Blasting fragmentation prediction method based on ore rock crushing mechanism

Abstract

The invention relates to the technical field of mine engineering, and discloses a blasting lumpiness prediction method based on an ore rock crushing mechanism, which comprises the following steps of: modeling an ore body and initializing blasting parameters, constructing a three-dimensional mechanical model of the ore body and setting blasting design parameters; establishing a blasting energy transfer field model, and calculating the distribution of blasting energy in the ore body based on the physical characteristics of the ore body and blasting design parameters; simulating a crack propagation process based on the energy transfer field, establishing a crack propagation model, and calculating crack distribution parameters; and combining the crack distribution parameters to construct a blasting lumpiness distribution model, and fitting the parameters of the model. According to the invention, a modularized technical scheme is adopted, closed-loop design from theory to practical application is realized through division and cooperation of the ore body modeling module, the energy transfer field simulation module, the crack propagation modeling module, the lumpiness distribution prediction module and the feedback improvement module, the design scientificity is improved, and the method can adapt to complex ore body conditions.

Description

Technical Field

[0001] The present invention relates to the technical field of mine engineering, and in particular to a blasting fragment size prediction method based on the ore-rock fragmentation mechanism. Background Art

[0002] In the process of mine exploitation, blasting, as one of the key links, directly affects the efficiency of subsequent loading, transportation, grinding, and beneficiation processes. The effect of blasting operations is usually measured by the fragment size distribution. The uniformity and rationality of the fragment size distribution play a decisive role in production efficiency, energy consumption, and resource utilization rate. A reasonable blasting design can effectively control the ore fragment size distribution, thereby improving the crushing efficiency and reducing the grinding cost. However, due to the complexity of the ore body geological conditions and the dynamic characteristics of blasting energy transfer, achieving accurate prediction of the fragment size distribution has always been a difficult problem in mine engineering.

[0003] In the prior art, the prediction of fragment size distribution mostly relies on empirical formulas or simplified physical models. These methods usually rely on historical data of mine engineering and estimate the fragmentation effect of ore-rock by establishing simple mathematical relationships. For example, common empirical formulas based on parameters such as rock mass strength, blasting energy, and hole spacing can obtain certain guiding significance under specific mine conditions. Such formulas mainly rely on the results of statistical regression rather than being based on the ore-rock fragmentation mechanism, so their applicability and accuracy are extremely limited. Especially in the face of complex ore body geological conditions or multi-point initiation scenarios, the limitations of these formulas become more obvious.

[0004] Moreover, the heterogeneity and dynamic changes of the ore body further increase the difficulty of predicting the fragment size distribution. In actual mines, the physical properties of the ore body (such as density, elastic modulus, Poisson's ratio) often fluctuate with the change of spatial position, and the fracture distribution also has significant randomness and regionality. These factors make the energy transfer process in the ore body more complex, and the behavior of crack propagation also has a high degree of uncertainty. However, the prior art usually assumes a homogeneous ore body and ignores the spatial heterogeneity of ore-rock physical parameters and the complex propagation process of blasting energy in heterogeneous ore bodies. Although this simplification can reduce the calculation difficulty, it also greatly reduces the accuracy of the prediction results, resulting in poor performance of the design scheme in actual production. Therefore, those skilled in the art propose a blasting fragment size prediction method based on the ore-rock fragmentation mechanism to solve the above problems. Summary of the Invention

[0005] Aiming at the deficiencies of the prior art, the present invention provides a blasting fragment size prediction method based on the ore-rock fragmentation mechanism, which solves the problem that in the prior art, the prediction of fragment size distribution mostly relies on empirical formulas or simplified physical models.

[0006] To achieve the above object, the present invention is realized through the following technical solutions: A blasting fragment size prediction method based on the ore-rock fragmentation mechanism, comprising the following steps: Ore body modeling and blasting parameter initialization, constructing a three-dimensional mechanical model of the ore body and setting blasting design parameters; Establishing a blasting energy transfer field model, calculating the distribution of blasting energy inside the ore body based on the physical properties of the ore body and the blasting design parameters; Based on the energy transfer field, simulating the crack propagation process, establishing a crack propagation model, and calculating crack distribution parameters; Combining the crack distribution parameters to construct a blasting fragment size distribution model, and fitting the parameters of the model; Verifying the prediction accuracy of the fragment size distribution model through experiments, and optimizing the blasting design parameters; Applying the optimized fragment size distribution prediction model to the actual blasting operation in the mine, and further improving the model based on the feedback data.

[0007] Preferably, the three-dimensional mechanical model of the ore body includes the density, elastic modulus, Poisson's ratio, compressive strength, and tensile strength of the ore body; the blasting design parameters include the blasting hole diameter, hole spacing, row spacing, hole depth, single-hole charge amount, and initiation method.

[0008] Preferably, the blasting energy transfer field model satisfies the following relationship: Where: represents the gradient operator; κ is the energy transfer coefficient of the ore body; E is the energy density; ρ is the density of the ore body; Q is the energy source term.

[0009] Discretely solving the equation by the finite element method to calculate the distribution of the energy density inside the ore body.

[0010] Preferably, the crack propagation model is established based on the relationship between the crack propagation radius and the energy density, and the crack distribution parameters are calculated by the fractal dimension, and the fractal dimension is defined as: Where: D is the fractal dimension of the crack, dimensionless; N is the number of cracks at a certain specific scale, dimensionless; L max is the maximum characteristic length of the crack; L min is the minimum characteristic length of the crack.

