A method, system and device for optimizing parameters of millisecond blasting in a large open-pit mine

Abstract

This invention discloses a method, system, and equipment for optimizing micro-delay blasting parameters in a large open-pit mine, belonging to the field of engineering blasting technology. The method includes: establishing a blasting parameter optimization model with the goal of minimizing blasting costs; adding constraints to the blasting parameter optimization model; calculating the step size and gradient according to the maximum principle, and iterating the step size and gradient a finite number of times to obtain an optimized blasting parameter scheme; using Aegis mining blasting software, simulating a single detonation test of the open-pit mine area to be blasted according to the optimized blasting parameter scheme and the order of detonation, obtaining simulated blasting effect data; collecting blasting effect data of the same category as the open-pit mine area to be blasted, and comparing it with the simulated blasting effect data; when the comparison result is greater than a preset threshold, weighting the added constraints, and recalculating and iterating the step size and gradient until the optimal blasting parameter optimization scheme is obtained and output, thereby improving the blasting effect.

Description

Technical Field

[0001] This invention relates to the field of engineering blasting technology, and more specifically to a method, system and equipment for optimizing micro-differential blasting parameters in large-scale open-pit mines. Background Technology

[0002] In traditional open-pit mining, conventional blasting methods are typically used, which involve loading all blast holes with explosives and detonating them simultaneously. While this method is simple and easy to implement, it suffers from problems such as uneven energy distribution, poor blasting effectiveness, and high safety risks.

[0003] To address these issues, a large-area micro-delay blasting method was proposed. Also known as multi-delay blasting, this method employs micro-delay controlled blasting technology, detonating multiple holes in a single, sequential manner over a large area to achieve a large blast volume and improve blasting efficiency. Simultaneously, micro-delay controlled blasting can also reduce safety risks and increase mining efficiency.

[0004] However, in practice, existing open-pit mine micro-delay blasting cannot reasonably select blasting parameters. They are usually selected based on experience or calculated manually. To a large extent, the problems of low blasting efficiency and high blasting cost are caused by errors in experience selection or manual calculation.

[0005] Therefore, how to provide a method, system, and equipment for optimizing micro-differential blasting parameters in large-scale open-pit mines is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0006] In view of this, the present invention provides a method, system and equipment for optimizing micro-differential blasting parameters in a large open-pit mine, thereby solving the technical problems existing in the prior art.

[0007] To achieve the above objectives, the present invention provides the following technical solution:

[0008] A method for optimizing micro-differential blasting parameters in a large open-pit mine includes:

[0009] S100: Establish a blasting parameter optimization model with the goal of minimizing blasting costs;

[0010] S200: Add constraints to the blasting parameter optimization model;

[0011] S300: Calculate the step size and gradient according to the maximum principle, and perform a finite number of iterations on the step size and gradient to obtain the blasting parameter optimization scheme;

[0012] S400: Using Aegis mining blasting software, based on the blasting parameter optimization scheme, a single blasting test is simulated in the open-pit mine area to be blasted according to the sequential blasting order to obtain simulated blasting effect data;

[0013] S500: Collect blasting effect data of the same type as the open-pit mine area to be blasted, and compare it with the simulated blasting effect data. When the comparison result is greater than the preset threshold, return to S200 to perform weighted processing on the added constraints, and recalculate and iterate the step size and gradient until the optimal blasting parameter optimization scheme is obtained and then output.

[0014] Optionally, the blasting parameter optimization model includes:

[0015] minC(C d b, a, w d Q, L) = C1 + C2 + C3 + C4;

[0016] In the formula, C d , where b is the rock expansion buffer coefficient, a is the row spacing, and w is the hole spacing. d The chassis resistance line is represented by Q, the total charge amount, L, the charge length, C1, the cost of the detonator, C2, the cost of the explosive, C3, the drilling cost, and C4, the construction cost.

[0017] Optionally, the added constraints include constraints on the rock expansion buffer coefficient:

[0018] C d -ε f / (ε f -ε d )≤0;

[0019] In the formula, ε f ε is the axial strain at the peak stress point. d The axial strain is the initial expansion point of the volume.

[0020] Optionally, the added constraints include constraints on the chassis resistance line:

[0021] w d ≤(25-45)d;

[0022] In the formula, d is the diameter of the projectile.

