Three-soft coal seam mining area arrangement optimization method, medium and system

Abstract

The invention provides a three-soft coal seam mining area arrangement optimization method, medium and system, and belongs to the technical field of three-soft coal seam mining area arrangement, and the method comprises the steps: firstly collecting detailed geological, hydrological and mining condition data, and obtaining a plurality of feasible development-mining system schemes; and then based on a numerical simulation means, mining processes, mining area stability and water disaster risks under different schemes are evaluated. And solving the optimization model by adopting a multi-objective optimization algorithm to obtain a Pareto optimal solution set. And finally, discretizing the Pareto solution to form a multi-dimensional state space, optimizing the state space by using an improved grey wolf hunting algorithm, and finally obtaining an optimal mining area arrangement scheme. According to the method, multi-source data are fully utilized, analog calculation and optimization algorithms are fused, and effective technical support is provided for safe and efficient mining of the three-soft coal seam. The technical problem that the optimization effect is limited due to the fact that the coupling relation between a mining system and an excavation system is ignored in the prior art is solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of three-soft coal seam mining area layout, and in particular, relates to a three-soft coal seam mining area layout optimization method, medium and system. Background Art

[0002] As one of China's most important energy resources, coal plays an important role in the development of the national economy. Among them, the three soft coal seams have always been a major challenge facing the coal industry due to their low coal quality, high mining difficulty and strong hydrogeological influence. In order to achieve efficient mining of the three soft coal seams, it is crucial to formulate a reasonable mining area layout plan. The existing three soft coal seam mining optimization methods mainly focus on single-objective optimization, such as only considering factors such as economic benefits, safety or environmental impact, and cannot comprehensively evaluate the comprehensive performance of the mining plan. In addition, these methods usually use empirical evaluation criteria, which is difficult to objectively reflect the complex geological, hydrological and technological factors in the actual mining process. At the same time, most optimization methods only optimize the development system or the mining system, ignoring the coupling relationship between the two, resulting in limited optimization effects. Summary of the invention

[0003] In view of this, the present invention provides a method, medium and system for optimizing the layout of mining areas of three-soft coal seams, which can solve the technical problem that the existing layout optimization method of three-soft coal seams only optimizes the development system or the mining system, ignoring the coupling relationship between the two, resulting in limited optimization effect.

[0004] The present invention is achieved in that:

[0005] A first aspect of the present invention provides a method for optimizing the layout of a three-soft coal seam mining area, which comprises the following steps:

[0006] S10. Collect geological data, hydrological data and mining condition data of three soft coal seams;

[0007] S20, obtaining a plurality of pre-designed feasible development-mining system solutions, including shaft location, tunnel layout, coal mining technology and mining area sequence;

[0008] S30, establishing a joint optimization model of the development-mining system taking into account geological data, hydrological data and mining condition data as well as the development-mining system scheme, including a coal mining cost function, a production function, a safety constraint function and an environmental impact function;

[0009] S40, simulating and calculating the mining process for each development-mining system scheme to obtain simulation indicators, including coal mining face advancement speed, recovery rate, roof management effect and surface settlement;

[0010] S50. Use numerical simulation methods to analyze stress distribution, surrounding rock deformation and changes in hydrogeological conditions under different development-mining system schemes, and evaluate the stability of the mining area and water hazard risks;

[0011] S60, based on a multi-objective optimization algorithm, determining multiple optimization indicators of the development-mining system joint optimization model, solving the development-mining system joint optimization model, and obtaining a set of Pareto optimal solutions;

[0012] S70, discretizing the obtained Pareto optimal solution to form a multi-dimensional state space, where each dimension represents an optimization index, and each point in the state space represents a possible mining area layout plan;

[0013] S80. Use the multi-dimensional improved gray wolf hunting algorithm to optimize the discretized state space and obtain the optimal mining area layout plan.

[0014] On the basis of the above technical solution, the method for optimizing the layout of three-soft coal seam mining areas of the present invention can also be improved as follows:

[0015] The multiple optimization indicators of the joint optimization model of the development-mining system are determined, specifically including: economic benefit indicators, safety indicators and environmental impact indicators.

[0016] Furthermore, the constraint conditions for determining the joint optimization model of the development-mining system specifically include: mining area boundary constraints, minimum mining area constraints, maximum mining area constraints, tunnel spacing constraints, working face length constraints and coal mining process constraints.

