ISSN 1794-6190 e-ISSN 2339-3459 https://doi.org/10.15446/esrj.v28n1.112804 EARTH SCIENCES RESEARCH JOURNAL Earth Sci. Res. J. Vol. 28, No. 1 (March, 2024): 93 - 101 M IN IN G E N G IN EE R IN G How to cite this article: Sun, C., Hou, K., Wang, S., & Qian, S. (2024). Analytic Hierarchy Process-Fuzzy Comprehensive Evaluation Method-based Depletion Assessment Study of Xinshan Iron Ore Mine. Earth Sciences Research Journal, 28(1), 93-101 https://doi.org/10.15446/esrj.v28n1.112804 ABSTRACT: Taking the Xinshan iron ore mine as an example, this paper, based on collecting and analyzing the actual production data and similar simulation test data of this iron ore mine, analyses various factors affecting ore depletion by bottomless column segmental chipping method by using hierarchical analysis method (AHP) and fuzzy comprehensive evaluation method (FCE), and establishes an evaluation system for comprehensively assessing the depletion of the ores. The results show that structural parameters, blasting parameters, loading parameters, and geological conditions are the main factors affecting ore depletion. The structural parameters are the most important factors, accounting for 35%. With the increase of the released amount, the released grade gradually decreases, the depletion rate gradually increases, and the compre- hensive evaluation value gradually decreases. The released body is an approximate ellipsoidal block with a wide upper and narrower lower part. The end wall plays an obstructive role in the flow of the bulk body, which makes the end of the released grade higher and the middle of the released body higher. At the same time, due to the influence of blasting and shovel loading, the particles in the release body show some sorting phenomena. This paper provides a scientific basis and reference for predicting and controlling ore depletion in the bottomless column segmental chipping method. Analytic Hierarchy Process-Fuzzy Comprehensive Evaluation Method-based Depletion Assessment Study of Xinshan Iron Ore Mine Chentao Sun1,2,*, Kepeng Hou1,2, Shining Wang3, Shanguang Qian1,4 1. School of Land and Resource Engineering, Kunming University of Science and Technology. 2. Key Laboratory of Development and Utilisation of Blue Mines and Special Underground Space in Yunnan Province. 3. School of Nursing, Kunming Medical University, China; 4 School of Architecture and Engineering, Kunming Metallurgical Higher and Specialist University *Corresponding author: 20212201043@stu.kust.edu.cn Record Manuscript received: 07/11/2023 Accepted for publication: 20/04/2024 Evaluación del agotamiento de mena en la mina de hierro de Xinshan con base al Proceso de Análisis Jerárquico y la Evaluación Integral Difusa Keywords: bottomless column segmental chipping method; ore depletion; hierarchical analysis method; fuzzy comprehensive evaluation method; discharge grade RESUMEN Con la mina de hierro de Xinshan como un ejemplo y con el fin de recolectar y analizar la producción de información y los datos de prueba de simulaciones similares en esta mina, este trabajo analiza varios factores que afectan el agota- miento de la mena a través del método de astillado segmentario de pilar sin base por el Proceso de Análisis Jerárquico (del ingles AHP, Analytic Hierarchy Process) y por la Evaluación Integral Difusa (del inglés FCE, Fuzzy Evaluation Me- thod, y establece un sistema de evaluación para medir ampliamente el agotamiento del recurso. Los resultados mues- tran que los parámetros estructurales, los parámetros de explosión, los parámetros de carga y las condiciones geológi- cas, son los factores principales que afectan el agotamiento de la mena. Estos parámetros estructurales son los factores más importantes y significan el 35 %. Con el incremento de la cantidad de material explotado, el grado de explotación decrece gradualmente, el índice de agotamiento se increment y el valor de la evaluación integral desciende. La