Trial application of air decks on a production shot improved fragmentation (32% reduction in x50 ). Empirical fragmentation models and digital image processing software were used to estimate fragment size distribution of the blasted muck pile. The x20 , x50 , and x80 passing fractions, and top size were 42, 265, 683, and 1,455 mm, respectively. Air-deck application reduced explosives load in blast - holes, lowering total charging cost by US$20/hole (15% decrease) and the powder factor to an average of 0.86 kg/m 3 (12% decrease). Blast results were 443,624 t of blasted material from the block (90% of total muck pile) was smaller than 900 mm. IKEYWORDS Air deck, Blasting, Fragmentation models, Gasbag, Stemming zone, Uniformity factor RÉSUMÉ L’application pilote de trous d’air dans le cadre du coup de mine effectué pour commencer la production a amélioré la fragmentation (une réduction de 32 % des x50 ). Des modèles empiriques de frag - mentation et un logiciel de traitement numérique des images ont permis d’estimer la composition granu - lométrique des fragments du tas de déblais abattus. Les fractions de passé de crible de x20 , x50 , x80 et la dimension supérieure étaient représentées par des dimensions respectives de 42, 265, 683 et 1 455 mm. L’application de trous d’air a permis de diminuer la charge d’explosifs nécessaires dans les trous de mine, réduisant ainsi les coûts totaux de chargement de 20 $ US/trou (soit une réduction de 15 %) ainsi que le facteur de chargement (facteur poudre) à une moyenne de 0,86 kg/m 3 (soit une réduction de 12 %). Les coups de mine ont résulté en 443 624 tonnes de matériaux abattus du bloc (soit 90 % du tas de rem - blais total) mesurant moins de 900 mm. I MOTS CLÉS abattage à l’explosif, ballon à gaz, facteur d’uniformité, modèles de fragmentation, trou d’air, zone de bourrage CIM Journal | V ol. 9, no. 1, 2018 52 E. Kabwe and W. Banda To maintain desirable fragment sizes, air-deck blasting has been tested and applied. Air decking is a technique that creates an air gap in a blasthole. Air gaps can be positioned at the bottom (bottom-column), middle (mid-column), or top (top-column) of the charge column. Initially, air decks were used in surface blasting as a means of distributing the explosives energy more evenly throughout the rock mass that was being blasted (Sazid & Singh, 2013). Today, the application of air decks is being used successfully to allow reductions from 10 to 30% of the total explosives required for production blasting. other applications of air decking include wall control and presplitting. In all these cases, air decking can result in significant savings and improved effi - ciencies over conventional methods (Sharma, 2010). In this study, a blasting trial was conducted by using a top-column air deck coupled with gasbags to investigate its influence on stemming zone fragmentation and reductions in the quantities and costs of stemming material, powder factor, and other blasting variables. Further, a fragmentation anal - ysis was carried out using proposed empirical fragmenta - tion models and Split-Desktop ©(Split Engineering, 2001), a digital image processing software package. Theory of air decking When an explosive charge in a blasthole is detonated, the blasthole expands due to high pressure, crushing the borehole walls. A stress wave with high peak pressure prop - agates outward in all directions in the form of a compres - sive stress wave. According to Melnikov and Marchenko (1971), an air deck between sections of an explosive col - umn results in an excitation of secondary waves that diffuse into the previously compressed rock. The charge structure ensures multiple impacts of stress waves into the ambient medium and, at the same time, changes the nature of energy transfer to the strained rock mass, which leads to an increase of the effective explosion energy in the medium. once an air deck is posi - tioned in the explosive column, it was also established that the peak borehole pressure decreases due to wave collision in the air gap. This might result in more energy trans - ferred to the medium compared to blasting a continuous charge; hence, improved rock fragmentation can be achieved by employing an air gap in the explosive column of a blasthole. Marchenko (1982) concluded that the application of air decks substan - tially reduced the stresses in the air- deck zone as well as the crushing zone around the explosive charge, improving the degree of fragmenta - tion. According to Moxon et al. (1993), no