[0011] Preferably, the blasting fragment size distribution model is constructed by combining the fractal dimension and the relationship of the fragment size probability density, and the fragment size distribution probability density function is expressed as: where: P(d) is the probability density of ore particles with particle size d, dimensionless; d is the ore particle size; D is the fractal dimension of cracks, dimensionless; α is a fitting parameter related to energy distribution and crack density, dimensionless; β is a normalization constant, dimensionless, used to ensure that the integral value of the probability density function is 1.

[0012] Preferably, the experimental verification includes: Collecting actual fragment size distribution data through laboratory or on-site blasting tests; Fitting the parameters of the fragment size distribution model by the least squares method to minimize the error between the model prediction value and the experimental measurement value; Using an optimization algorithm to adjust the blasting design parameters, with the optimization goal of minimizing the prediction error of the fragment size distribution model.

[0013] Preferably, the feedback data includes the particle size data of the fragment size distribution and its distribution range, and the feedback data is used to iteratively correct the parameters of the fragment size distribution model.

[0014] Preferably, the blasting fragment size prediction method is applicable to mine environments with poor ore body homogeneity or complex physical properties, and can effectively reduce the grinding energy consumption after crushing.

[0015] Preferably, the fitting process of the optimized blasting design parameters and the fragment size distribution model is improved to enhance the crushing uniformity of the ore, improve the ore dressing efficiency and reduce the production cost.

[0016] A blasting fragment size prediction system based on the ore-rock crushing mechanism, comprising: An ore body modeling module for constructing a three-dimensional ore body mechanical model and initializing the blasting design parameters; An energy transfer field simulation module for calculating the distribution of blasting energy density based on ore body parameters and blasting parameters; A crack propagation modeling module for calculating crack propagation based on energy density and determining crack distribution parameters; A fragment size distribution prediction module for constructing a fragment size distribution model in combination with crack distribution parameters and optimizing the model parameters; An experimental verification and optimization module for verifying the prediction accuracy of the fragment size distribution model and optimizing the blasting design parameters; A feedback improvement module for iteratively optimizing the fragment size distribution model by collecting actual production data.

[0017] The present invention provides a blasting fragment size prediction method based on the ore-rock crushing mechanism. It has the following beneficial effects: 1. The present invention adopts a modular technical solution. Through the division of labor and cooperation among the ore body modeling, energy transfer field simulation, crack propagation modeling, fragmentation distribution prediction, and feedback improvement modules, a closed-loop design from theory to practical application is achieved. Compared with the design methods in the prior art that only rely on empirical formulas or single physical models, the present invention not only improves the scientific nature of the design but also can adapt to complex ore body conditions, solving the problem of insufficient accuracy in the prior art when dealing with ore body non-uniformity and the synergistic effect of multiple blasting points.

[0018] 2. Through the accurate calculation of the energy transfer field and crack propagation modeling, the present invention directly correlates the fragmentation behavior of ore and rock with the energy distribution. Combining with the fractal theory, an accurate fragmentation distribution prediction model is constructed. Compared with the technical solutions in the prior art that directly deduce the fragmentation distribution using empirical data, the present invention significantly reduces the fragmentation prediction error, solving the deficiency of large deviation in prediction results of traditional methods under multi-scale ore bodies and complex blasting conditions.

[0019] 3. The present invention introduces an experimental verification and feedback improvement module, dynamically optimizes the fragmentation distribution model and blasting parameters using actual production data, and forms a closed-loop optimization design scheme. Compared with the design methods in the prior art that fix model parameters or perform single optimization, the present invention can adjust the optimization strategy in real time with the change of production conditions, effectively solving the problem of poor adaptability of traditional design methods to dynamic ore body conditions.

[0020] 4. By optimizing the blasting design parameters, the present invention accurately controls the fragmentation distribution of ore, improves the fragmentation uniformity and ore dressing efficiency. Compared with the technical solutions in the prior art that cause resource waste and high grinding energy consumption due to excessive blasting or uneven fragmentation, the present invention significantly reduces energy consumption and costs, solving the deficiency that it is difficult to balance economy and technology in traditional blasting design. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 is a schematic flow chart of the method of the present invention; Figure 2 is a schematic diagram of the system architecture of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0022] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0023] Please refer to the attached Figure 1 , the embodiment of the present invention provides a blasting fragmentation prediction method based on the ore and rock fragmentation mechanism, including the following steps: S1. Ore body modeling and blasting parameter initialization, constructing a three-dimensional mechanical model of the ore body and setting blasting design parameters.

[0024] Specifically, the modeling of the ore body and the initialization of blasting parameters are important links of the present invention, providing basic data support for the subsequent establishment of the energy transfer field and the accurate construction of the fragmentation distribution model. The physical properties of the ore body determine the propagation law of blasting energy, and reasonable blasting parameters can significantly improve the scientificity and accuracy of blasting design. This step involves the analysis of the physical properties of the ore body and the construction of a three-dimensional mechanical model, and at the same time, the initial setting of blasting parameters is carried out in combination with the ore body characteristics to ensure the accuracy and sufficiency of the input conditions for the energy field simulation and the crack propagation model.

[0025] The key to ore body modeling lies in clarifying the physical parameters of the ore body and constructing a three-dimensional mechanical model based on these parameters. Generally, the physical parameters of the ore body include density, elastic modulus, Poisson's ratio, compressive strength, and tensile strength, etc.