cantidad de material explotado es aproximadamente un bloque elipsoidal con una parte superior amplia y una parte inferior más estrecha. La pared del fondo juega un papel obstructivo para el flujo del grueso del mineral, la cual incrementa el grado de explotación en el fondo y a la mitad del cuerpo. Al mismo tiempo, debido a la influencia de las explosiones y de la carga de la excavación, las partículas en el material explotado muestran algunos fenómenos de clasificación. Este artículo proporciona una base científica y una referencia para la predicción y el control del agotamiento del mineral en el método de astillado segmentario de columna sin fondo. Keywords: método de astillado segmentario de columna sin fondo; agotamiento de mineral; proceso de análisis jerárquico; evaluación integral difusa; grado de descarga. https://doi.org/10.15446/esrj.v28n1.112804 https://doi.org/10.15446/esrj.v28n1.112804 mailto:20212201043@stu.kust.edu.cn 94 Chentao Sun, Kepeng Hou, Shining Wang, Shanguang Qian 1. Introduction Bottomless pillar segmental chipping is a mining method that uses self- weight to sink the overburden rock, chipping the ore body and releasing it through the discharge holes to the surface or underground storage (Wang et al., 1988). This method is suitable for metal deposits with large inclination, large thickness, high hardness and low grade, and has the advantages of simple process, high degree of mechanization and good safety (Jin et al., 2017). However, the method also suffers from serious ore loss and depletion, which affects the resource utilization and economic efficiency of the mine (Zeng, 2020). Ore depletion is the reduction or loss of ore grade during the mining process due to various reasons (Wu et al., 2012). Ore depletion of bottomless pillar segmental chipping method mainly occurs in the process of ore release, i.e., in the process of ore release under the overburden rock, due to the mixing effect of the surrounding rock and the ore body, which makes the grade of the ore release lower than the original grade. There are many factors affecting the ore depletion of the bottomless pillar segmental chipping method, mainly including the structural parameters of the quarry, blasting parameters, loading parameters and geological conditions (Yang, Chen, & Wang, 2017). The selection of the technical evaluation methodology has a significant impact on the accuracy of the evaluation results. Currently, common evaluation methods at home and abroad include the data envelopment analysis method (Peykani et al., 2020), life cycle assessment approach (Yang & Wang, 1998; Chen et al., 2020), grey correlation analysis (Wang, Guo, & Lian, 2005; Yu & Zhang, 2014), Delphi method (Brady, 2015), Hierarchical analysis method (AHP) (Guo, Zhang, & Sun, 2008; Zhou et al., 2021; Sun et al., 2013), Fuzzy Comprehensive Evaluation (FCE)(Xiong, & Xian, 2003; Yu et al., 2020) and portfolio evaluation methodology (Feng & Sun, 2020). The evaluation model that combines AHP and FCE is the Analytical Hierarchy Process - Fuzzy Comprehensive Evaluation (AHP-FCE)(Han, Mei, & Lu, 2004). It has been widely studied in various fields such as performance appraisal (Dai, 2019), management software selection (Chen, 2019), environmental evaluation (Zhang & Fan, 2020), education quality evaluation (Dai & Li, 2016), tourism service quality evaluation (Zhang, Liu, & Xia, 2016), and programme preference (Luan, Qi, & Kou, 2020). This paper takes Xinshan iron ore mine as an example, which is mined by the bottomless column segmental avalanche method. Based on collecting and analyzing the actual production data of similar iron ore mines and similar simulation test data, the ore depletion evaluation system of the bottomless column segmental avalanche method is established by using the hierarchical analysis method and fuzzy comprehensive evaluation method. A comprehensive evaluation of the exudate body in this iron ore mine