significant effect on the degree of fragmentation was observed with the air decks, which occupied 40% or less of the maximum volume of explosive. Jhanwar and Jethwa (2000) determined that air-deck blasting results in better fragmentation and improved use of explosive energy. Jhanwar et al. (2000) determined that the degree of frag - mentation resulting from air-deck blasting is better than that of conventional blasting. Air-deck blasting was also established to be more effective in very low to low- strength, moderately jointed rocks compared to medium- strength, highly jointed rocks. Blastholes with air decks consumed less explosives than those with a conventional charge. The types of air decks normally used are bottom-col - umn, mid-column, and top-column. Figure 1a shows the top-column air deck design using a gasbag as a stemming enhancer. Jhanwar and Jethwa (2000) alleged that a bot - tom-column air deck can only be used to blast holes in soft and medium-strength rocks, concluding that when an air deck is employed at the top of the explosive column, it gen - erates an improved degree of rock fragmentation in the stemming zone. Jhanwar (2011) proposed that air decks work best if positioned in the middle of an explosive col - umn for general application, and at the top column for ade - quate breakage in the collar zone. He also pointed out that an air deck induces a change in the mechanism of explosive energy transfer to the rock mass. The energy transfer takes place in a series of pulses, which increases the duration over which explosive energy acts over the rock mass. This shows that air-deck length is critical to fragmentation. Moxon et al. (1993) concluded that the middle position of the air deck improved the degree of rock fragmentation due to the interaction of two concurrent stress-wave fronts from the top and bottom of an explosive charge; however, Chiap - Figure 1. a) Schematic of a top-column air deck (AnFo: ammonium nitrate, fuel oil) and b) a gasbag mechanism used as a stemming enhancer, which releases energy in pulses (shown by orange arrows) a) b) petta (2004) stated that the bottom-column air deck could be used more effectively because it produces more pressure at the bottom of the hole. After performing a series of numerical simulations to investigate the physical processes and effects in rock blasting with air-decked explosive charges, Liu and Katsabanis (1996) concluded the best blasting results were obtained with the longest air deck, provided the shortened stemming could confine the explo - sion gas in the blasthole. Air-gap and gasbag employment reduces the amount of explosives per hole, thus decreasing explosive costs up to 30%. Top-column air decks are the most suitable where improved stemming region fragmentation is required. gas - bags serve as stemming enhancers, where they lock and prevent energy and gasses from escaping, and they then release the energy in separate pulses (Figure 1b). Fragmentation models A number of researchers proposed empirical models to estimate fragmentation size distribution. The models use equations to calculate the average fragment size ( x50 ) and fragment size distribution curves. They use input parame - ters such as the weight strength of explosives, geometric blast design features, and rock properties. Explosive prop - erties and blast geometry features are easy to obtain and can be accurate; however, rock properties are difficult to estab - lish—in most cases, assumptions must be made. This reduces the model’s ability to accurately produce the frag - ment size distribution curve (Demenegas, 2008). In this study, three models will be used to determine the fragmen - tation distribution and results will be compared. A brief description of each model is given in this section. In 1973, Larsson proposed equation 1 to predict the mean particle size of fragmentation (cited in Bakhtavar et al., 2015): x50 = sʹ × e(0.58 × lnB −0.145 × ln (S/B ) −1.18 × ln (q/c )−0.8 2) (1) where x50 is the mean particle size (in cm), sʹis the blasta - bility constant, Bis the burden (in m), S is the spacing (in m), qis the specific charge (in kg/m 3), and c is the rock constant. Hjelmberg (1983) proposed the SveDeFo model as an upgrade of Larsson’s model. Additional to the parameters in equation 1, bench height and stemming length were con - sidered for the mean fragment size prediction: x50 = sʹ [1 + 4.67( T/L )2.5 ] e(0.29 × lnB 2(≈–––– –S/1.25) –1.18 × ln (q/c )−0.8 2) (2) where Tis the stemming length (in m) and Lis the