[0026] The density ρ of the ore body rock is a parameter that measures the mass per unit volume of the ore, with the unit of kg / m 3 . It directly affects the propagation speed and absorption characteristics of energy in the ore body. The elastic modulus E rock describes the ability of the ore body to undergo elastic deformation under stress, with the unit of Pa, usually determined by ore material experiments. Poisson's ratio v reflects the ratio between the transverse strain and the longitudinal strain of the material, a dimensionless value, generally determined by experiments, and the typical value range is between 0.2 - 0.4. The compressive strength σ c is the stress required for the ore body to reach failure under compression, with the unit of Pa, mainly related to the structural compactness of the ore. And the tensile strength σ t is the stress at which the ore body reaches failure under tension, with the unit of Pa, usually much smaller than the compressive strength.

[0027] The three-dimensional mechanical model of the ore body can be realized by finite element modeling tools (such as ANSYS, COMSOL). In the model, the ore body is discretized into finite elements, and each element is assigned the above physical parameters. By simplifying the ore body into a finite element model with non-uniform material properties, the propagation characteristics of energy in the complex ore body can be fully simulated.

[0028] During the three-dimensional modeling process of the ore body, geological structure information can be introduced, such as fracture distribution, fault location, etc. These factors directly affect the failure behavior of the ore body. The fracture distribution field can be constructed through mine exploration data, and combined with the macroscopic mechanical properties of the ore body, a refined description of the model can be achieved.

[0029] The initialization of blasting parameters is based on the characteristics of the ore body model and is set in combination with blasting design experience. Generally, the blasting hole diameter d boreholeDenote the diameter of the blast hole, in m, usually taking values between 0.1 and 0.3. The hole spacing \(l\) hole and the row spacing \(l\) row are the distances between adjacent blast holes and between adjacent rows of holes respectively, in m, and their values are usually determined according to the ore body fragmentation difficulty and the required block size distribution. The hole depth \(h\) hole is the vertical depth of the blast hole, in m, which needs to be set according to the thickness of the ore body, generally 3 to 10 m. The charge per hole \(q\) charge is the mass of the explosive loaded in each blast hole, in kg, and its value needs to satisfy the balance between the energy required to fragment the ore body and avoiding excessive blasting. The initiation method can be selected as continuous initiation or delay initiation. Continuous initiation is usually applicable to scenarios with high requirements for the ore body fragmentation efficiency, while delay initiation can reduce blasting vibration and optimize the block size distribution. The initiation sequence is adjusted according to the ore body structure, such as initiation from bottom to top or from inside to outside, which can effectively control the fragmentation range and the direction of energy propagation.

[0030] The mathematical model for initializing blasting parameters can be determined through empirical formulas in combination with the mechanical properties of the ore body. For example, the charge can be preliminarily calculated by the following formula: where: \(\rho\) explosive is the density of the explosive, in kg / m 3 ; \(V\) borehole is the volume of the blast hole, in m 3 , which can be calculated from the hole depth and diameter; \(H\) is the heat of explosion of the explosive, in J / kg; \(E\) required is the theoretical energy required for ore body fragmentation, in J.

[0031] Based on the heterogeneity characteristics of the ore body model, the charge of the blast holes can be designed in zones. For example, for high-density areas, the charge per hole is appropriately increased to ensure the fragmentation effect; for low-density areas, the charge is reduced to avoid excessive blasting. This design can be verified and optimized through numerical simulation.

[0032] S2. Establish a blasting energy transfer field model, and calculate the distribution of blasting energy inside the ore body based on the physical properties of the ore body and the blasting design parameters.

[0033] Specifically, the establishment of the energy transfer field is an important part of the method of the present invention, which is directly related to the distribution characteristics of blasting energy inside the ore body. By accurately simulating the propagation path and intensity of energy in the ore body, key input conditions can be provided for subsequent crack propagation modeling and block size distribution prediction. The construction of the energy transfer field is based on the physical parameters of the ore body and the blasting design parameters set in step S1, mainly using the theoretical framework of energy conservation and combining with the finite element numerical calculation method, and finally realizing the accuracy and visualization of the distribution of blasting energy inside the ore body.

[0034] The establishment of the blasting energy transfer field first determines the initial energy distribution of the blasting points based on parameters such as the density, elastic modulus, Poisson's ratio of the ore body, and input parameters such as the hole diameter, hole spacing, row spacing, and single-hole charge set in the blasting design. Generally, the distribution of blasting energy follows the following control equation: Where: denotes the gradient operator, which is used to describe the distribution of energy in space; k is the energy transfer coefficient of the ore body, determined by the thermal conductivity and elastic modulus of the ore body; E is the energy density, with the unit of J / m 3 , representing the energy intensity at a certain point during the blasting process; ρ is the density of the ore body, with the unit of kg / m 3 , and its value is determined by the ore body model; Q is the energy source term, with the unit of J / m 3 , and the specific definition is: Where: ρ explosive is the explosive density, with the unit of kg / m 3 ; H is the explosion heat of the explosive, with the unit of J / kg; V borehole is the volume of the blasting hole, with the unit of m 3 ; V total is the volume of the blasting influence range, with the unit of m 3 .

[0035] The initial condition is set such that the energy density at the blasting point reaches the peak, while the energy density at other positions is zero, to reflect the characteristic of concentrated energy release during blasting. The boundary condition is usually set such that the energy is completely dissipated at the ore body boundary, i.e.: E| boundary = 0 This setting ensures that the energy propagates only within the ore body and does not exceed the ore body boundary.

[0036] The finite element method is used to discretize and solve the above control equation. Generally, the ore body is divided into a finite element grid, and the energy density within each element is approximately a constant value. Through iterative calculation, the energy distribution field E(x, y, z, t) at different time points after blasting is obtained. To improve the calculation accuracy, smaller grid elements and time step Δt can be selected, satisfying the stability condition: Where: h is the size of the grid element, with the unit of m; c is the energy propagation speed, with the unit of m / s, and its value is calculated from the elastic modulus and density of the ore body.