[0023] Optionally, the added constraints include constraints on hole spacing:

[0024]

[0025] In the formula, e is the charge height, p is the charge amount per meter, q is the explosive consumption per unit, and w d The chassis resistance line is H, the stage height is a. max This is the upper limit of the hole spacing constraint.

[0026] Optionally, the added constraints include constraints on the amount of propellant fired simultaneously:

[0027] Q = R 1 / 3 (V / K) 1 / 1.65 ≤Q min ;

[0028] In the formula, R is the shortest distance from the open-pit mine to the safe zone, V is the maximum allowable particle vibration velocity in the safe zone, K is a constant that varies with the structure of the blasted rock mass, and Q... min To ensure the safe charge amount for simultaneous blasting.

[0029] Optionally, step S300 involves calculating the step size and gradient based on the maximum principle, and performing a finite number of iterations on the step size and gradient to obtain an optimized blasting parameter scheme, including:

[0030] S310: Add constraints to the blasting parameter optimization model and establish the Hamiltonian function;

[0031] S320: Obtain the control conditions based on the Hamiltonian function;

[0032] S330: Calculate the upper and lower bounds of the control conditions according to the maximum principle, and obtain the step size and gradient according to the Lagrange multipliers;

[0033] S340: Perform a finite number of iterations on the step size and the gradient to obtain an optimized blasting parameter scheme.

[0034] Optionally, step S340 involves performing a finite number of iterations on the step size and the gradient to obtain an optimized blasting parameter scheme, including:

[0035] S341: Calculate the upper and lower bounds of the next round of iteration constraints for the variable parameters of the current control conditions, and obtain the current step size and gradient based on the Lagrange multipliers;

[0036] S342: After mapping the current variable parameters to the upper and lower bounds of the constraints, determine whether all constraints are satisfied. If yes, output the result; otherwise:

[0037] S343: Return to S342 until the upper and lower bounds of the current constraint satisfy all constraint conditions and output.

[0038] A system for optimizing micro-differential blasting parameters in a large open-pit mine includes:

[0039] The model building module establishes a blasting parameter optimization model with the goal of minimizing blasting costs.

[0040] The parameter optimization module adds constraints to the blasting parameter optimization model;

[0041] The calculation module calculates the step size and gradient according to the maximum principle, and performs a finite number of iterations on the step size and gradient to obtain the blasting parameter optimization scheme.

[0042] The simulation module, based on Aegis mining blasting software, simulates a single blasting test of an open-pit mine area to be blasted according to the optimized blasting parameter scheme and the sequential blasting order, and obtains simulated blasting effect data.

[0043] The output module collects blasting effect data of the same type as the open-pit mine area to be blasted, and compares it with the simulated blasting effect data. When the comparison result is greater than the preset threshold, it returns to S200 to perform weighted processing on the added constraints, and recalculates and iterates the step size and gradient until the optimal blasting parameter optimization scheme is obtained and then outputs it.

[0044] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement a method for optimizing micro-differential blasting parameters in a large open-pit mine.

[0045] As can be seen from the above technical solution, compared with the prior art, the present invention discloses a method, system and equipment for optimizing micro-differential blasting parameters in a large open-pit mine. Under the premise of ensuring normal mine production, it aims to minimize blasting costs, thereby achieving the goal of saving production costs. Through iterative calculations of gradients and step sizes, all constraints are ultimately satisfied, simplifying the cumbersome iterative process and obtaining an optimized blasting parameter scheme. Furthermore, by comparing the blasting effects with those of similar types through experiments, the optimal blasting parameter optimization scheme is obtained, effectively improving the accuracy and scientific validity of the blasting parameter optimization scheme. Attached Figure Description

[0046] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0047] Figure 1 This is a schematic diagram of the method flow of the present invention;

[0048] Figure 2 This is a schematic diagram of the system structure of the present invention. Detailed Implementation