[0017] Furthermore, the step of optimizing the discretized state space using the multi-dimensional improved gray wolf hunting algorithm is specifically:

[0018] Map each individual gray wolf to a point in the state space;

[0019] Design a fitness function based on the optimization index to evaluate the fitness of each individual gray wolf;

[0020] Introducing a multi-dimensional search strategy enables the wolf pack to efficiently explore in high-dimensional state space;

[0021] Design an adaptive weight adjustment mechanism to balance local search and global search;

[0022] By updating the gray wolf's position through multiple iterations, the optimal solution of the target is finally obtained, that is, the optimal mining area layout plan.

[0023] Furthermore, the geological data include coal seam thickness, inclination, top and bottom plate lithology, fault distribution, coal seam burial depth and coal quality parameters.

[0024] Furthermore, the hydrological data include aquifer location, hydrogeological unit division, groundwater flow direction and flow, and water quality characteristics.

[0025] Furthermore, the mining condition data includes the scope of mining rights, distribution of surface buildings, transportation conditions, topographical features and environmentally sensitive areas.

[0026] Furthermore, the multi-objective optimization algorithm adopts NSGA-II algorithm.

[0027] Further, the following is a detailed description of the functions in the present invention:

[0028] 1. Coal mining cost function:

[0029]

[0030] In the formula, C t is the total mining cost; C d is the direct mining cost; C m is the material cost; C t is the transportation cost; C e is the equipment cost; C r is the repair cost; Q i is the mining volume of the i-th mining area; n is the number of mining areas; ε c is the cost error term.

[0031] Parameter acquisition method: C d ,C m ,C t ,C e ,C r Obtained through historical data analysis and expert evaluation;Q i Determined through geological reserve calculations and mining area design.

[0032] 2. Yield function:

[0033]

[0034] In the formula, P is the total output; k i is the mining coefficient of the i-th mining area; A i is the area of ​​the ith mining area; H i is the average coal seam thickness of the i-th mining area; ρ i is the coal seam density; η i is the recovery rate; λ is the mining rate parameter; t i is the mining time; p is the output error term.

[0035] Parameter acquisition method: k i ,A i ,Hi ,ρ i Obtained through geological exploration data; i ,λ is obtained through coal mining process analysis and historical data fitting.

[0036] 3. Security constraint function:

[0037]

[0038] Where S is the safety index; w i is the weight of the i-th safety factor; α i ,β i ,γ i is the influence coefficient of stress, deformation and hydrological factors; σ i is the stress state parameter; δ i is the deformation parameter; ω i is the hydrological parameter; ε s is the safety assessment error term.

[0039] Parameter acquisition method: w i Determined through expert evaluation; i ,β i ,γ i Obtained through numerical simulation and statistical analysis; σ i ,δ i ,ω i Through field monitoring and numerical simulation calculation.

[0040] 4. Environmental impact function:

[0041]

[0042] Where, E is the environmental impact index; S i is the surface subsidence; N i is the noise pollution degree; W i is the degree of water quality impact; a i ,b i ,c i is the weight coefficient of each factor; d i is the time attenuation coefficient; t is time; m is the number of environmental factors; ε e Evaluate the error term for the environment.

[0043] Parameter acquisition method: S i Calculated by surface settlement prediction model; N i ,W i Obtained through environmental monitoring; a i ,b i ,c i ,d i Determined through environmental impact assessment and expert evaluation.

[0044] 5. Fitness function:

[0045]

[0046] Where F is the fitness value; w1, w2, w3 are the weight coefficients of economic benefits, safety and environmental impact; ε f The error term for fitness estimation.

[0047] Parameter acquisition method: w1, w2, w3 are determined by hierarchical analysis method; T is obtained by expert scoring and fuzzy comprehensive evaluation method.

[0048] 6. Multi-dimensional search strategy combined with gray wolf hunting algorithm:

[0049]

[0050] In the formula, is the position vector of the i-th gray wolf at time t; is the prey position vector; and is the coefficient vector; is a vector that decreases linearly from 2 to 0 during the search process; and is a random vector between [0,1].

[0051] In order to adapt to high-dimensional space search, an adaptive step size is introduced:

[0052]

[0053] Where, T max is the maximum number of iterations; d is the problem dimension.