[0037] In some embodiments, there may be multiple blasting points in the ore body, and the initial distribution of energy needs to be calculated by superposition. Specifically, assuming there are multiple blasting points (x i , yi , z i ), the energy density of each blasting point is E i , then the total energy density is: Where: E total (x, y, z) is the total energy density distribution at a certain point (x, y, z) inside the ore body, with the unit of J / m 3 ; n is the total number of blasting points, dimensionless; E i (x, y, z) is the energy density contributed by the i-th blasting point at a certain point (x, y, z), with the unit of J / m 3 ; i is the serial number of the blasting point, dimensionless, and the value range is 1 ≤ i ≤ n; (x, y, z) are the spatial coordinates, representing the lateral position, longitudinal position, and height position inside the ore body respectively, with the unit of m.

[0038] In order to verify the accuracy of the energy transfer field, a small-scale blasting experiment can be carried out. During the experiment, the energy distribution inside the ore body is measured through sensors, and compared with the numerical simulation results to correct the values of parameters such as k and ρ in the model.

[0039] S3. Based on the energy transfer field, simulate the crack propagation process, establish a crack propagation model, and calculate the crack distribution parameters.

[0040] Specifically, crack propagation is the direct manifestation of the blasting effect, and the energy transfer field provides the power source for the formation and propagation of cracks. The present invention combines the energy distribution field obtained in step S2 to establish a crack propagation model, so as to accurately describe the distribution characteristics and propagation law of cracks. The dynamic behavior of crack propagation is comprehensively affected by the material properties of the ore body, the intensity of blasting energy, and the energy propagation path. In this step, the crack propagation process is described through physical equations and fractal mechanics theory, and the complex geometric characteristics of cracks are quantified as the fractal dimension, providing important inputs for subsequent block size distribution modeling.

[0041] The establishment of the crack propagation model is based on the following core assumptions: The blasting energy propagates in the form of waves in the ore body and forms cracks in the local high-stress concentration area. The crack propagation speed is proportional to the energy density, and the shape and distribution of cracks are controlled by the physical properties of the ore body. Generally, the crack propagation radius r(t) satisfies the following relationship: Where: r(t) is the crack propagation radius at time t after blasting, with the unit of m; r0 is the initial crack radius, with the unit of m; γ is the crack propagation coefficient, related to the tensile strength σ of the ore body t , with the unit of m / J; E is the energy density of the blasting point, with the unit of J / m 3 .

[0042] The parameter γ of the crack propagation model can be determined experimentally according to the physical properties of the ore body. In some embodiments, the value range of γ is 10 -4 ~10 -3 m / J. In addition, the initial crack radius r0 depends on the natural fracture density of the ore body and is usually 0.1 - 1 mm. In the high energy density region, the crack propagation speed is faster, while in the low energy density region, the crack gradually stops propagating.

[0043] The inhomogeneity of the ore body can be considered in the crack propagation model. By introducing the regional parameter k(x, y, z) into the energy transfer field, the crack propagation behavior in different regions is described. For example, in the high density region, the k value is large and the crack propagation is restricted, while in the low density region, the k value is small and the crack propagation is more significant.

[0044] The geometry and distribution of cracks can be quantified by the fractal dimension D. The fractal dimension reflects the geometric complexity of the cracks and their distribution law in space. The calculation formula for the fractal dimension is: Where: D is the fractal dimension of the crack, dimensionless; N is the number of cracks at a certain specific scale, dimensionless; L max is the maximum characteristic length of the crack, with the unit of m; L min is the minimum characteristic length of the crack, with the unit of m.

[0045] The value range of the fractal dimension D is from 1.2 to 2.5, and the specific value is determined by the material properties of the ore body and the blasting energy distribution. In some embodiments, the crack distribution data is determined experimentally, and the value of the fractal dimension is fitted to verify the accuracy of the model.

[0046] To achieve the numerical solution of the crack propagation model, the finite difference method is used to discretize the crack propagation radius equation. With the time step Δt as the interval for iterative calculation, the crack propagation behavior can be expressed as: [[ID=2‎7]] Where: r n+1 is the crack radius at the time step (n + 1)Δt; r n is the crack radius at the time step nΔt; γ is the crack propagation coefficient; E n is the energy density at the time step nΔt.

[0047] To simulate the crack distribution in different ore body regions, the energy distribution field E(x, y, z) can be combined with the ore body zoning information, and by adjusting the local γ value, a three-dimensional crack distribution field is generated. At this time, the spatial distribution of the cracks can be expressed by the following relational expression: Where: N(x, y, z) is the number of cracks per unit volume, dimensionless; N0 is the initial crack density, in units of number / m 3 ; r(x, y, z) is the crack propagation radius of the region, in units of m; r0 is the initial crack propagation radius, representing the propagation range of cracks near the blasting point in the initial stage, in units of m.

[0048] To verify the effectiveness of the crack propagation model, the crack distribution image of the ore body can be obtained through high-speed cameras or CT scanning technology in the experiment and compared with the simulation results. In some embodiments, to further improve the applicability of the model, parameters such as γ and r0 can be adjusted to make the model adapt to different types of ore body conditions.

[0049] S4. Construct a fragmentation size distribution model in combination with the crack distribution parameters and fit the parameters of the model.

