Junhyeok Park,Kwangmin Kim

Department of Mining and Geological Engineering,University of Arizona,Tucson,AZ 85721,USA

Keywords:Monitoring while drilling (MWD)Mine-to-mill (MTM)optimization Rock-breakage efficiency Drilling penetration rates

ABSTRACT In a hard-rock mine,blasting is an important rock-breakage process that impacts energy consumption both in downstream comminution processes and mine productivity.Optimizing the blast fragmentation to improve rock-breakage efficiencies during crushing and grinding is key to mine-to-mill (MTM)optimization.This study explores the use of monitoring while drilling (MWD)data to achieve this goal.Representative penetration rates(PRs)were extracted from blastholes to estimate intact rock properties and predict the breakage efficiencies that directly affect comminution energy consumption.Two intact rock properties,tensile strength(TS)and Bond work index(BWI),were correlated with the PR data to predict these efficiencies in crushing and grinding,respectively.Because of the complexity of the raw MWD data and effects of various disturbances,the MWD data was preprocessed and normalized to achieve a representative PR value at each blasthole.This preprocessing entailed defining valid PR ranges from the MWD data that could eliminate the noise related to discontinuity features in the rock mass structure as well as errors in operator behaviors.The PR data was also normalized using the adjusted penetration rate (APR)to minimize the effects of mechanical factors such as drill feed force,torque,and rotational speed.To correlate the representative APR value with intact rock properties,TS and BWI,various laboratory experiments were conducted:drilling tests using a high-precision coring machine,Brazilian disc tests,and Bond grindability tests.Based on the results of these experiments,models were developed to predict rock-breakage efficiencies during crushing and grinding based on APR.The result of this study can be used to obtain blast energy designs that consider comminution energy consumption and efficiency in the downstream rock-breakage processes.

1.Introduction

Crushing and grinding are some of the most energy-intensive industrial processes in the world.In mines,rock-breakage (comminution)processes comprise a major portion-about 30%-60%-of the total energy consumption [1-5].These processes impact not only energy consumption but also mine productivity.Over the last few decades,efforts have been made to improve the comminution efficiency at hard-rock mines,and one of the most popular of these approaches is mine-to-mill (MTM)optimization,which was first introduced by Julius Kruttschnitt Mineral Research Centre.The ultimate goal of MTM is to improve the energy efficiency of rock breakage by optimizing blast fragmentation.Blasting generates microcracks that reduce the required comminution energy in crushing and grinding,resulting in productivity increases [6,7].

MTM research falls under two main categories:determining the optimal(target)blast fragmentation and controlling the blast fragmentation.This study falls under the former category and considers the effect on downstream comminution processes,mainly crushing and grinding,to optimize the blast fragmentation.

The MTM process for optimizing the blast fragmentation(Fig.1)is complex and must consider rock mass characteristics,the blast energy,and downstream comminution processes.Rock mass characteristics are especially important but challenging to determine in real time since acquiring this information is labor-intensive and time-consuming;these characteristics are typically measured directly from laboratory and field tests.Furthermore,approaches for applying rock mass characteristics in blast energy design are limited;in practice,it is almost impossible to represent the entire rock mass in real time given the scale and frequency of blasting and the highly varied rock properties typically found at hardrock mines.In a porphyry copper deposit,for example,rock properties usually vary dramatically over even small domains because of the complexity of the alteration zone and its interaction with hydrothermal fluid [8].In addition,access to the muckpile or pit highwall to obtain samples may prompt safety concerns.

Fig.1.Concept of MTM.

Drilling performance data can provide a viable means of characterizing the rock mass for MTM.The use of blasthole drilling data is advantageous since it is acquired systematically,routinely,and in real time [9].In the mining industry,monitoring while drilling(MWD)is a standard practice for obtaining penetration rate (PR),rotational speed,flushing pressure,and GPS-based drillhole positioning data [10].Various data logging systems are commercially available for recording this information,including terrain,drilling efficiency indicator(DEI)TM,pro-visionTM,and the rig control system(RCS)TM.Many researchers have tried using drilling performance data to characterize rock properties and rock mass conditions[11-13].Segui and Higgins identified the rock hardness from MWD data for use in a blast design [10].Ghosh et al.developed a contour map of rock mass strength based on the measured MWD data in the Aitik mine[14].Chen and Yue applied MWD data to understand rock weathering grades[15].Hatherly et al.verified the validity of MWD data for classifying rock types by comparing it with geophysical logging data from a drillhole probe [16].And MWD data was also used to detect geological boundaries such as coal seams [17,18].Recently,machine learning technology has been applied to classify rock types using MWD data[19-21].However,the current MWD technologies have still not been fully developed to accurately delineate and understand rock mass conditions,especially for MTM.