[0049] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0050] The purpose of this invention is to provide a method, system, and equipment for optimizing micro-delay blasting parameters in a large open-pit mine. The method includes: establishing a blasting parameter optimization model with the goal of minimizing blasting costs; adding constraints to the blasting parameter optimization model; calculating the step size and gradient according to the maximum principle, and iterating the step size and gradient a finite number of times to obtain an optimized blasting parameter scheme; using Aegis mining blasting software, simulating a single blasting test of the open-pit mine area to be blasted according to the blasting parameter optimization scheme and the blasting effect data obtained by sequential detonation; collecting blasting effect data of the same category as the open-pit mine area to be blasted, and comparing it with the simulated blasting effect data; when the comparison result is greater than a preset threshold, weighting the added constraints, and recalculating and iterating the step size and gradient until the optimal blasting parameter optimization scheme is obtained and output. This provides a solution to the problems of low blasting efficiency and high blasting costs caused by incorrect experience selection or manual calculation errors in the prior art.

[0051] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0052] On the one hand, see Figure 1 This invention discloses a method for optimizing micro-differential blasting parameters in a large open-pit mine, comprising:

[0053] S100: Establish a blasting parameter optimization model with the goal of minimizing blasting costs;

[0054] S200: Add constraints to the blasting parameter optimization model;

[0055] S300: Calculate the step size and gradient according to the maximum principle, and perform a finite number of iterations on the step size and gradient to obtain the blasting parameter optimization scheme;

[0056] S400: Using Aegis mining blasting software, based on the blasting parameter optimization scheme, a single blasting test is simulated in the open-pit mine area to be blasted according to the sequential blasting order to obtain simulated blasting effect data;

[0057] S500: Collects blasting effect data of the same type as the open-pit mine area to be blasted, and compares it with the simulated blasting effect data. When the comparison result is greater than the preset threshold, it returns to the parameter optimization module to perform weighted processing on the added constraints, and recalculates and iterates the step size and gradient until the optimal blasting parameter optimization scheme is obtained and then outputs it.

[0058] This invention enables dynamic optimization of the corresponding blasting parameters, improving the practicality, accuracy, and timeliness of blasting parameter optimization, and ensuring that the final blasting effect is close to the ideal blasting effect.

[0059] In one specific embodiment, the blasting parameter optimization model includes:

[0060] minC(C d b, a, w d Q, L) = C1 + C2 + C3 + C4;

[0061] In the formula, C d , where b is the rock expansion buffer coefficient, a is the row spacing, and w is the hole spacing. d The chassis resistance line is represented by Q, the total charge amount, L, the charge length, C1, the cost of the detonator, C2, the cost of the explosive, C3, the drilling cost, and C4, the construction cost.

[0062] In a specific instance of adding constraints, the constraint for the rock expansion buffer coefficient is included:

[0063] C d -ε f / (ε f -ε d )≤0;

[0064] In the formula, ε f ε is the axial strain at the peak stress point. d The axial strain is the initial expansion point of the volume.

[0065] In a specific instance of adding constraints, the chassis resistance line constraint is included:

[0066] w d ≤(25-45)d;

[0067] In the formula, d is the diameter of the projectile.

[0068] In a specific instance of adding constraints, there is a constraint on the hole spacing:

[0069]

[0070] In the formula, e is the charge height, p is the charge amount per meter, q is the explosive consumption per unit, and w d The chassis resistance line is H, the stage height is a. max This is the upper limit of the hole spacing constraint.

[0071] In a specific instance of adding constraints, there is a constraint on the amount of propellant fired simultaneously:

[0072] Q = R 1 / 3 (V / K) 1 / 1.65 ≤Q min ;

[0073] In the formula, R is the shortest distance from the open-pit mine to the safe zone, V is the maximum allowable particle vibration velocity in the safe zone, K is a constant that varies with the structure of the blasted rock mass, and Q... min To ensure the safe charge amount for simultaneous blasting.

[0074] In one specific embodiment, S300: Calculate the step size and gradient according to the maximum principle, and perform a finite number of iterations on the step size and gradient to obtain an optimized blasting parameter scheme, including:

[0075] S310: Add constraints to the blasting parameter optimization model and establish the Hamiltonian function, specifically:

[0076] The parameters of the blasting parameter optimization model and the existing Kuz-Ram prediction model are used to obtain:

[0077]

[0078]

[0079] In the formula, X 50 For the average unbroken piece size, C d Let q be the rock expansion buffer coefficient, q be the explosive consumption per unit, Q be the salvo charge, E be the relative weight yield of the ammunition, and w be the explosive yield per unit. d δ represents the chassis resistance line, L represents the charge length, H represents the stage height, P1 and p2 are correction factors, and δ represents the rock structure factor.