[0050] Specifically, the fragmentation size distribution model is an important part of the present invention and is used to describe the particle size distribution characteristics of the blasted ore. Step S4, based on the crack propagation model and distribution results obtained in step S3, converts the fragmentation behavior of the ore body into the probability density distribution of particle size, thereby providing a basis for optimizing blasting parameters. The construction of the fragmentation size distribution model comprehensively considers the geometric characteristics of crack distribution, the physical parameters of the ore body, and statistical analysis methods, and can effectively characterize the size and distribution law of particle diameters in the ore body, laying a theoretical foundation for subsequent experimental verification and production applications.

[0051] The fragmentation size distribution model is constructed based on the fractal characteristics of crack distribution, combining fractal theory and probability statistics methods. Generally, the particle size distribution of the blasted ore can be described by a probability density function. Specifically, the probability density function P(d) represents the proportion of ore particles with a particle diameter of d in the overall distribution, and its definition is: Where: P(d) is the probability density of ore particles with a particle diameter of d, dimensionless; d is the ore particle diameter, in units of m; D is the fractal dimension of the crack, dimensionless; α is a fitting parameter related to energy distribution and crack density, dimensionless; β is a normalization constant, dimensionless, used to ensure that the integral value of the probability density function is 1.

[0052] The normalization constant β can be calculated by the following formula: Where: d max is the maximum particle diameter, in units of m, generally related to the geometric characteristics of the blasting area; d minThe minimum particle size, in meters, is usually obtained by measuring experimental data; D is the fractal dimension of the crack, dimensionless, which describes the complexity and distribution law of the crack; α is a fitting parameter, dimensionless, related to the energy distribution and crack propagation behavior; d is the particle size, in meters, representing the size of ore particles of a specific particle size.

[0053] To improve the applicability of the model, α and β can be fitted according to the physical properties of the ore body and blasting parameters. This fitting method usually uses the least squares method to minimize the error between the model prediction value and the experimental measurement value. The fitting objective function is defined as: where: n is the number of experimentally measured particle size data points, dimensionless, representing the specific number of data points of the particle size distribution obtained in the experiment; P measured (d i ) is the actual particle size probability density of the i-th experimental data point, dimensionless, obtained by experimental measurement; P(d i ; α,β) is the probability density of the i-th particle size point calculated by the model, dimensionless, calculated by the fragmentation distribution model with the given fitting parameters α and β; d i is the particle size of the i-th particle size data point, in meters.

[0054] In the complex situation of some ore bodies, the fragmentation distribution may show a bimodal distribution or a multimodal distribution. As a possible implementation, the particle size probability density function can be fitted piecewise. For example, the fitting parameters α and β in different regions can be calculated separately. Through piecewise fitting, the actual fragmentation characteristics of complex ore bodies can be better reflected.

[0055] To further verify the effectiveness of the fragmentation distribution model, parameter optimization can be carried out in combination with experimental data. Generally, in the experiment, the particle size distribution of the blasted ore is obtained by the screening method or image processing technology. The screening method is suitable for larger particle sizes, while the image processing technology can capture the distribution characteristics of smaller particles. By comparing the experimentally measured particle size data and the model prediction value, the model parameters can be adjusted to adapt to different ore body conditions.

[0056] In the case of poor homogeneity of the ore body or uneven crack distribution, the regional fractal dimension D(x,y,z) can be introduced to describe the particle size distribution characteristics of different regions. Specifically, the ore body can be divided into multiple regions, and the fragmentation distribution probability density function is calculated using different fractal dimensions within each region. The calculation method of the regional fractal dimension is the same as that of the global dimension, but it needs to be corrected according to the local crack density data.

[0057] A three-dimensional model of the fragmentation distribution can be constructed based on the three-dimensional energy field and the crack distribution field. At this time, the output of the fragmentation distribution model is not only the particle size probability density function but also the spatial distribution characteristics of the particle size. The mathematical form of the spatial distribution model is as follows: Where: P(d, x, y, z) is the probability density of ore particles with a particle size of d at the position (x, y, z); β(x, y, z) is the local normalization constant, dimensionless; α(x, y, z) is the local fitting parameter, dimensionless.

[0058] To reduce the computational complexity, the three-dimensional model can be reduced in dimension. For example, the fragmentation distribution can be calculated in a certain plane. This method is applicable to ore bodies with significant layered characteristics, such as coal mines or shale mines.

[0059] S5. Verify the prediction accuracy of the fragmentation distribution model through experiments and optimize the blasting design parameters.

[0060] Specifically, experimental verification and optimization are the key links to ensure the accuracy and applicability of the fragmentation distribution model. By comparing the experimental data with the model prediction results, the reliability of the model can be verified, and the model can be corrected for the error part. At the same time, the experimental data can also be used to optimize the blasting parameters to make the actual blasting effect closer to the target fragmentation distribution. This step combines on-site experiments and data fitting methods to provide solid technical support for subsequent industrial applications.

[0061] The experimental verification is divided into two parts: the collection of experimental data and the comparison of the data with the model. Generally, the collection of experimental data is completed through on-site blasting tests, and the test objects are real ore bodies or laboratory specimens with similar mechanical properties. By measuring the ore particle size distribution, the actual data of the fragmentation distribution are obtained. The particle size measurement methods include two methods: the screening method and the image processing method.

[0062] The screening method is applicable to the distribution of larger ore particles and uses multiple layers of sieve meshes for hierarchical screening. Each layer of sieve mesh corresponds to a particle size range. By measuring the mass of the ore after screening on each layer, the mass proportion of particles in different particle size ranges is calculated, so as to obtain the particle size distribution. The particle size calculation formula of the screening method is as follows: Where: P measured (d i ) is the mass proportion of particles with a particle size of d i , dimensionless; m i is the total mass of particles with a particle size range of d i after screening, in kg; M is the total mass of all particles after blasting, in kg.