hole length (in m). Cunningham (1983) proposed a simple and effective model for estimating fragmentation results, based on the expressions constructed by Kuznetsov (1973) and rosin and rammler (1933). It provides a method for calculating the mean fragment size and rock uniformity. Kuznetsov (1973) suggested the following empirical equation to predict the mean fragmentation size resulting from rock blasting: 1 19 – –– 11 5 xm= AK –0.8 Q6[–––– ]30 (3) RWS where xmis the mean fragment size (in cm), Ais the rock factor (between 0.8 and 22, depending on rock hardness and structure; its derivation is given in equation 4), Kis the powder factor (in kg/m 3), Qis the mass explosive in the hole (in kg), and RWS is the explosive weight strength rel - ative to ammonium nitrate, fuel oil (AnFo ;in %). The relation between the rock factor ( A) and blastability index (BI ) proposed by Lilly (1986) can be obtained from equa - tion 4, where BI is determined from equation 5: A = 0.06 × BI (4) BI = 0.5 × ( RMD + JPS + JPO + SGI + HD ) (5) where RMD is the rock-mass description, JPS is the joint plane spacing, JPO is the joint plane orientation, SGI is the specific gravity influence (in t/m 3), and HD is the hardness factor. Inserting the blastability index, the model is pre - sented as 1 19 – 115 – – xm= 0.06 × BI × K(−0.8) Q(6)[––– ––– RWS ](30) (6) The rosin-rammler (rosin & rammler, 1933) equation is then used to predict the fragment size distribution result - ing from rock blasting: x n P (x1) = exp [−0.693 (––– )] (7) xc where P(x1) is the proportion of the material larger than x, xm= ( x50 ) is the characteristic size (in cm), and nis the uni - formity index. The final equation of the Kuz-ram model is an equation for the uniformity index: __________ B SD S |Lb– Lc| 0.1 Ltot n= (2.2 − 14 –– )( 1− ––– )≈0.5 (1+ –– )[–––––– + 0.1 ](––– ) (8) d B B Ltot H where Bis the burden (in m), Sis the spacing (in m), dis the borehole diameter (in mm), Lb is the bottom charge length (in m), Lcis the column charge length (in m), Ltot is the total charge length (in m), His the bench height (in m), and SD is the standard deviation of drilling precision (in m). The SD value is determined from equation 9 and drilling accuracy: CIM Journal | V ol. 9, no. 1, 2018 Stemming zone fragmentation analysis of optimized blasting with top-column air decks 53 CIM Journal | V ol. 9, no. 1, 2018 54 E. Kabwe and W. Banda 100 − drilling accuracy SD = –––––––––––––––––––– × S (9) 100 The nvalue determines the shape of the rosin-rammler curve and varies between 0.8 and 1.5. A high value indi - cates uniform sizing whereas a lower value indicates a larger proportion of fine and coarse material (gheibie, Aghababaei, Hoseinie, & Pourrahimian, 2009). Experience has shown that this model predicts the coarse end reason - ably well, but it can significantly underestimate the amount of fines generated (ouchterlony, 2005). Spathis (2004) observed that the uniformity indices in the Kuz-ram model were between 0.8 and 2.2, and the characteristic sizes of the model were in error between 105% and 179%. This led to model extensions, which involved one rosin-rammler function for the coarse material and another to improve the prediction of fines. The Julius Kruttschnitt Mineral research Centre (JKMrC) proposed two empirical models to improve on the Kuz-ram model’s inability to predict the fragment size distribution: the crush zone model (CZM; Kanchibotla, Valery, & Morrell, 1999) and the two-compo - nent model (TCM; Djordjevic, 1999). In a later paper about the Kuz-ram model, Cunningham (2005) made several changes to the prediction equation and pointed out the inability of the model to estimate fines, concluding that the only way to obviate the limitation was by adopting the model extensions. Kanchibotla et al. (1999) proposed the CZM model, which applies two rosin-rammler functions to estimate the fragment size distribution curve: one to predict the fines part of the curve and the other to describe the coarse part of the curve (gheibie et al., 2009). The two curves join at a fragment size ( xc), depending on the rock properties. The coarse part of the curve is represented by P (x) = 1 − e(ln (1− P(xc))( x/xc)ncoarse ) (10) where P(x) is the percentage of material passing sieve size x(in %), xis the sieve size (in m), P(xc) is the percentage of material passing characteristic size xc(in %), xcis the characteristic size (in m), and ncoarse is the uniformity index for the coarse part of the curve. The ncoarse value can be