[0080] The established expression for the Hamiltonian function is:

[0081]

[0082]

[0083] In solving the Hamiltonian function, let y(*)=(C d b, a, w d Given , Q, L), find the partial derivatives.

[0084] S320: The control conditions are obtained based on the Hamiltonian function;

[0085] Specifically, the control conditions include: governing equations, state equations, Euler equations, transverse conditions, and complementary relaxation conditions.

[0086] S330: Calculate the upper and lower bounds of the control quantity according to the maximum principle, and obtain the step size and gradient according to the Lagrange multipliers;

[0087] S340: By performing a finite number of iterations on the step size and gradient, an optimized scheme for the blasting parameters is obtained, including:

[0088] S341: Calculate the upper and lower bounds of the next round of iteration constraints for the variable parameters of the current control conditions, and obtain the current step size and gradient based on the Lagrange multipliers;

[0089] Specifically, the Hamilton multipliers are solved based on the transversal condition and the Euler equation.

[0090] S342: After mapping the current variable parameters to the upper and lower bounds of the constraints, determine whether all constraints are satisfied. If yes, output the result; otherwise:

[0091] S343: Return to S342 until the upper and lower bounds of the current constraint satisfy all constraint conditions and output.

[0092] The above technical solution can achieve rapid convergence.

[0093] On the other hand, see Figure 2 This invention discloses a system for optimizing micro-differential blasting parameters in a large open-pit mine, comprising:

[0094] The model building module establishes a blasting parameter optimization model with the goal of minimizing blasting costs.

[0095] The parameter optimization module adds constraints to the blasting parameter optimization model.

[0096] The calculation module calculates the step size and gradient according to the maximum principle, and performs a finite number of iterations on the step size and gradient to obtain the optimization scheme of the blasting parameters.

[0097] The simulation module, based on Aegis mining blasting software, simulates a single blasting test of an open-pit mine area to be blasted according to the optimized blasting parameter scheme and the sequential blasting order, and obtains simulated blasting effect data.

[0098] The output module collects blasting effect data of the same type as the open-pit mine area to be blasted, and compares it with the simulated blasting effect data. When the comparison result is greater than the preset threshold, it returns to the parameter optimization module to perform weighted processing on the added constraints, and recalculates and iterates the step size and gradient until the optimal blasting parameter optimization scheme is obtained and then outputs it.

[0099] In another aspect, embodiments of the present invention disclose an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement a method for optimizing micro-differential blasting parameters in a large open-pit mine.

[0100] The system apparatus disclosed in the embodiments is described simply because it corresponds to the method disclosed in the embodiments; relevant details can be found in the method section.

[0101] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to in the method section.

[0102] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for optimizing micro-delay blasting parameters in a large open-pit mine, characterized in that, include: S100: Establish a blasting parameter optimization model with the goal of minimizing blasting costs; The blasting parameter optimization model includes: In the formula, Here, b is the rock expansion buffer coefficient, a is the row spacing, and a is the hole spacing. The chassis resistance line is represented by Q, the total charge quantity (Q) for simultaneous firing is Q, and the charge length is L. Due to the cost of the detonator, For the cost of explosives, For drilling costs, For construction costs; S200: Add constraints to the blasting parameter optimization model, as follows: Constraints on the rock expansion buffer coefficient: In the formula, The axial strain at the peak stress point The axial strain is the initial expansion point of the volume. Chassis resistance line constraints: In the formula, d is the diameter of the projectile; Hole spacing constraints: In the formula, e is the charge height, p is the charge amount per meter, and q is the explosive consumption per unit. H represents the chassis resistance line, and H represents the stage height. This serves as the upper limit for hole spacing constraints. Constraints on the amount of propellant fired simultaneously: In the formula, R is the shortest distance from the open-pit mine to the safe zone, V is the maximum allowable particle vibration velocity in the safe zone, and K is a constant that varies with the structure of the blasted rock mass. To ensure the safe charge quantity for simultaneous blasting; S300: Calculate the step size and gradient according to the maximum principle, and perform a finite number of iterations on the step size and gradient to obtain the blasting parameter optimization scheme; S400: Using Aegis mining blasting software, based on the blasting parameter optimization scheme, a single blasting test is conducted on the open-pit mine area to be blasted according to the sequential blasting order to obtain simulated blasting effect data; S500: Collect blasting effect data of the same type as the open-pit mine area to be blasted, and compare it with the simulated blasting effect data. When the comparison result is greater than the preset threshold, return to S200 to perform weighted processing on the added constraints, and recalculate and iterate the step size and gradient until the optimal blasting parameter optimization scheme is obtained and then output.