[0054] 7. Adaptive weight adjustment mechanism combined with gray wolf hunting algorithm:

[0055]

[0056] In the formula, w i is the weight of the i-th gray wolf; w min and w max are the minimum and maximum values ​​of the weight respectively; α, β, δ are the position vectors of the wolves respectively.

[0057] This adaptive weight adjustment mechanism can maintain a large global search capability in the early stage of iteration and enhance the local search capability in the later stage of iteration, thereby more effectively finding the optimal solution in high-dimensional space.

[0058] 8. The joint optimization model of the development-mining system can be expressed as a multi-objective optimization problem:

[0059] MaximizeF(x)=[f1(x),f2(x),f3(x),f4(x)];

[0060] Subject to g j (x)≤0,j=1,2,...,m;and h k (x) = 0, k = 1, 2, ..., p;

[0061] where x=(x1,x2,...,x n )∈X;

[0062] In the formula, F(x) is the objective function vector, x is the decision variable vector, and g j (x) is the inequality constraint function, h k (x) is the equality constraint function, X is the feasible solution space, Subject to represents the constraint condition, and represents the parallel relationship between the two; where represents the limitation of the feasible solution range.

[0063] The specific optimization objective function is as follows:

[0064] 1) Economic benefit objective function:

[0065]

[0066] Where P i is the coal sales price, Q i is the output of the i-th mining area, C i is the mining cost, r is the discount rate, t i is the mining time, and ε1 is the error term for economic benefit evaluation.

[0067] 2) Security objective function:

[0068]

[0069] In the formula, w i is the safety factor weight, α i ,β i ,γ i is the influence coefficient, σ i ,δ i ,ω i are stress, deformation and hydrological parameters respectively, and ε2 is the safety assessment error term.

[0070] 3) Environmental impact objective function:

[0071]

[0072] In the formula, S i is the surface subsidence, Ni is the noise pollution degree, W i is the degree of water quality impact, a i ,b i ,c i is the weight coefficient, d i is the time decay coefficient, and ε3 is the environmental assessment error term. The negative sign indicates minimizing the environmental impact.

[0073] The constraint formula is described as follows:

[0074] 1) Mining area boundary constraints:

[0075] g1(x):x min ≤x i ≤x max ,i=1,2,...,n;

[0076] In the formula, x min and x max are the minimum and maximum values ​​of the mining area coordinates respectively.

[0077] 2) Minimum mining area constraints:

[0078] g2(x):A i ≥A min ,i=1,2,...,n;

[0079] In the formula, A i is the area of ​​the ith mining area, A min The minimum permitted mining area.

[0080] 3) Maximum mining area constraints:

[0081] g3(x):A i ≤A max ,i=1,2,...,n;

[0082] In the formula, A max The maximum mining area allowed.

[0083] 4) Lane spacing constraints:

[0084] g4(x):D min ≤D i ≤D max ,i=1,2,...,m;

[0085] Where D i is the distance between the ith lane and the adjacent lane, D min and D max are the minimum and maximum allowed lane spacing, respectively.

[0086] 5) Working surface length constraint:

[0087] g5(x):L min ≤L i ≤L max ,i=1,2,...,k;

[0088] Where, L i is the length of the i-th working surface, L min and L max These are the minimum and maximum allowed working surface lengths, respectively.

[0089] 6) Coal mining process constraints:

[0090]

[0091] In the formula, y i is the coal mining process selection variable, and p is the number of optional coal mining processes.

[0092] A second aspect of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores program instructions, and when the program instructions are run in a computer, they are used to execute the above-mentioned method for optimizing the layout of mining areas in three-soft coal seams.

[0093] The third aspect of the present invention provides a three-soft coal seam mining area layout optimization system, which includes the above-mentioned computer-readable storage medium.

[0094] Compared with the prior art, the method, medium and system for optimizing the layout of three-soft coal seams provided by the present invention establish a joint optimization model of development-mining system, comprehensively consider geological conditions, hydrological conditions and mining conditions, and use a multi-objective optimization algorithm to find a layout plan with better economic benefits, safety and environmental impact. Compared with the prior art, this method has the following main advantages:

[0095] 1. Make full use of various engineering data to establish a comprehensive optimization model integrating geology, hydrology and technology, which better reflects the complex factors in the mining of three soft coal seams and improves the accuracy and reliability of the optimization results.