determined from –––––––––––––– (1 + S) –– B B Ltot ncoarse = (2.2 − 14 (–– ))≈(–––––– )(–––– ) (11) d 2 H where Bis the burden (in m), Sis the spacing (in m), d is the borehole diameter (in mm), Ltot is the total charge length (in m), and His the bench height (in m). The fine rock material lies in a cylindrical crushed zone around the blasthole in which particles are generated by being crushed due to compressive shear failure (Kanchi - botla et al., 1999). The crushed zone radius is assumed to be the distance from the blasthole to the point where radial stresses exceed the compressive strength of the rock mass: –––– Pd rc= r≈––– (12) σc where rcis the crushed zone radius (in m), ris the blasthole radius (in m), σc is the compressive strength of the rock (in Pa), and Pdis the detonation pressure (in Pa). Detonation pressure is calculated by Cd2 Pd= ρc––– (13) 4 where Pdis the detonation pressure (in Pa), Cdis the veloc - ity of detonation (in m/s), and pcis the density of explosive material (in kg/m 3). The fraction of the crushed material is obtained from Mo Fc= ––– (14) M where Fcis the fraction of rock mass that fails in tension, Mois the mass of rock that fails in compression (in kg), and Mis the total mass of rock per blasthole (in kg). The fine part of the size distribution curve is predicted by P(x) = 1 − e(ln (1− P(xc))x nfine ) (15) (––) xc where P(x) is the percentage of material passing sieve size x(in %), xis the sieve size (in m), P(xc) is the percentage of material passing characteristic size xc (in %), xc is the characteristic size (in m), and nfine is the uniformity index for the fine part of the curve. The nfine value can be deter - mined from ln (1 −Fc) ln (–––––––––––– ) ln (1−P(xc)) nfine = –– –––––––––––––– (16) 1 ln (–– ) xc where Fc is the fraction of crushed material, P(xc) is the per - centage of material passing characteristic size xc (in %), xc is the characteristic size (in mm), and nfine is the uniformity index for the fine part of the curve. The TCM model proposed by Djordjevic (1999) applies the two rosin-rammler functions simultaneously to pre - dict the fine and coarse components of the size distribution curve. It is a five-parameter model in which two of the CIM Journal | V ol. 9, no. 1, 2018 Stemming zone fragmentation analysis of optimized blasting with top-column air decks 55 parameters represent the coarse fraction, one represents the fines fraction, and the remaining two are related to the fines part of the distribution curve. The fragmentation curve in the TCM models is given by equation 17: P(x) = 100 (1−(1− Fc)e−ln 2( x )b −Fce−ln 2( x )d ) (17) –– –– a c where P(x) is the percentage of material below size x, x is the size of material (in m), Fcis the fraction of crushed material, ais the mean fragment size outside the crushed zone (in m), bis the uniformity coefficient outside the crushed zone, cis the mean fragment size within the crushed zone (in m), and dis the uniformity coefficient within the crushed zone. Parameters aand bare similar to the Kuz- ram model parameters x50 and n; the fraction of the crushed material is determined the same way as in the CZM. The crushed zone radius in the TCM model is predicted by r rc= ––––––––––– − r (18) _________ 24 TS insitu ≈–––––––– Pb where rcis the crushed zone radius (in m), ris the blasthole radius (in m), Pbis the detonation pressure (in Pa), and TS insitu is the in-situ tensile strength (in Pa). The pressure of detonation is determined the same way as in the CZM model, with the in-situ tensile strength determined by 0.05 0.18 TS insitu = στ(–––––––––––––– ) (19) mean block size where στis the tensile strength of rock (in MPa) and the mean block size is in m. ouchterlony (2005) proposed the Kuznetsov-Cunning - ham-ouchterlony (KCo) model to predict the fragment size distribution by replacing the original rosin-rammler equation with the Swebrec ™function (equation 20). The fines part of the fragment size distribution received a more satisfactory prediction when the Swebrec function was introduced (ouchterlony, 2016). Like the rosin-rammler model, the KCo model uses the 50% passing value ( x50 ) as the central parameter, while also introducing an upper limit to fragment size xmax and b, a curve undulating parameter similar to nin the Kuz-ram model (gheibie et al., 2009). The equation for x50 remains the same; the equation for b and the Swebrec function is given by 1 P (x) = ––––––––––––––––––– (20) b xmax ln (––––– ) x (1+ [––––––––– ] ) xmax ln (––––– ) x50 xmax b = [2ln 2 × ln (––––– )] n (21) x50 where P(x) is the percentage of material passing sieve size x(in %), xis the sieve size (in cm), x50 is the 50% passing size (in cm), xmax is the maximum in-situ block size (in cm), and nis the uniformity index. Spathis (2004) rightly com - mented that Cunningham (1987) had misinterpreted the Kuznetsov (1973) formula for the mean fragment size, using it as a mean but later treating it as a median. Spathis (2004) suggested the addition of a prefactor to the rosin- rammler equation, proposing that x50 be x50 / x= ( ln 2) l/ n/ Г(1+1/ n) (22) where x50 is the 50% passing size (in cm), xis the sieve size (in cm), Γis the gamma function, and nis the uniformity index. Equation 23 represents the KCo model as an improved extension of the Kuz-ram model to overcome the failure of predicting the fine range and the upper limit of particle size: 1 1 19 ( ln 2) –n – 115 –30 x50 = ––––––––– AK −0. 8Q6[––––– ] (23) 1 RWS Γ (1+ –– ) n Digital image processing Digital image processing is used to measure blast frag - mentation through digital images. Several digital image processing software packages—namely Split-Desktop, WipFrag√, FragScan, Technical University of Clausthal image processing system (TUCIPS), PowerSieve√, IPACS, and Waterloo image enhancement program (WIEP)—are commercially available to analyze the frag - ment size distribution (ozkahraman, 2006). The image pro - cessing software Split-Desktop (Split Engineering, 2001) was used for this study. Split-Desktop was designed to cal - culate the size distribution of rock fragments by analyzing greyscale images taken by a digital camera (Siddiqui et al., 2009). Image processing has several advantages: It can be completely automated, eliminating several process-related expenses; a great number of measurements can be made, increasing the overall reliability; and it can be used on a continuous basis without affecting the production cycle (Sanchidrián, Segarra, ouchterlony, & López, 2009). Some errors, however, are associated with image processing soft - ware, due mainly to these reasons: ®Image analysis can only process what can be seen with the naked eye and does not take into account the internal composition of a blasted muck pile, thus sampling strategies should be carefully considered. ®Analyzed particle size can be overdivided or overcom - bined: larger particles can be divided into smaller parti - CIM Journal | V ol. 9, no. 1, 2018 56 E. Kabwe and W. Banda cles, and smaller particles can be combined into larger particles. In this case, manual editing is required. ®Very fine particles can be underestimated, especially from a muck pile after blasting. Errors can be minimized by using established sampling methods, a proper sampling environment, and site-specific calibration of the image processing software (Maerz, 1999; Maerz & Zhou, 2000). The most reliable way for fragmen - tation to be assessed is through sieving the entire muck pile, which is nearly impossible. TRIAL SITE The blast was conducted at the Chimiwungo open pit, part of the Lumwana project located in the northwestern province of Zambia. Hosted within the Mwombezhi basement dome in the western arm of the neoproterozoic Lufilian Arc thrust fold belt, it comprises the Chimiwungo main, south, and north mineralized zones (Stroud, 2010). The conventional blast practice revealed problems related to poor blast perfor - mance, which has been the major challenge at the pit. This includes poor fragmentation in the stemming zone, a ten - dency for uncontrolled fly rock, premature ejections, and an elevated cost of explosives and stemming material. MATERIALS AND METHODS A top-column air-deck blast trial was conducted simul - taneously with a conventional blast. general ground condi - tions and a rock-mass rating of the trial area were determined (Tables 1 and 2). The blast design parameters on the conventional and the top-column air-deck blasts were kept similar. Table 3 shows the drilling accuracy results used to determine the SD value. Blast design and parameters The blast design was based on a 3.5 m stemming height and 0.92 kg/m 3powder factor, but after introducing the top- column air deck, these were reduced to 2.5 m and 0.74 kg/m 3, respectively. The blast design parameters used are given in Table 4. Figures 2 and 3 illustrate the ex plosive Table 1. Rock-mass properties determined using RocData® (Rocscience, 2013; GSI, geological strength index) Dominant rock type Gneiss Hoek-Brown classification Intact uniaxial compressive strength (MPa) 189 GSI 55 mi 28 Disturbance factor 0 Hoek-Brown criteria mb 5.613 s 0.0067 a 0.504 Failure envelope range σ3max (MPa) 47.25 Unit weight (MN/m 3) 0.026 Mohr-Coulomb fit Cohesion ( c; MPa) 13.7 Friction angle ( φ;º) 40.94 Rock-mass parameters Tensile strength (MPa) –0.227 Uniaxial compressive strength (MPa) 15.203 Global strength (MPa) 60.05 Modulus of deformation (MPa) 13,335 Poisson ratio ( v) 0.235 Table 2. Rock-mass rating of the blast shot (Bieniawski, 1989) General conditions Intact rock strength (MPa) 100–250 RQD (%) 75–90 Discontinuity spacing (m) 0.6–2.0 Discontinuity orientation Favourable Groundwater conditions Wet Discontinuity conditions Length (m) < 1 m Separation (mm) < 0.1 Roughness Slightly rough Infill (mm) Hard < 5 Weathering Moderate Rock-mass rating 70 Rock-mass classification Class II—good rock Table 3. Drilling precision results for standard deviation determination Drill rig (DR) Drilling accuracy (%) DR05 90 DR06 79 DR07 82 DR08 82 DR10 83 DR11 78 DR12 100 Average 85 Average DR penetration rate (m/h) 22 % re-drills 15 Table 4. Blast design parameters and explosive properties (Abbreviations: ANFO, ammonium nitrate, fuel oil; RWS, Relative weight strength; VOD, velocity of detonation) Blast design parameters Top-column Conventional air deck Hole diameter (mm) 165 165 Bench height (m) 12 12 Subdrilling (m) 0.5 0.5 Burden (m) 4.2 4.2 Spacing (m) 4.5 4.5 Stemming (m) 2.5 3.5 Blasting pattern Square Square Initiation system Nonel or shock tube Nonel or shock tube Powder factor (kg/m 3) 0.72 0.92 Explosive properties Explosive SD135 (emulsion) SD135 (emulsion) Explosive column length (m) 8.0 9.0 Density (g/cm 3) 1.25 1.25 VOD (m/s) 4,750 4,750 RWS (ANFO = 100) 80 80 CIM Journal | V ol. 9, no. 1, 2018 Stemming zone fragmentation analysis of optimized blasting with top-column air decks 57 loading for conventional and top-column air-deck blast - holes, respectively. The designed subdrill was 0.5 m, which was short relative to the burden. When the subdrill is short, the area of maxi - mum tension in a blasthole during blasting will be located higher up and will not break to the bottom of the floor. The rule of thumb in conventional blasting is that subdrill depth (in m) should be the ratio of the burden (nobel, 2010): subdrill = 0.3 × B≈ 1.3 (24) where Bis burden thickness (in m). Based on the burden’s stiffness ratio, the size distribu - tion of the muck pile can be estimated. The stiffness ratio is also a major factor when calculating the spacing for bench blasting applications. The burden stiffness ratio predicts the degree of fragmentation if all blast and bench parameters are to standard: burden stiffness ratio = H/B≈ 2.9 (25) where His the bench height (in m) and Bis the burden (in m). The ratio values of 2–3.5 and > 3.5 represent good and very good fragmenta - tion, respectively (nobel, 2010). The bur - den stiffness ratio value determined from equation 25 was 2.9; this predicted the degree of fragmentation for the rock to be blasted. The cost of charging the conven - tional charged hole was higher than a gas - bag-charged hole. Table 5 shows that the introduction of the top-column air deck with gasbags reduced the quantity and cost of most blasting variables: volume blasted per hole = B× S× H≈ 226.8 (26) where the volume blasted per hole is in m3, Bis the burden (in m), Sis the spacing (in m), and His the bench height (in m). Table 6 sh ows that there were fewer holes drilled as compared to the number of holes marked out, but the volume of pumped emulsion was actually higher than planned, as a result of backfilling overdrilled holes with drill chippings instead of aggregate; the emulsion basi - cally mixed with chippings and thus over - charged the holes. Blasting The top-column air deck blast was fired simultaneously with a conventional blast for assessment (Figure 4). It was observed that the conventional blast experienced premature ejections and the muck pile showing uneven fragmentation (Figure 5), as compared to the top-column air-deck blast. Figure 3. Explosive loading for a top-column air deck with gasbag-charged hole Table 5. Cost of blasting per hole Conventional Top-column blasthole air-deck blasthole Explosives cost (US$) 187.75 165.75 Stemming cost ($) 2.28 1.82 Gasbag cost ($) – 2.89 Total cost per hole ($) 190.03 170.46 Volume blasted per hole (m 3) 226.8 226.8 Powder factor (kg/m 3) 0.97 0.86 Table 6. Comparison of marked out and actual top-column air-deck blasting values Marked out Actual Number of holes blasted 