2. The method for optimizing micro-delay blasting parameters in a large open-pit mine area according to claim 1, characterized in that, S300: Calculates the step size and gradient according to the maximum principle, and performs a finite number of iterations on the step size and gradient to obtain an optimized blasting parameter scheme, including: S310: Add constraints to the blasting parameter optimization model and establish the Hamiltonian function; S320: The control conditions are obtained based on the Hamiltonian function; the control conditions include: control equations, state equations, Euler equations, transverse conditions, and complementary relaxation conditions; S330: Calculate the upper and lower bounds of the control conditions according to the maximum principle, and obtain the step size and gradient according to the Lagrange multipliers; S340: Perform a finite number of iterations on the step size and the gradient to obtain an optimized blasting parameter scheme.

3. The method for optimizing micro-delay blasting parameters in a large open-pit mine area according to claim 2, characterized in that, S340: Performing a finite number of iterations on the step size and the gradient to obtain an optimized blasting parameter scheme, including: S341: Calculate the upper and lower bounds of the next round of iteration constraints for the variable parameters of the current control conditions, and obtain the current step size and gradient based on the Lagrange multipliers; S342: After mapping the current variable parameters to the upper and lower bounds of the constraints, determine whether all constraints are satisfied. If yes, output the result; otherwise: S343: Return to S342 until the upper and lower bounds of the current constraint satisfy all constraint conditions and output.

4. A system for optimizing micro-delay blasting parameters in open-pit mines using the method for optimizing micro-delay blasting parameters in large-scale open-pit mines according to any one of claims 1-3, characterized in that, include: The model building module establishes a blasting parameter optimization model with the goal of minimizing blasting costs. The blasting parameter optimization model includes: In the formula, Here, b is the rock expansion buffer coefficient, a is the row spacing, and a is the hole spacing. The chassis resistance line is represented by Q, the total charge quantity (Q) for simultaneous firing is Q, and the charge length is L. Due to the cost of the detonator, For the cost of explosives, For drilling costs, For construction costs; The parameter optimization module adds constraints to the blasting parameter optimization model, as follows: Constraints on the rock expansion buffer coefficient: In the formula, The axial strain at the peak stress point The axial strain is the initial expansion point of the volume. Chassis resistance line constraints: In the formula, d is the diameter of the projectile; Hole spacing constraints: In the formula, e is the charge height, p is the charge amount per meter, and q is the explosive consumption per unit. H represents the chassis resistance line, and H represents the stage height. This serves as the upper limit for hole spacing constraints. Constraints on the amount of explosives fired simultaneously: In the formula, R is the shortest distance from the open-pit mine to the safe zone, V is the maximum allowable particle vibration velocity in the safe zone, and K is a constant that varies with the structure of the blasted rock mass. To ensure the safe charge quantity for simultaneous blasting; The calculation module calculates the step size and gradient according to the maximum principle, and performs a finite number of iterations on the step size and gradient to obtain the blasting parameter optimization scheme. The simulation module, based on Aegis mining blasting software, simulates a single blasting test of an open-pit mine area to be blasted according to the optimized blasting parameter scheme and the blasting sequence, and obtains simulated blasting effect data. The output module collects blasting effect data of the same type as the open-pit mine area to be blasted, and compares it with the simulated blasting effect data. When the comparison result is greater than a preset threshold, it returns to the parameter optimization module to perform weighted processing on the added constraints, and recalculates and iterates the step size and gradient until the optimal blasting parameter optimization scheme is obtained and then outputs it.

5. An electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method for optimizing micro-differential blasting parameters in a large open-pit mine area as described in any one of claims 1 to 3.

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