MWD data includes two main types of information about the rock mass condition:its intact rock properties and its structural features (discontinuities).This study considers each separately and focuses on intact rock properties,which can provide a measure of the rock-breakage efficiency during the crushing and grinding processes and,ultimately,of the target blast fragmentation.This approach could simplify and reduce the high level of uncertainty associated with the field data.Various state-of-theart remote sensing technologies are available for observing structural features and can be used with the MWD system described below to investigate rock mass conditions in a mine.These technologies include LiDAR and photogrammetry instrumentation that can be mounted on unmanned aerial vehicles (UAVs),as shown in Fig.2.

This study reasonably assumes that intact rock properties control the breakage efficiencies in the crushing and grinding processes.For blast fragmentation control,rock mass structures must be considered in addition to intact rock properties [9].However,once the rock is blasted,its breakage efficiency depends mainly on its intact properties since the particle sizes in the crushing and grinding processes are much smaller than the typical block size in rock mass [22].Two parameters can be used to represent intact rock properties for predicting the comminution efficiency:tensile strength (TS)and Bond work index (BWI).TS is fundamental to the crack-opening stages of crushing and BWI is an intrinsic material characteristic for the fine grinding process [23,24].These two parameters are inversely proportional to the energy efficiencies for crushing and grinding;higher TS and BWI require more energy,which results in lower breakage efficiencies.

2.Preprocessing of drilling performance (MWD)data

MWD systems record the real-time drilling performance data using sensors.Field MWD data usually includes some errors and inconsistent PR data that may originate from several sources:the drilling equipment itself,the media (due to geological variations),and operator behaviors.For example,PR fluctuates when the drill bit contacts discontinuities such as faults,joints,fractures,and bedding planes,and operators may stop drilling to deal with unexpected situations such as rod or shift changes.Given the frequency and scale of blasting in hard-rock mines,a significant amount of MWD data must be updated daily.Therefore,the errors and inconsistencies in the PR data have a significant effect on the ability to achieve optimum blast fragmentation.Consequently,it is important to simplify the PR data,minimize these errors,and obtain a consistent PR within a rock type to use drilling performance as a measure of intact rock properties for MTM.

Fig.2.Various sensing technologies and MWD systems for rock characterization and MTM in a hard-rock mine.

Fig.3 shows the analysis of approximately 986,000 PR measurements at a hard-rock mine obtained using a Pit Viper RCS system.The data distribution was positively skewed,meaning that the dataset contained a large amount of exceptional values;therefore,simple averaging could produce misleading results.To simplify the PR data and minimize errors,values higher than 1 m/min (about 5.4% of total population)were considered outliers and treated as exceptional and invalid since including them would result in unreasonably high PR averages.For example,the average of the collected PR data was 0.4 m/min,but the median was almost half,0.22 m/min,and the median was still much higher than the most frequent value (mode),0.14 m/min.These values illustrate the complexity of conducting statistical analyses for MWD data.It is notable that the 1 m/min threshold was based on the overall range of PR data in the mine as well as interviews with drill operators;consequently,this value is site specific and can vary in each mine.

2.1.Defining a valid PR range

Fig.3.Histogram of PR data from field measurement.

Fig.4.Example of valid data points from raw PR data.

To address these issues,a valid PR range was defined to represent intact rock properties.Data was collected from a single blasthole (around 15 m deep)in a hard-rock mine,as shown in Fig.4.Even though the geological domain was relatively consistent throughout the borehole,some data points did attain exceptional PR values(over 2 m/min)and fluctuated widely due to operational errors and geological features.For example,the wide range of PR values observed in the first 5 min was not considered valid.In addition to operational errors,these values may represent structural features such as voids and fractures as well as mixed intact rock properties.Since the rock type was consistent throughout the entire hole,it was reasonably assumed that the PR range could represent the intact properties for it was relatively consistent,steady,and below the 1 m/min threshold (Fig.4).