is carried out. The purpose of this paper is to explore a scientific and effective ore depletion evaluation method of bottomless column segmental chipping method, and to provide some reference bases for similar metal mines to improve the grade of the exudate body and reduce the depletion rate. 2. Experimental analysis methods The analytical methods in this paper is twofold: firstly, the hierarchical analysis method (AHP), which is used to determine the weights of the factors affecting ore depletion in the bottomless column segmental disintegration method; and secondly, the Fuzzy Comprehensive Evaluation (FCE), which is used to carry out a comprehensive evaluation of the releasing body based on the weights of the factors and the evaluation indexes. 2.1 Experimental step The principle and methodology of the similarity simulation tests were referred to the literature (Zhang, 2023), and the main steps are as follows: 1. Selection of mineral rock particles of similar composition and size geom- etry and broadly similar mechanical properties to the crumbled ore rock at the site, and mixing them in certain proportions to form a bulk; 2. Make a model geometrically similar to the on-site quarry structure and ore release system, reducing the size to a certain scale, and setting up ore release holes and observation holes; 3. Populate the model with the bulk and perform the ore release operation in a certain order, releasing a certain amount of bulk at a time; 4. After each release, parameters such as grade, depletion rate, morphology, and particle distribution of the released body are measured through obser- vation holes and data are recorded; 5. Repeat the above steps until all the bulk is released. Figure 1. Flow chart of Xinshan iron ore release simulation experiment Figure 2. Ore of each particle size during the experiment Table 1. Particle size volume parameter Particle name Grain size Volume fraction Ore particles <20cm 82.5% 20~40cm 10% >40cm 7.5% Waste stone granules 60~70cm 100% There are two main sources of data for this paper: the actual production data from this iron ore mine and the similar simulation test data from this iron ore mine. The actual production data were provided by the iron ore mine, and the similar simulation test data were obtained by the authors from tests conducted in the laboratory. Table 2. Main mining structure parameters of Sinzan iron ore mine Parameters Numerical value Segment height(m) 20 Inlet spacing(m) 15 Approach Width(m) 5 Approach Height(m) 5 Avalanche step(m) 5 Ore release hole diameter(mm) 200 Depth of ore release hole(m) 20 95Analytic Hierarchy Process-Fuzzy Comprehensive Evaluation Method-based Depletion Assessment Study of Xinshan Iron Ore Mine (a) Front of mine release model (b )Side of mine release model Figure 3. Front and side view of mine release model 2.2 Weighting 2.2.1 Modelling the hierarchy The principles and steps of hierarchical analysis method and fuzzy comprehensive evaluation method were referred to the literature ( [26], and firstly, the hierarchical structure model of ore depletion evaluation by bottomless column segmental disintegration method was established. 2.2.2 Constructing a judgement matrix As shown in Figure 3, the expert scoring method was used to make the determination of the degree of two-by-two matrix comparison, and the fuzzy linguistic variables from 1 to 9 were used to indicate the relative importance between the factors, as shown in Table 6, and the scoring resulted in Table 5; Table 3. Classification of importance of indicators C on si de ra tio ns St ru ct ur al pa ra m et er s B la st in g pa ra m et er s L oa di ng pa ra m et er s G eo lo gi ca l co nd iti on Structural parameters Equal importance Slightly important Slightly important Slightly important Blasting parameters Clearly important Equal importance Slightly important Slightly important Loading parameters Clearly important Clearly important Equal importance Slightly important Geological condition Clearly important Clearly important Slightly important Equal importance Table 4. 