[0063] The image processing method is applicable to the measurement of a wider range of particle size distributions. By taking images of the ore distribution after blasting, using edge detection algorithms to identify the boundaries of particles, calculating the equivalent diameter of each particle, and finally obtaining particle size distribution data. In some embodiments, in order to improve the accuracy of particle size identification, fractal theory can be combined to correct the image data.

[0064] After the experimental data acquisition is completed, the particle size distribution data is compared and analyzed with the fragmentation distribution model in step S4. By calculating the error between the model prediction value and the experimental measurement value, the fitting effect of the model can be evaluated. The error calculation formula is: where: Err is the model prediction error, dimensionless; n is the number of particle size measurement data points, dimensionless; P measured (d i ) is the particle size probability density measured experimentally, dimensionless; P(d i ; α,β) is the particle size probability density calculated by the model, dimensionless.

[0065] In order to improve the prediction accuracy of the model, the model parameters α and β can be optimized based on experimental data. Specifically, the least squares method is used to adjust the model parameters to minimize the error Err, thereby realizing the dynamic correction of the model.

[0066] On the basis of verifying the model accuracy, the blasting parameters are optimized through experimental data. The optimization goal is to minimize the error between the predicted value of the fragmentation distribution model and the experimental measurement value, while considering the utilization rate of blasting energy and the fragmentation effect. The optimization variables include the blast hole spacing l hole , row spacing l row , charge per hole q charge , etc. The mathematical form of the optimization problem is: where: Err is the error value of the objective function, dimensionless, used to measure the deviation between the predicted fragmentation distribution of the model and the actual data; params is the set of optimization variables, including the blasting parameters q charge and l hole , which are the charge per hole and hole spacing respectively; q min is the minimum allowable value of the charge per hole, in kg, usually set according to the ore body characteristics and fragmentation requirements; q max is the maximum allowable value of the charge per hole, in kg, used to limit the charge to avoid excessive fragmentation or danger; l min is the minimum allowable value of the hole spacing, in m, determined according to the blasting energy coverage range and fragmentation requirements; l max is the maximum allowable value of the hole spacing, in m, used to limit the hole spacing to ensure uniform coverage of the blasting area.

[0067] The genetic algorithm or particle swarm optimization algorithm can be used to solve the above optimization problem. These algorithms can quickly find a parameter combination close to the global optimum by simulating natural evolution or swarm cooperation behavior.

[0068] The optimized blasting design can be reproduced through experiments, the particle size distribution data after blasting can be measured, and compared with the model prediction values again. In some embodiments, to further improve the optimization efficiency, a multi-objective optimization method can be combined, considering both the uniformity of the fragment size distribution and the minimization of blasting vibration.

[0069] S6. Apply the optimized fragment size distribution prediction model to the actual blasting operation in the mine, and further improve the model based on the feedback data.

[0070] Specifically, practical application and feedback improvement are key steps to verify and optimize the effectiveness and applicability of the fragment size distribution model in actual blasting design. After experimental verification and parameter optimization, the optimized model is applied to the actual blasting operation at the mine site. By collecting the fragment size distribution data during the production process, the prediction accuracy of the model and the feasibility of the optimized design are further analyzed. The model is adjusted based on the on-site data feedback to form a dynamic optimization closed-loop, enabling the model to continuously adapt to complex mine conditions and improve the prediction accuracy and design scientificity.

[0071] In the actual application process, first, the on-site blasting design is carried out according to the blasting parameter combination (such as hole diameter, hole spacing, row spacing, single-hole charge amount) optimized in the previous steps, and the optimized fragment size distribution model is applied to the actual mine production. Generally, the ore body in the actual blasting scenario has great inhomogeneity, including local fluctuations in ore body density, irregularities in fracture distribution, etc. These factors may have a certain impact on the prediction accuracy of the model. To reduce these impacts, in this embodiment, the parameter fitting result of the model is continuously optimized by dynamically collecting the fragment size distribution data after actual blasting.

[0072] The measurement method of the fragment size distribution in actual application is similar to that in the experimental verification stage. The screening method or image processing method can be used for data collection. As an option, in the large-scale mine blasting scenario, the image processing method is more applicable. High-resolution images of the ore distribution are obtained through drone aerial photography technology. Combining with image recognition algorithms, the boundary information of ore particles is extracted, and their particle size distribution characteristics are calculated. These data can be used as an important basis for subsequent model correction.

[0073] The statistical results of the on-site fragment size distribution are expressed in the form of a probability density function and compared with the prediction results of the model. The error calculation formula is: where: Err is the error between the in-situ block size distribution and the model prediction, dimensionless; n is the number of in-situ measured particle size data points, dimensionless; P field (d i ) is the particle size probability density measured in-situ, dimensionless; P(d i ; α,β) is the particle size probability density calculated by the model, dimensionless.

[0074] To reduce the possible errors in practical applications, in this embodiment, the fitting parameters of the model are dynamically adjusted through a feedback mechanism. Specifically, based on the in-situ measurement data, the model parameters α and β are refitted, and the normalization constant β is corrected. The objective function for fitting is: where: n is the number of in-situ measured particle size data points, dimensionless; P field (d i ) is the particle size probability density measured in-situ, dimensionless; P(d i ; α,β) is the particle size probability density calculated by the model, dimensionless.

[0075] The dynamic feedback process can combine the measurement data of multiple actual blasting operations, and continuously iterate and optimize the model parameters to make the model adapt to different mine conditions. For example, in an ore body with significantly uneven crack distribution, the local value of the fractal dimension D is adjusted to better reflect the regional block size distribution characteristics.

