage of subeconomic or waste material mined in the course of extraction of the planned stope and is dependent, to a large extent, on rockmass quality, stope geometry and design, amongst other factors (Henning, 2007;Henning and Mitri, 2008;Mathews et al, 1980;Sutton, 1998). Figure 1 provides a simplistic overview of planned and unplanned dilution for a stope in open stope mining.…”

“…The stope's geotechnical properties, such as the rock quality designation (RQD), modified stability number (N ′ ), rockmass rating (RMR) and Q-rating (Q and Q ′ ), which generally measure the quality of the rockmass, have been found to have an indirect correlation with dilution (Clark, 1998;Mathews et al, 1980;Potvin, 1989), with parameters such as rock Joint spacing (Js), Joint alteration (Ja), Joint water (Jw), Joint roughness (Jr) and stress reduction factor (SRF) moderating the rockmass classification according to the relationship expressed in equation ( 1) (Henning and Mitri, 2008;Hughes, 2011;Sutton, 1998;Urli, 2015):…”

Strategies for surface crown pillar design using numerical modelling – A case study

“…Similar trends were previously reported in the field by Henning & Mitri (2008); the measured 172 stope overbreak histories at the Bousquet #2 mine showed a positive correlation between stope volume and stope exposure time.…”

“…The overbreak that occurs in lower-level stopes extends the relaxation/unstressed zone, showing well-known stress shadow effect, which could enlarge the HW overbreak volume in the current level. Henning & Mitri (2008) analyzed HW dilution data measured by the Cavity Monitoring System (CMS) at the Bousquet #2 mine. It was found primary stopes above different heights of backfilled zones exhibit the different overbreak volume due to differing stress settings.…”

Dilution prediction in underground open stope mining using gene expression programming and backpropagation artificial neural network algorithms

“…The stope's geotechnical properties, such as the rock quality designation (RQD), modified stability number ( N normal′), rockmass rating (RMR) and Q-rating (Q and Q′), which generally measure the quality of the rockmass, have been found to have an indirect correlation with dilution (Clark, 1998; Mathews et al, 1980; Potvin, 1989), with parameters such as rock Joint spacing (Js), Joint alteration (Ja), Joint water (Jw), Joint roughness (Jr) and stress reduction factor (SRF) moderating the rockmass classification according to the relationship expressed in equation (1) (Henning and Mitri, 2008; Hughes, 2011; Sutton, 1998; Urli, 2015): where J w / SRF represents the stress factor, in the absence of which, equation (1) then measures a moderated but still effective value of Q, known as Q-prime (Q′). Extant literature on dilution and overbreak in both mining and tunnelling has shown that rockmass with higher quality ( N normal′, Q, RQD) classification/rating are generally less susceptible to dilution and vice versa (Mathews et al, 1980; Papaioanou and Suorineni, 2016; Sutton, 1998; Suorineni, 2010).…”

“…Input assumptions such as mining dilution and recovery are essential in estimating the mining efficiency of planned mining blocks, called stopes. In particular, dilution accounts for the additional percentage of subeconomic or waste material mined in the course of extraction of the planned stope and is dependent, to a large extent, on rockmass quality, stope geometry and design, amongst other factors (Henning, 2007;Henning and Mitri, 2008;Mathews et al, 1980;Sutton, 1998). Figure 1 provides a simplistic overview of planned and unplanned dilution for a stope in open stope mining.…”

“…The stope's geotechnical properties, such as the rock quality designation (RQD), modified stability number (N ′ ), rockmass rating (RMR) and Q-rating (Q and Q ′ ), which generally measure the quality of the rockmass, have been found to have an indirect correlation with dilution (Clark, 1998;Mathews et al, 1980;Potvin, 1989), with parameters such as rock Joint spacing (Js), Joint alteration (Ja), Joint water (Jw), Joint roughness (Jr) and stress reduction factor (SRF) moderating the rockmass classification according to the relationship expressed in equation ( 1) (Henning and Mitri, 2008;Hughes, 2011;Sutton, 1998;Urli, 2015):…”