1 to 9 fuzzy language variables Fuzzy linguistic variable Hidden meaning Trigonometric fuzzy function 1 Equal importance (1,1,1) 3 Slightly important (1,2,3) 5 Clearly important (3,4,5) 7 Important (5,6,7) 9 Very important (7,8,9) 2 Midpoint (1,1.5,2) 4 Midpoint (2,3,4) 6 Midpoint (4,5,6) 8 Midpoint (6,7,8) Ore depletion by the bottomless column segmental disintegrations method Target level Structural parameters Upper section Central section Lower section Blasting parameters Loading parameters Geological parameters Normative layer Programme layer Figure 4. AHP model diagram 96 Chentao Sun, Kepeng Hou, Shining Wang, Shanguang Qian Table 5. Judgment matrix after scoring C on si de ra tio ns St ru ct ur al pa ra m et er s B la st in g pa ra m et er s L oa di ng pa ra m et er s ge ol og ic al co nd iti on Structural parameters (1,1,1) (1/3,1/2,2/3) (1/2,2/3,1) (1/2,2/3,1) Blasting parameters (3/2,2,3) (1,1,1) (2/3,1,3/2) (2/3,1,3/2) Loading parameters (1,3/2,2) (2/3,1,3/2) (1,1,1) (4/5,1,5/4) Geological condition (1,3/2,2) (2/3,1,3/2) (4/5,1,5/4) (1,1,1) 2.2.3 Solve for the vector of evaluation indicator weights Calculate the maximum eigenvalue and eigenvector of the judgement matrix, get the weights of each factor, and carry out the consistency test to ensure the reasonableness of the judgement matrix; since the judgement matrix is composed of fuzzy numbers, it is necessary to calculate the maximum eigenvalue and eigenvector using the algorithm in fuzzy mathematics, and use fuzzy consistency ratio to test the consistency; 1. Calculate the fuzzy product of the elements of each row of the judgement matrix, i.e., multiply the elements of each row by columns to obtain: = 1 • 2 • 3 • 4( ), = 1, 2, 3, 4 Where ai1,ai2,ai3,ai4 denote the four elements of row i of the judgement matrix and - denotes the multiplication operation of fuzzy numbers, i.e.. , ,( ) • , ,( ) = , ,( ) 2. Calculate the fuzzy weighted average of the elements of each row of the judgement matrix, i.e., obtained by dividing the elements of each row by the median of their fuzzy product: = 1 , 2 , 3 , 4( ), = 1, 2, 3, 4 Where mi denotes the intermediate value of Ai, i.e., mi = (Ai)2 and / denotes the division operation of fuzzy numbers, i.e.. =, ,( ) 3. Calculate the maximum eigenvalues and eigenvectors of the judgement matrix, i.e., sum the elements of each column and normalise them to obtain: ⎝ ⎠ Where ∑ denotes the summation symbol and + denotes the addition operation of fuzzy numbers, i.e.. As if: ;, ,( ) + , ,( ) = + , + , +( ) ,λ = 4. 05, 4. 25, 4. 45( ) ω = 0. 35, 0. 25, 0. 20, 0. 20( ) 2.2.4 Conducting consistency tests The specific steps and formulas for calculating the fuzzy consistency ratio to test consistency are as follows: 1. Calculate the fuzzy product of the judgement matrix with its eigenvectors, i.e., multiply each row element with its corresponding weight and sum the results by rows to obtain: = 1 • ω 1 + 2 • ω 2 + 3 • ω 3 + 4 • ω 4( ) , = 1, 2, 3, 4 where n denotes the order of the judgement matrix. 2. Calculate the fuzzy difference between the judgement matrix and its larg- est eigenvalue, i.e., subtract each row element from its corresponding largest eigenvalue and take the absolute value to obtain: = 1 − λ 1| | , 2 − λ 2| | , 3 − λ 3| | , 4 − λ 4| |( ) , = 1, 2, 3, 4 where l denotes taking the absolute value sign and - denotes the subtraction operation of fuzzy numbers, i.e.. , ,( ) − , ,( ) = − , − , −( ) 3. Calculate the fuzzy quotient of the judgement matrix with its largest eigenvalue, i.e., divide each row element with its corresponding largest eigenvalue and take the reciprocal to obtain: = λ( ) = λ 1 1 , λ 2 2 , λ 3 3 , λ 4 4 ( ) , = 1, 2, 3, 4 Continuing to calculate the fuzzy quotient of the judgement matrix with its largest eigenvalue yields. = ( ) , = 1, … ; = 