[0076] To verify the feasibility of the optimized design, further verification can also be carried out through the combination of simulation and experiment. Before actual blasting, numerical simulation is carried out on the optimized parameter combination to predict the block size distribution of the ore after blasting, and it is compared with the in-situ measurement results. If the error between the predicted value and the actual value exceeds the preset range, the blasting design parameters are readjusted and the model fitting results are updated.

[0077] In a complex scenario of multi-point initiation, the adaptability of the model can be further improved through a regionalized model. The ore body is divided into multiple regions, and independent fitting parameters and fractal dimensions are calculated for each region. The regionalized block size distribution model can be expressed as: where: P(d,x,y,z) is the probability density of ore particles with a particle size of d at the position (x,y,z), dimensionless; β(x,y,z) is the local normalization constant, dimensionless; α(x,y,z) is the local fitting parameter, dimensionless; D(x,y,z) is the fractal dimension of the local crack distribution, dimensionless.

[0078] The regionalized model is applicable to ore bodies with obvious geological zoning characteristics, such as layered ore bodies or complex ore body structures with multiple faults.

[0079] A blasting fragment size prediction system based on the ore-rock fragmentation mechanism described below can be correspondingly referred to the a blasting fragment size prediction method based on the ore-rock fragmentation mechanism described above.

[0080] Please refer to the attached Figure 2 , an embodiment of the present invention provides a blasting fragment size prediction system based on the ore-rock fragmentation mechanism, including: An ore body modeling module, configured to build a three-dimensional ore body mechanical model and initialize blasting design parameters; An energy transfer field simulation module, configured to calculate the distribution of blasting energy density based on ore body parameters and blasting parameters; A crack propagation modeling module, configured to calculate crack propagation based on the energy density and determine crack distribution parameters; A fragment size distribution prediction module, configured to build a fragment size distribution model in combination with the crack distribution parameters and optimize the model parameters; An experimental verification and optimization module, configured to verify the prediction accuracy of the fragment size distribution model and optimize the blasting design parameters; A feedback improvement module, configured to iteratively optimize the fragment size distribution model by collecting actual production data.

[0081] Specifically, a blasting fragment size prediction system based on the ore-rock fragmentation mechanism completes the full-process prediction and optimization from ore body modeling to actual blasting data feedback through the coordinated operation of multiple modules, further improving the scientificity of blasting design and the applicability of production practice.

[0082] Ore body modeling module This module is not only used to build a three-dimensional ore body mechanical model, but also includes a comprehensive analysis and characteristic extraction function of the ore body physical parameters. By inputting physical parameters such as the density, elastic modulus, Poisson's ratio, compressive strength, and tensile strength of the ore body, an accurate three-dimensional model is generated.

[0083] Supports the input of different ore body geometries and geological structure characteristics, such as fracture distribution and fault information, and can provide a more detailed description of the ore body structure for subsequent modules.

[0084] The module supports direct data interaction with ore body exploration data acquisition equipment (such as ground penetrating radar or CT scanning equipment) to quickly update the model.

[0085] Energy transfer field simulation module This module accurately calculates the three-dimensional distribution of blasting energy density based on ore body parameters and blasting design parameters. In addition to the basic energy propagation simulation, it can also calculate the superposition effect of energy at different blasting points.

[0086] Supports dynamic simulation of multi-point initiation, simulates and analyzes the setting of blasting time delay, and visualizes the energy transfer path and high-energy area distribution.

[0087] It can output a three-dimensional energy distribution field and provide it to subsequent modules for calling in a visual form. At the same time, it supports the dynamic adjustment of energy transfer parameters (such as energy dissipation coefficient).

[0088] Crack propagation modeling module Based on the energy transfer field, this module simulates the formation and propagation process of cracks and determines the spatial distribution parameters of cracks. It describes the geometric characteristics of cracks through fractal theory and completes the calculation of the complex distribution of cracks.

[0089] It has added the modeling function for multi-scale cracks and supports simulating the distribution characteristics of micro-cracks and macro-fractures. At the same time, it can automatically adjust the crack propagation radius and fractal dimension according to the energy density in different regions, providing the ability of zoning modeling.

[0090] It supports the output of the three-dimensional distribution map of cracks, as well as key parameters such as fractal dimension and crack density, providing input for the subsequent fragment size distribution prediction module.

[0091] Fragment size distribution prediction module By combining crack distribution parameters and ore body fragmentation mechanism, it constructs a fragment size distribution model and optimizes the model parameters. The module can simulate the fragment size distribution under different blasting designs, providing a basis for optimization.

[0092] It supports the prediction of fragment size distribution for special ore body structures (such as stratified ore bodies or regions with complex rock inclusions) and models the synergistic effect of multiple blast holes. The module can select different probability density functions (such as fractal model or normal distribution model) to adapt to different ore body conditions.

[0093] The module can output the fragment size distribution prediction results in the form of charts or distribution curves, and directly dock with the optimization module, supporting multiple rounds of optimization iterations.

[0094] Experimental verification and optimization module Based on actual experimental data, this module verifies the prediction accuracy of the fragment size distribution model and optimizes the model and blasting parameters. It supports fitting methods such as the least squares method to dynamically correct the model.

[0095] It has added the integration ability with on-site blasting monitoring equipment, such as vibration sensors or high-speed cameras, to collect blasting effect data in real time. At the same time, it supports the automatic generation of experimental reports, including prediction errors, fitting parameters and optimization results.