2.2.Normalization using APR

Although a PR range can be defined to minimize the disturbances related to geological variations and operator behaviors,it does not apply to mechanical factors such as feed force,torque,and rotational speed.To estimate intact rock properties using drilling performance for MTM,the dependency of raw PR on mechanical factors must also be minimized.Many researchers have tried to find a function to obtain a representative index for estimating geological conditions from raw PR data;however,their results are challengeable because the theoretical approach to describing drill-rock interactions is limited to account for external disturbances [9].Two popular approaches have been used to infer the rock strength from MWD data:applying the specific energy of drilling (SED)based on empirical analysis and applying the APR for data normalization.

The SED for rotary non-percussion drilling is defined as the work done to eliminate a unit volume of rock[25].It can be calculated as:

where SED is the specific energy of drilling;D the diameter of the drill;Fnthe feed force;ω the rotational speed;τ the torque;and v the penetration rate.

SED is the most popular approach due to its simplicity;it infers the rock strength index simply by adding the measured drilling parameters into Eq.(1)[26].However,SED is based on empirical analysis and has no normalization process [9].Consequently,it has a limited ability to eliminate the effect of the mechanical factors and limited applicability for measuring intact rock properties for MTM.For a rotary drill,the APR was suggested to be applied in this study [20].The effect of the mechanical factors was minimized by normalizing the PR data using APR,as calculated by Eq.(2)[18].It is notable that,although this study focuses on a rotary drilling,this approach could be applied to other drilling systems,pending further studies.

where the coefficient α is determined by reference values,which are represented by the subscript ‘‘r”.

The reference values consider the rock type,rock strength,drilling method,and operational conditions.To determine these values,the maximum change ranges must first be defined for three drill variables-feed force,rotational speed,and torque-while considering the possible operational conditions in a mine.The most representative (median)values can then be assigned as reference values using the results of baseline tests that entail applying the three controllable drill variables within the defined ranges.In this study,an electric motor drill was used.Since the drill’s power output is constant and the rotational speed and torque of its motor are inversely proportional,the variable for torque can be ignored in Eq.(2).However,in a drill powered by an internal combustion engine(the standard in mines),feed force,rotational speed,and torque respond independently,and torque (τ)varies depending on the revolutions per minute (RPM)and power of the engine [27].The reference values in this study were defined as 430 RPM(rotational speed)and 758 KPa (feed force).It is notable that the same values were used for sandstone and limestone since the difference in strength between these rock types was insignificant in this study and the tests were conducted in well-controlled environments using an electric drill.

Fig.5.Modified GCTS pressure-controlled coring machine (RCD-250)for a drilling test.

The APR was calculated using Eqs.(2)and (3)with these defined reference values.For example,if a PR is measured with 420 RPM and 800 KPa of feed force,the denominator becomesThe value of αcan be expressed asand divided by denominator.As mentioned above,torque (τ)is constant since an electric motor drill was used.With torque (τ)canceled,the APR turns out to be about 96% of the PR value.

A modified GCTS pressure-controlled coring machine(RCD-250)was used for the drilling tests with 24.5 mm core bits (9 mm diamond segment height,3 mm segment thickness,and 102 mm overall height).The drilling depth,rotational speed,and feed force of the bit were measured using a real-time multi-channel data logger.Typical drill progress ranges were between 25 and 40 mm,depending on the tested sample lengths available.Fig.5 shows the coring machine and set of sensors for the laboratory-scale MWD test.

The disturbances generated from the mechanical factors were successfully eliminated(normalized)using the APR,and consistent PR data was obtained.Fig.6 shows the results of normalizing the laboratory drill data:the raw PR data (Fig.6a),which were highly dependent on feed force and rotational speed changes,and the PR data after applying APR (Fig.6b).

Fig.7 shows histograms constructed using PR and APR data that falls within the valid range to compare their overall distributions.Although the trends for the two histograms do not differ significantly,the APR data is more distributed and less centralized than the PR data.

The results shown in Figs.6 and 7 are based on laboratory PR test data;applying them to field conditions is premature.Field applications will require further studies-in particular,studies featuring drills that use internal combustion engines.For example,the APR equation (Eq.(2))must consider a specific torque curve that can be provided by equipment manufacturers.

3.APR and intact rock properties

Processed MWD data can be used to predict the rock-breakage efficiencies in crushing and grinding processes based on the relationship between drilling performance(APR)and intact rock properties.A series of laboratory rock tests-drilling tests using a highprecision coring machine,tensile tests,and BWI tests-were conducted to find the relationship between drilling performance and intact rock properties.As previously noted,this study assumed that TS and BWI could represent the resistance to fracturing and abrasion during the comminution;TS can be considered as a fundamental property for the rock breakage in crushing and BWI is the most popular measure of the energy required in fine-grinding[27].