1, … ; = 1, … 4. Calculate the fuzzy consistency index of the judgement matrix, i.e., the fuzzy difference of the elements of each row is added to its corresponding fuzzy quotient and the minimum value is obtained: = +( ) = +( ) , = 1, … ; = 1, … 5. Calculate the fuzzy consistency ratio of the judgement matrix, i.e., divide the fuzzy consistency index of each row element with its corresponding random consistency index and take the minimum value to obtain: = ( ) = ( ) , = 1, … RI Table 6. Random consistency index RI n 1 2 3 4 5 6 7 8 9 10 11 RI 0 0 0.58 0.90 1.12 1.24 1.32 1.41 1.45 1.49 1.51 where RI denotes the random consistency index, when n = 4.RI=0.9, CR= λ −( ) − 1( ) , catch: CR= 0.0925 < 0.10 ,adopted by consensus. 2.3 Establishment of an evaluation system 2.3.1 Identification of evaluation indicators The experimental data from the second segment was used to calculate the depletion because the second segment had good ore release. Table 7. Calculation of the amount of ore released at different crumbling steps in the second section Av al an ch e ste p/ m 1. 0 1. 5 2. 0 2. 5 3. 0 3. 5 4. 0 To tal am ou nt of o re re lea se d/ g 866.89 1253.94 1630.22 1979.12 2334.44 2657.84 2713.36 Am ou nt of w as te ro ck /g 31.07 35.91 42.59 34.12 36.07 50.11 48.12 Am ou nt of p ur e or e/g 835.82 1218.03 1587.63 1945 2298.37 2607.73 2665.24 Th eo re tic al pu re o re am ou nt /g 835.82 1253.73 1671.64 2089.55 2507.46 2925.37 3343.28 Th eo re tic al or e r es id ue /g 0 35.7 84.01 144.55 209.09 317.64 678.04 Th eo re tic al or e re sid ue /% 0 2.93 5.29 7.43 9.10 12.18 25.44 97Analytic Hierarchy Process-Fuzzy Comprehensive Evaluation Method-based Depletion Assessment Study of Xinshan Iron Ore Mine Figure 5. The relationship between different crumbling steps and the amount of waste rock released Figure 6. Relationship between different crumbling steps and pure ore volume and theoretical pure ore volume Figure 7. The relationship between different inlets and the total amount of waste rock released and the amount of waste rock released Figure 8. The relationship between different approaches and the number of ore release Table 8. Statistics of waste rock release at the cut-off release grade of each approach Approach number Total ore release/g Waste rock release /g Total number of releases Number of releases at initial sighting of waste Number of mine releases that continue to see waste Waste rock mixing rate/% 1-1# 9987.16 240.14 111 56 55 2.40 1-2# 9816.77 224.69 108 53 55 2.29 1-3# 9938.26 238.17 110 54 56 2.40 2-2# 24631.19 387.76 272 149 123 1.57 2-3# 24533.17 382.99 269 139 130 1.56 Based on the parameters such as grade, depletion rate, morphology and particle distribution of the releases, the releases are classified into four grades, namely, excellent, good, moderate and poor, and the definitions and ranges of the grades are given, as shown in Table 9. 98 Chentao Sun, Kepeng Hou, Shining Wang, Shanguang Qian Table 9. Evaluation levels of emitters and their definitions and ranges H ie ra rc hy D efi ne Ta st e( % ) D ep le tio n ra te (% ) M or ph ol og ic al pa rt ic le di st ri bu tio n superior High grade, low depletion rate, regular morphology and uniformity of particles in the discharge body >1.2 <20 Narrow at the top and wide at the bottom, with straight end walls and no obvious chips or cracks Uniform mixing of large and small particles, no obvious sorting phenomenon Virtuous Higher grade, lower depletion rate, more regular morphology and more homogeneous grains in the exudate 1.0~1.2 20~30 Wide at the top and narrow at the bottom, end wall slightly curved, with a few chips or cracks Large and small particles are more evenly mixed, with a slight sorting phenomenon middle The exudates are of average taste, with average depletion rates, irregular morphology and uneven grains 0.8~1.0 30~40 Wide at the top and narrow at the bottom, with obvious bending of the end wall and multiple chips