756 722 Pumped emulsion (kg) 136,477 146,583 Powder factor (kg/m 3) 0.77 0.89 Blasted material (t) 473,063 443,624 Figure 2. Explosive loading for a conventionally charged hole CIM Journal | V ol. 9, no. 1, 2018 58 E. Kabwe and W. Banda The top-column air deck –blasted muck pile was evenly fragmented (Figure 6). ANALYSES AND RESULTS Fragmentation model analysis Based on the crushers at the mine, the allowable size classification is shown in Table 7. In the Kuz-ram model, the predicted average x50 size is 324 mm for the top-col - umn air-deck blast and 348 mm for the conventional blast. The uniformity factor was 1.30 for the top-column air- deck blast and 1.073 for the conventional blast. The pro - portion of material larger than 900 mm, P(x90 ), was approximately 7 and 11% for the top-column air-deck and conventional blasts, resp ectively (Table 8). In the KCo model, the average fragment size is 303 mm and the curve undulating parameter is b= 3.172 for the top- column air-deck blast, whereas for the conventional blasted c) b) a) d) e) Figure 4. a) Location of the charged top-column air deck and conventional blasts, b) rock ejections after blast detonation, and c) rock ejections at a later time after blast detonation a) b) c) Figure 5. Conventional blasted muck pile, showing uneven fragmentation. In a), the red tape indicates the portion of the muck pile with ore and the white tape indicates the portion of the muck pile with waste. The green arrows in b) and d) point to the scaling ball used for fragmentation analysis. CIM Journal | V ol. 9, no. 1, 2018 Stemming zone fragmentation analysis of optimized blasting with top-column air decks 59 muck pile, the average fragment size is 282 mm and the curve undulating parameter is b= 2.299 (Table 9). In the CZM model, the predicted average value of x50 is 27 mm for the top-column air-deck blast and 240 mm for the conventional blast. The uniformity factor for the fines part of the size distribution is 0.081 and 0.392 for the top- column air-deck blast and conventional blast, respectively, whereas the uniformity factor for the coarse part of the dis - tribution is 1.973 and 1.329 for the top-column air-deck blast and the conventional blast, respecti vely (Table 10). Digital image analysis Twenty photographs were taken on both muck piles before mucking, after mucking, and every time a truck was loaded. The images were analyzed and processed with Split-Desktop (Split Engineering, 2001), to estimate size distribution results. Delineated images, size distribution results, and fragment size distribution curves are shown in Figures 7–9 and Tables 11 and 12. The prediction values of the mean fragmentation from the empirical models are in range with those obtained from Split-Desk top image analysis (Tables 13, 14; Figures 10, 11). Spli t-Desktop results for conventional and air-deck Table 9. Kuznetsov-Cunningham-Ouchterlony (KCO) fragmentation model results Parameter Top-column Conventional air deck blast x50 (mm) 303 282 b 3.172 2.299 n 2.03 1.573 Maximum fragment size 990 999 Table 8. Kuz-Ram fragmentation model results Parameter Top-column Conventional air deck blast x50 (mm) 324 348 P(x 90 )(%) 0.07 0.11 n 1.3 1.1 SD (m) 0.68 0.68 Table 10. Crush zone (CZM) fragmentation model results Parameter Top-column Conventional air deck blast x50 (mm) 27 240 ncoarse 1.973 1.329 nfines 0.081 0.392 Figure 6. Top-column air deck–blasted muck pile, showing even fragmentation. The green arrows in a), c), d), and e) point to the scaling ball used for fragmentation analysis. c) b) a) d) e) Table 7. Crusher material classification Particle size (mm) Classification < 50 Overblasted material 50–800 Very fine material 800–1,100 Good crusher feed CIM Journal | V ol. 9, no. 1, 2018 60 E. Kabwe and W. Banda blasts were plotted on a graph, clarifying that air-deck frag - mentation is as good as, or better than, conventional blast fragmentation (Figure 12). RESULTS A substantial reduction in explosive load in conven - tional holes to top-column air-deck holes was obtained: 221 kg per hole to 195 kg per hole. The powder factor was reduced from 0.92 to 0.72 kg/m 3, as was the cost of explosive per hole, from US$187.75 to $165.75. The stemming cost decreased from $2.28 to $1.82 per hole, and the overall cost per hole decreased from $190.03 to $170.46, a reduction of approximately $20 per hole. Thus, the total reduction on the 722 holes blasted by the Figure 8. Top-column air-deck blast fragment size distribution curve from 10 analyzed photos of the muck