Fig.6.Results of normalizing the laboratory drill data.

Fig.7.Comparisons of distributions of PR and APR data that falls within the valid range.

Fig.8.Rock samples and laboratory experiment procedures.

In the laboratory tests,the tested rock types included Coconino sandstone as well as limestone from a quarry in northern Arizona.A total of 141 samples(80 for the sandstone,61 for the limestone)was used for the tests.The NX core (54.7 mm diameter)with 100 mm of height was divided into two parts:25 mm for Brazilian disc tests(to measure TS)and 75 mm for drilling tests(to measure APR).Fragments of the tested samples were then collected,crushed using a laboratory-scale jaw crusher,and used for the grindability tests to determine the BWI.It is notable that fewer Bond grindability tests were conducted because they require a larger amount of feed samples(over 5 kg)than the Brazilian disc and drilling tests.Fig.8 shows the tested rock samples and the procedure for the laboratory experiments.

3.1.Grindability tests

Bond ball mill tests were conducted to measure the grindability of rock.BWI is an intrinsic characteristic of rock grindability,representing the specific energy for grinding rock when the feed size is 6 mesh and the product size is 100 mesh.This study used a laboratory Bond ball mill consisting of a cylindrical steel jar and a 70 RPM motor to derive BWI.Fig.9 shows the mill.

To measure BWI,the standard Bond grindability test procedure was followed,as shown in Fig.10 [28,29].This test requires feed material(rock particles)of less than 6 mesh(3350 μm).These particles were obtained by crushing the aftermath fragments from the drilling and Brazilian disc tests with the laboratory jaw crusher.

Each tumbling procedure was repeated until the material flow reached the steady state of the circulation load,which was calculated by using the ratio of the weight of oversize to undersize fragments based on the 100 mesh (150 μm)sieve opening.Once the circulation load remained at 250% for three tumbling periods,it was regarded as reaching a steady state.

Based on test results,BWI was calculated using Eq.(4).This calculation used product/feed particle sizes along with the average net grams produced per revolution(Gpr)measured during the last three periods after reaching a steady state.P80and F80were obtained from the Gaudin-Schumann distribution curve plotted by sieving tests using various mesh sizes.

Fig.9.Laboratory Bond ball mill.

Fig.10.Procedure of the Bond standard grindability test.

where BWI is the Bond work index;Pithe opening sieve size;Gpr the grams produced per revolution;P8080%passing size of product;and F8080% passing size of feed.

For limestone samples,Eq.(4)was applicable,but for the sandstone samples,a different approach was used to calculate BWI.Since sandstone is a typical clastic sedimentary rock consisting of 100-200 μm silicate particles,the cohesion between particles is much weaker than their strength.This characteristic caused an inconsistent phase of the grinding speed in the tumbling mill.The particle size quickly reduced to 100-200 μm,but additional grinding at finer sizes produced almost no change due to the high hardness of the silicate.Therefore,it was difficult to achieve a steady state for calculating Gpr.As an alternative,the Berry and Bruce comparative method was used to estimate the BWI [30].The applicability of this method has been verified to measure the BWI values of graphite and columbite ores,respectively,and showed that their values were within the range of the actual BWI values of the ores [31,32].Likewise,it was found that the BWI estimates from this comparative method were within 8% of the actual BWI [33].Given the purpose of this study-identifying the relative changes of BWI in accordance with the blasthole APR changes and predicting/improving the rock-breakage efficiencythis method was acceptable.Although it does not yield an actual BWI,it provides consistent test results,which is key to this study.

Table 1 Information for tested rock samples and experiment results.

The BWIs of sandstones were approximated using Eq.(5):where r and t refers to the referenced and tested material,respectively.

The sandstone sample that was chosen as a reference material(sandstone No.1 in Table 1)had a more cohesive structure than other specimens and did not exhibit the general propensity of sandstone as described above.

The average BWIs for sandstone and limestone were 5.84±1.51 and 6.45±0.61 kWh/t,respectively.The measured BWIs were still considered acceptable given the purpose of this study and the variability of rock,although the BWIs were lower than the general ranges for sandstone (11-38 kWh/t)and limestone (4-26 kWh/t)[34].