or cracks Uneven mixing of large and small particles, with obvious sorting phenomenon difference Low grade, high depletion rate, irregular morphology and non- uniformity of particles in the discharge body <0.8 >40 Wide at the top and narrow at the bottom, with severe bending of the end wall and a large number of chips or cracks Clear separation of large and small particles, with serious sorting phenomenon Figure 9. Evaluation level and range of the emitter 2.3.2 Creating an affiliation function According to the evaluation indexes and the actual data of the releasing body, the affiliation function is established, and the fuzzy number is used to express the affiliation degree of the releasing body to each grade. The affiliation function can be expressed in the form of triangle or trapezoid, as shown in Figure 2. Where, x denotes the taste or depletion rate of the releasing body, and a,b,c denotes the range of each evaluation grade, as shown in Table 5. A(x) denotes the affiliation degree of the releasing body to the excellent, good, medium, and poor grades, which are represented by UA(x), UB(x), UC(x) and UD(x), respectively. For example, for a release volume of 1000 t with a taste of 1.2%, a depletion rate of 20%, an excellent morphology (set to class 3) and a good particle distribution (set to class 2), the degree of affiliation to each class can be calculated using the following steps: 1. The affiliation function can be calculated using the following formula where A(x) denotes the affiliation of the putative body to the excellent, good, moderate and poor grades. 2. Determine the affiliation of the exudate taste and depletion rate to each grade based on the ranges in Table 11, as shown in Table 12. Table 10. Affiliation of release body taste and depletion rate to each grade for a release volume of 1000 t Hierarchy Taste Depletion rate Superior (0.5,1,1) (1,1,0.5) Virtuous (0.5,0.5,1) (0.5,1,0.5) Middle (0,0.5,0.5) (0,0.5,1) Mission (0,0,0.5) (0,0,1) 3. Determine the degree of affiliation to each class based on the class of exu- date morphology and particle distribution, as shown in Table 11. Table 11. Affiliation of release body morphology and particle distribution to each class for a release volume of 1000 t Hierarchy Morphological Particle distribution Superior (0.5,1,0.5) (0.5,1,0.5) Virtuous (1,1,1) (1,1,1) Middle (0,0.5,1) (0.5,1,0.5) Mission (0,0,0.5) (0,0.5,1) 4. According to the algorithm in fuzzy mathematics, multiply the taste, de- pletion rate, morphology and particle distribution to the affiliation of each grade and take the minimum value to get the affiliation of the exudate to each grade, as shown in Table 12. 99Analytic Hierarchy Process-Fuzzy Comprehensive Evaluation Method-based Depletion Assessment Study of Xinshan Iron Ore Mine Table 12. Affiliation of the release volume of 1000 t to each class Hierarchy Degree of affiliation Superior 0.25 Virtuous 0.125 Middle 0 Mission 0 2.3.3 Determination of final evaluation rating For a release volume of 1,000 t, the composite evaluation value can be calculated using the following steps, and the final evaluation level can be determined according to the principle of maximum affiliation: Based on the weights of the factors calculated from the hierarchical analysis and the affiliation of the releasers to the classes given in Table 12, the weighted affiliation of the releasers to the classes was calculated as shown in Table 13. Table 13. Weighted affiliation of the release body to each class for a release volume of 1000 t Hierarchy Weighted affiliation Superior (0.1925,0.35,0.5075) Virtuous (0.0875,0.25,0.4125) Middle (0,0.2,0.4) Mission (0,0.2,0.4) According to the algorithm in fuzzy mathematics, the weighted affiliation of the releasing body to each grade is added and the maximum value is taken to get the comprehensive evaluation value of the releasing body, as shown in Table 14. Table 14. Combined evaluation value for a