[0096] The module can directly call the results of the fragment size distribution prediction module for verification and feedback the optimized design parameters to the ore body modeling module to achieve closed-loop operation of the whole system.

[0097] Feedback improvement module Based on the data from production practice, this module further optimizes the block size distribution model. Through iterative updates, it ensures that the model adapts to different ore body conditions, improving the accuracy of prediction and the applicability of design.

[0098] It supports collecting multi-round production data and dynamically adjusts the key parameters of the model, such as fractal dimension, normalization constant, and fitting parameters, through machine learning algorithms. At the same time, it can identify changes in ore body conditions (such as density or fracture distribution) and automatically adjust the model structure.

[0099] It is docked with the on-site monitoring system to collect data in real time and work collaboratively with other modules to complete automatic updates and optimizations. The module outputs include optimized model parameters, design suggestions, and improvement records.

[0100] Although the embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A blasting fragment size prediction method based on the ore-rock fragmentation mechanism, characterized in that, It includes the following steps: Ore body modeling and blasting parameter initialization, constructing a three-dimensional mechanical model of the ore body and setting blasting design parameters; Establishing a blasting energy transfer field model, calculating the distribution of blasting energy inside the ore body based on the physical properties of the ore body and the blasting design parameters; Based on the energy transfer field, simulating the crack propagation process, establishing a crack propagation model, and calculating crack distribution parameters; Combining the crack distribution parameters to construct a blasting fragment size distribution model and fitting the parameters of the model; Verifying the prediction accuracy of the fragment size distribution model through experiments and optimizing the blasting design parameters; Applying the optimized fragment size distribution prediction model to the actual blasting operation in the mine and further improving the model based on the feedback data.

2. The blasting fragment size prediction method based on the ore and rock fragmentation mechanism according to claim 1, characterized in that, The three-dimensional mechanical model of the ore body includes the density, elastic modulus, Poisson's ratio, compressive strength, and tensile strength of the ore body; the blasting design parameters include the blasting hole diameter, hole spacing, row spacing, hole depth, single-hole charge amount, and initiation method.

3. A blasting fragment size prediction method based on the ore-rock fragmentation mechanism according to claim 1, characterized in that, The blasting energy transfer field model satisfies the following relationship: Wherein: represents the gradient operator; κ is the energy transfer coefficient of the ore body; E is the energy density; ρ is the density of the ore body; Q is the energy source term; Discretely solving the equation by the finite element method to calculate the distribution of energy density inside the ore body.

4. A blasting fragment size prediction method based on the ore-rock fragmentation mechanism according to claim 1, characterized in that, The crack propagation model is established based on the relationship between the crack propagation radius and the energy density. The distribution parameters of the cracks are calculated by the fractal dimension, and the fractal dimension is defined as: Where: D is the fractal dimension of the crack, dimensionless; N is the number of cracks at a specific scale, dimensionless; L max is the maximum characteristic length of the crack; L min is the minimum characteristic length of the crack.

5. A blasting fragment size prediction method based on the ore and rock fragmentation mechanism according to claim 1, characterized in that, The blasting fragment size distribution model is constructed by combining the fractal dimension and the relationship of the fragment size probability density. The fragment size distribution probability density function is expressed as: Where: P(d) is the probability density of ore particles with a particle size of d, dimensionless; d is the ore particle size; D is the fractal dimension of the crack, dimensionless; α is a fitting parameter related to the energy distribution and crack density, dimensionless; β is a normalization constant, dimensionless, used to ensure that the integral value of the probability density function is 1.

6. The blasting fragment size prediction method based on the ore-rock fragmentation mechanism according to claim 1, wherein, The experimental verification includes: Collecting actual fragment size distribution data through laboratory or on-site blasting tests; Fitting the parameters of the fragment size distribution model by the least squares method to minimize the error between the model prediction value and the experimental measurement value; Using an optimization algorithm to adjust the blasting design parameters, with the optimization goal of minimizing the prediction error of the fragment size distribution model.

7. A blasting fragment size prediction method based on the ore-rock fragmentation mechanism according to claim 1, characterized in that The feedback data includes the particle size data of the fragment size distribution and its distribution range, and the feedback data is used to iteratively correct the parameters of the fragment size distribution model.

8. A blasting fragment size prediction method based on the ore-rock fragmentation mechanism according to claim 1, characterized in that, The blasting fragment size prediction method is applicable to mine environments with poor ore body homogeneity or complex physical properties and can effectively reduce the grinding energy consumption after crushing.

9. A blasting fragment size prediction method based on the ore-rock fragmentation mechanism according to claim 1, characterized in that The fitting process of the optimized blasting design parameters and the fragment size distribution model improves the crushing uniformity of the ore, enhances the beneficiation efficiency, and reduces the production cost.

10. A blasting fragment size prediction system based on the ore-rock fragmentation mechanism is applied to a blasting fragment size prediction method based on the ore-rock fragmentation mechanism according to any one of claims 1-9, characterized in that, It includes: An ore body modeling module for constructing a three-dimensional ore body mechanical model and initializing blasting design parameters; An energy transfer field simulation module for calculating the distribution of blasting energy density based on ore body parameters and blasting parameters; A crack propagation modeling module for calculating crack propagation based on energy density and determining crack distribution parameters; A fragment size distribution prediction module for constructing a fragment size distribution model by combining crack distribution parameters and optimizing the model parameters; An experimental verification and optimization module for verifying the prediction accuracy of the fragment size distribution model and optimizing the blasting design parameters; A feedback improvement module for iteratively optimizing the fragment size distribution model by collecting actual production data.

Images