Table 1 shows details for the tested rock samples as well as the experiment results.The Leeb hardness(LH)index was measured to investigate the basic strength of the rock samples using the method proposed by Aoki and Matsukura [35].The unconfined compressive strength (UCS)was estimated by converting the LH index[36].Because the number of rock samples was limited,other basic mechanical properties for the rock types were not evaluated in this study,and only one BWI measurement was conducted for each test set.

3.2.Prediction models

Models were established to predict rock-breakage efficiencies using nonlinear regression analysis.BWI and TS can be predicted using the measured APR.Previous studies have shown that the relationship between PR and the rock strength commonly follows a power or exponential function [37-40].The test results in this study followed a power function(Y=aXk)that can be expressed as a linear function in a log-log domain.Applying logarithmic transformations in a regression model is a common way to handle a nonlinear relationship between two variables,especially when they are highly skewed like the field MWD data,as shown in Fig.3.

Fig.11 shows the test results for the 80 sandstone samples.The model plots these results with a 95% confidence interval (CI)and prediction interval (PI)lines.It is notable that the CI indicates the 95%probability range in which a current observation lies,while PI indicates the range in which a future observation will fall with 95% probability.

The upper and lower PI boundaries were defined for the prediction model and the relationship between APR and TS,with the nonlinear plot(Fig.11a)and the log-log plot(Fig.11b).The prediction model for TS was generated using Eq.(6):

where APR is the adjusted penetration rate;and TS the tensile strength.

For APR values lower than 5 cm/min,the corresponding TS increased exponentially,indicating that the energy required for crushing is exponentially higher in this range(Fig.11a).For example,when APR was 5 cm/min,the predicted TS was 8.8 MPa.When APR decreased from 8 to 5 cm/min (a change of 3 cm/min),the TS increase was only 2.6 MPa;however,when it decreased from 5 to 2 cm/min (the same change),TS increased by 8.5 MPa.Consequently,if the measured APR in the field decreased,more energy will be required in the crushing process after blasting,but the required blast design to improve the rock-breakage efficiency will differ significantly depending on the APR range.APRs that are lower than 5 cm/min will require more blasting energy if they decrease based on the relationship shown in Fig.11.

Fig.12 plots BWI as a function of APR for sandstone.Data points were limited because of a shortage in feed materials (sandstone samples).Since BWI experiments require a relatively large amount of samples,more tests are required to generate the prediction model;however,this analysis did show that BWI relied significantly on APR changes.

Based on the results shown in Fig.12,a preliminary regression(prediction)model of APR versus BWI was generated for the sandstone,as shown in Eq.(7):

For APRs lower than around 7 cm/min,the corresponding BWI increased exponentially with decreasing APR (harder rock for grinding)(Fig.12a).

Figs.13 and 14 show the test results for the limestone samples,which vary less than the sandstone results.This difference could be related to the sample consistency(the limestone was sampled at a single quarry).More studies are required to determine the cause of this difference.

Figs.13 and 14 showed a clear dependency of TS and BWI on APR changes,but the data points were dispersed and too few in number to develop reliable prediction models.Since the goal of this study is to demonstrate the use of PR data in mining applications,preliminary regression models of APR versus TS/BWI were generated for limestone as shown in Eqs.(8)and (9).

Fig.11.Regression model of sandstone for APR versus TS.

Fig.12.Regression model of sandstone for APR versus BWI.

Fig.13.Regression model of limestone for APR versus TS.

Fig.14.Regression model of limestone for APR versus BWI.

For APRs below around 4 cm/min,the corresponding TS and BWI increased exponentially with decreasing APR (Figs.13a and 14a).

This analysis found that rock-breakage efficiencies(TS and BWI)could be predicted using APR and that the prediction models followed a power function (Y=aXk).Because the prediction model was both site-type-specific and rock-type-specific,it was possible to minimize the variation that may occur even within a single rock type in a mine.Therefore,the same specificity applies to the coefficients of the power function(‘‘a”and‘‘k”).The coefficients can be derived from laboratory tests initially and later updated to minimize the discrepancy between the predicted and monitored values based on the data obtained during blasting,crushing and grinding.Through this iterative process,reliable prediction models can eventually be developed for a mine.

4.Potential field applications

For practical application in the field,the results of this analysis need to be simplified and presented in a way that is useful for mine operators.This could be accomplished by developing thematic maps for various blasthole classifications (e.g.‘‘soft”,‘‘medium”,and ‘‘hard”)that could serve as a guide before blasting,especially in the absence of geological surveys.