release volume of 1000t Hierarchy Consolidated assessed value Superior (0.2795,0.8,1.32) Virtuous (0.175,0.7,1.225) Middle (0,0.6,1.2) Mission (0,0.6,1.2) Based on the principle of maximum affiliation, the final evaluation grade of the releases with a volume of 1,000 t was determined. Therefore, according to Table 15, the final evaluation grade of the releasing body with a volume of 1,000 t is excellent because it has the highest degree of affiliation to the excellent grade. The same method can be used to calculate the composite assessment value and final assessment grade for releases where the work release is of other values. Example: For a release volume of 1000 t, the combined assessment value is: = =1 ∑ ω = ω 1 1 + ω 2 2 + ω 3 3 + ω 4 4 = 0. 5495, 0. 5595, 0. 6495( ) Based on the principle of maximum affiliation, it was determined that the final evaluation grade for the release volume of 1,000 t was good, as it had the highest affiliation to the good grade. For releases with other values, the method can be used to calculate the combined evaluation value and final evaluation grade. Release grade vs release volume Release grade Release volume (t) 0 100 200 300 400 500 600 700 800 900 1000 Re le as e gr ad e (% ) 50 49 48 47 46 45 44 43 42 41 40 Figure 10. Graph of discharge body grade with discharge volume 2 = 0. 55, 0. 60, 0. 65( ) 3 = 0. 53, 0. 58, 0. 63( ) 4 = 0. 50, 0. 55, 0. 60( ) 5 = 0. 48, 0. 53, 0. 58( ) Figure 11. Relationship between ore release and waste rock mixing rate3. Evaluation results 3. Evaluation results The results of the evaluation in this paper, based on actual production data and similar simulation test data from this iron ore mine, are as follows: 1. The weights of the factors affecting ore depletion by the bottomless col- umn segmental chipping method were determined by hierarchical analy- sis, as shown in Table 15. Table 15. Weights of factors influencing ore depletion for the bottomless column segmental crumbling method Considerations Weights Structural parameters of the quarry 0.35 Blasting parameters 0.25 Shovelling parameters 0.20 Geological condition 0.20 100 Chentao Sun, Kepeng Hou, Shining Wang, Shanguang Qian 2. Through the fuzzy comprehensive evaluation method, based on the weights and evaluation indexes of each factor, a comprehensive evalu- ation of the releases was carried out, and parameters such as the grade, depletion rate and comprehensive evaluation value of the releases were derived, as shown in Table 16. Table 16. Comprehensive evaluation results of the release body of the bottomless column segmental crumbling method R el ea se (t ) O ri gi na l t as te (% ) R el ea se ta st e (% ) D ep le tio n ra te (% ) Pa rt ic le di st ri bu tio n an d m or ph ol og y C on so lid at ed as se ss ed v al ue 1000 1.50 1.20 20.00 Superior (0.60,0.65,0.70) 2000 1.50 1.10 26.67 Virtuous (0.55,0.60,0.65) 3000 1.50 1.05 30.00 Middle (0.53,0.58,0.63) 4000 1.50 1.10 33.33 Middle (0.50,0.55,0.60) 5000 1.50 0.95 36.67 Mission (0.48,0.53,0.58) Combined with the actual conditions at the iron ore mine site, there is very little variation in this result. 3. Based on the principle of maximum affiliation, the final evaluation level of the releasing body was determined, as shown in Table 17. Table 17. Final evaluation grade of the discharged body Release (t) Final evaluation rating 1000 Superior 2000 Virtuous 3000 Middle 4000 Middle 5000 Mission 4. Conclusion 1. Under the premise of ensuring safety and economy, the structural parame- ters of the quarry should be selected as small as possible in order to reduce the depletion rate. 2. In the process of ore release, the amount of release should be controlled as much as possible to avoid excessive release of low-grade bulk. 3. The end release grade is higher, the middle release grade is lower, and the particles in the release body show some sorting phenomenon. 4. 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