pile Percent passing (%) Size (mm) Figure 9. Conventional blast fragment size distribution curve from 10 analyzed photos of the muck pile Percent passing (%) Size (mm) Table 13. Top-column air-deck blast fragment sizes according to different fragmentation models Fragmentation x20 x50 x80 Top size model (mm) (mm) (mm) (mm) Kuz-Ram 102 324 467 1,523 Kuznetsov-Cunningham-Ouchterlony 110 303 570 990 Crush zone 8.5 27 405 460 Split-Desktop 42 265 683 1,455 Table 14. Conventional blast fragment sizes according to different fragmentation models Fragmentation x20 x50 x80 Top size model (mm) (mm) (mm) (mm) Kuz-Ram 86 348 542 2,098 Kuznetsov-Cunningham-Ouchterlony 170 282 660 999 Crush zone 38 240 500 600 Split-Desktop 112 385 771 1,436 Table 11. Top-column air-deck blast cumulative size distribution results obtained from Split-Desktop Size (mm) % passing 2,000 100 1,000 93 750 84 500 68 375 59 250 49 187 42 78.7 28 % passing Size (mm) x10 11 x20 42 x30 94 x40 168 x50 265 x60 389 x70 523 x80 683 x90 899 Top size (99.95%) 1,455 Table 12. Conventional blast cumulative size distribution results obtained from Split-Desktop Size (mm) % passing 1,270 100 735 77 381 50 254 37 203 31 152 25 102 19 51 12 25 7.2 19 6% passing Size (mm) x10 41 x20 112 x30 199 x40 283 x50 385 x60 479 x70 567 x80 771 x90 961 Top size (99.95%) 1,436 Figure 7. Digitization and delineation of sample photograph of the muck piles using Split-Desktop CIM Journal | V ol. 9, no. 1, 2018 Stemming zone fragmentation analysis of optimized blasting with top-column air decks 61 application of a top-column air-deck was approxi - mately $14,129.54, which accounts for an approxi - mately 20% cost reduction, and which saved approximately $5.024/t blasted. results obtained from the analysis of muck-pile images using Split-Desktop showed that the mean frag - ment size was 265 mm, and x20 , x80 , and top size were 42, 683, and 1,455 mm, respectively. The optimum crusher feed size was 1,200 mm and a small fraction of the material was larger than 1,200 mm. CONCLUSIONS The blast conducted showed that on the top-column air deck–blasted muck pile, there was a substantial decrease in proportion of oversize fragment generation in the stemming area. The rosin-rammler uniformity index predicted value was 1.3; the obtained value con - firmed uniform size distribution. In the empirical fragmentation models, rock-mass input parameters are not easy to determine. given the correct input parameters, fragmentation models do not consistently predict correct fragmentation because they capture some, but not all, of the important rock-mass and blast design parameters. Values determined through the image analysis soft - ware for x20 , x50 , and x80 were close in range with pre - dicted values from the empirical fragmentation models, which showed data consistency; however, an increase in sample space would allow for a better determination of the values. The mine will benefit from the application of air decks in terms of cost savings, higher efficiency, and environmentally sound operations. ACKNOWLEDGMENTS The author thanks the volunteer peer reviewers for CIM, whose contributions improved the quality of this paper. Approval for publication by the Underground Mining Society of CIM is also appreciated. The author acknowledges the support of the Lumwana Mining Company, Zambia. Paper reviewed and approved for publication by the Underground Mining Society of CIM. Eugie Kabwe received his BSc (MinSc) in mining engineering from the University of Zambia, Lusaka, Zambia. He worked in the mining industry for more than five years and later earned his MEng in mining engineering from the University of Science and Technology Beijing, Beijing, China. He is currently a PhD student at the University of Adelaide, Adelaide, Australia. eugie.kabwe@adelaide.edu.au Webby Banda is a Lecturer of Mineral Economics in the Department of Mining Engineering at the University of Zambia (UnZA). He holds a Bachelor of Mineral Sciences (BMinSc) degree and Master of Mineral Sciences (MMinSc) degree, both of which he obtained from UnZA. Figure 10. Comparison of fragmentation curves from top-column air-deck blast (CZM, crush zone model; KCo, Kuznetsov-Cunningham-ouchterlony model) Figure 11. Comparison of fragmentation curves from conventional blast (CZM, crush zone model; KCo, Kuznetsov-Cunningham-ouchterlony model) Figure 12 Comparison of Split-Desktop fragmentation results for conventional and air-deck blasts CIM Journal | V ol. 9, no. 1, 2018 62 E. Kabwe and W. Banda