This would entail developing a real-time thematic map that shows rock-breakage characteristics(crushability and grindability)based on representative APR values (MWD data)and prediction models.Such a map could be created by integrating MWD data into mine planning software such as MineSightTM,SurpacTM,VulcanTM,etc.;the data could be simply integrated into the existing blasthole database,which allows users to store,manage and visualize data.Fig.15 describes this concept.It is notable that applying this method in the field still poses many challenges and requires further study;the purpose of this section is to illustrate the concept of the potential applications in mining industry.

APR map displaying the representative APR value at each blasthole can be generated,as shown on the left side of Fig.15.Although each blasthole has a representative APR value,in practice,it is almost impossible for mine operators to apply different blast energies at each hole [10].Therefore,this map could be simplified by classifying blastholes-for example,as ‘‘soft”,‘‘medium”,and ‘‘hard”.It is notable that this classification refers to the rockbreakage efficiency such as crushability and grindability based on TS and BWI-not rock hardness.For example,two thresholds,5 and 10 cm/min,could be defined in Fig.11,which show the relationship between APR and TS.Blastholes having APR values that are lower than 5 cm/min could be classified as ‘‘hard”,between 5 and 10 cm/min as‘‘medium”,and higher than 10 cm/min as‘‘soft”.At each classification,a blast energy design could be assigned to improve the rock-breakage efficiency in the crushing process.Two simplified thematic maps could be generated based on the prediction models,as shown in Fig.15:a TS map for presenting the comminution efficiency in the crushing process and a BWI map for presenting the comminution efficiency in the grinding process.

Fig.15.Thematic mapping using drilling performance.

An optimal blast energy design requires both determining the target fragmentation and controlling the blast fragmentation.This study focused only on the target blast fragmentation,which must consider downstream comminution processes(crushing and grinding).Therefore,the use of thematic maps as suggested above is limited to identifying the target fragmentation;harder rock for crushing and grinding demands better (smaller)blast fragmentation,which in turn requires higher blast energy to improve the rock-breakage efficiency and reduce the downstream comminution energy.Blast fragmentation control,on the other hand,must consider factors such as rock mass strength and structural features that were not part of this study.Further studies are required to finalize the optimal blast energy(powder factor)design for a given rock mass.

5.Conclusions and future work

This study showed the potential for using drilling performance data to estimate rock-breakage characteristics in crushing and grinding as the part MTM efforts.A valid PR range was defined to minimize the effects of geological factors and represent intact rock properties-TS and BWI-from blasthole PR data.Likewise,the APR concept was applied to minimize the effect of mechanical factors by normalizing the PR data.The results of laboratory tests with sandstone and limestone samples indicated that APR could be correlated with TS and BWI,which were directly related to rockbreakage efficiencies in the crushing and grinding processes,respectively.Site-type-specific and rock-type-specific models were then generated to predict rock-breakage efficiencies with a representative APR at each blasthole.In addition,a methodology was proposed for field applications that involves creating thematic maps (possibly using mine planning software)to guide operators in allocating the proper blast energy design at blastholes.To create these maps,blastholes would be classified based on their rockbreakage characteristics (e.g.‘‘soft”,‘‘medium”,or ‘‘hard”).This study demonstrated how to extract intact rock properties for predicting rock-breakage characteristics in the crushing and grinding processes from drilling performance (MWD)data.It also showed how to develop prediction models for estimating rock-breakage characteristics using APR values from laboratory drilling,Brazilian disc,and Bond grindability tests.

Although raw MWD field data were used to define the valid PR ranges for extracting the intact rock properties,the prediction models were generated based on the results of laboratory tests only.Some challenges must be overcome before this methodology can be applied in the field.One such challenge is to understand the heterogeneous nature of the rock mass and effectively extract the intact rock properties.Although this study extracted valid PR ranges and obtained APR values via simple algorithms,this was achieved under fully controlled conditions in the laboratory;actual field data may require much more sophisticated and complex statistical methods.Another challenge is to correlate the field and laboratory APR values.Obtaining representative APR values from raw field MWD data must account for various conditions such as significant interactions between the drill bit and rock,friction between the drill rods and bore wall,the use of drilling fluid,and bit wear[16].Therefore,developing a reliable correlation between laboratory APR and field APR currently involves a significant uncertainty.Further follow-up studies will be conducted to achieve reliable field APR values,site-specific prediction models and,ultimately,site-specific target blast fragmentation that considers the downstream comminution processes in a hard-rock mine.