[0096] 2. Using multi-objective optimization methods to seek a balance between economic benefits, safety and environmental impact can provide decision makers with a set of Pareto optimal solutions and provide sufficient basis for the selection of the final solution.

[0097] 3. The introduction of numerical simulation technology and intelligent optimization algorithms, such as the multi-dimensional improved gray wolf hunting algorithm, can not only effectively discretize the continuous Pareto frontier, but also realize efficient search in high-dimensional state space, thereby improving the engineering feasibility of the optimization results.

[0098] In summary, the present invention solves the technical problem that the existing layout optimization method for three soft coal seams only optimizes the development system or the mining system, ignoring the coupling relationship between the two, resulting in limited optimization effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0099] Figure 1 A flow chart of the method provided by the present invention; DETAILED DESCRIPTION

[0100] In order to make the purpose, technical solution and advantages of the embodiments of the present invention more clear, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0101] like Figure 1 As shown, it is a flow chart of a method for optimizing the layout of three-soft coal seam mining areas provided by the present invention. The method comprises the following steps:

[0102] S10, collect geological data, hydrological data and mining condition data of three soft coal seams, wherein the geological data include coal seam thickness, dip angle, top and bottom plate lithology, fault distribution, coal seam burial depth and coal quality parameters; the hydrological data include aquifer location, hydrogeological unit division, groundwater flow direction and flow, water quality characteristics; the mining condition data include mining rights scope, surface building distribution, transportation conditions, topographic and geomorphic characteristics and environmentally sensitive areas;

[0103] S20, obtaining a plurality of pre-designed feasible development-mining system solutions, including shaft location, tunnel layout, coal mining technology and mining area sequence;

[0104] S30, establishing a joint optimization model of the development-mining system taking into account geological data, hydrological data and mining condition data as well as the development-mining system scheme, including a coal mining cost function, a production function, a safety constraint function and an environmental impact function;

[0105] S40, simulating and calculating the mining process for each development-mining system scheme to obtain simulation indicators, including coal mining face advancement speed, recovery rate, roof management effect and surface settlement;

[0106] S50. Use numerical simulation methods to analyze stress distribution, surrounding rock deformation and changes in hydrogeological conditions under different development-mining system schemes, and evaluate the stability of the mining area and water hazard risks;

[0107] S60, based on a multi-objective optimization algorithm, determining multiple optimization indicators of the development-mining system joint optimization model, solving the development-mining system joint optimization model, and obtaining a set of Pareto optimal solutions;

[0108] S70, discretizing the obtained Pareto optimal solution to form a multi-dimensional state space, where each dimension represents an optimization index, and each point in the state space represents a possible mining area layout plan;

[0109] S80. Use the multi-dimensional improved gray wolf hunting algorithm to optimize the discretized state space and obtain the optimal mining area layout plan.

[0110] The specific implementation methods of the above steps are described in detail below:

[0111] The specific implementation method of step S10 is to first collect geological data, hydrological data and mining condition data of the three soft coal seams. The geological data includes coal seam thickness H, coal seam inclination θ, top and bottom plate lithology ρ r , fault distribution F, coal seam burial depth D and coal quality parameters Q, etc. Hydrological data include aquifer location L w , Hydrogeological unit division G, Groundwater flow direction and flow rate Q g 、Water quality characteristics C w And aquifer parameters K, μ, S, etc. Mining conditions data include mining rights scope A m , surface building distribution B, transportation conditions T, topographic and geomorphic features T p and environmentally sensitive areas E, etc. These data are collected mainly through field exploration surveys, geological drilling and hydrological testing.

[0112] The specific implementation method of step S20 is to obtain a plurality of pre-designed feasible development-mining system solutions, including shaft position P, tunnel layout L d , coal mining technology M and mining area sequence S o These plans can be drawn up through the experience accumulation of technicians and computer-aided design tools.

[0113] The specific implementation method of step S30 is to establish a joint optimization model of the development-mining system that takes into account geological data, hydrological data, mining condition data, and the development-mining system solution. The optimization model mainly includes the following four objective functions:

[0114] 1. Coal mining cost function:

[0115]

[0116] Among them, C t is the total mining cost; C d ,C m ,C t ,C e ,C rThey are direct mining cost, material cost, transportation cost, equipment cost and repair cost; Q i is the mining volume of the i-th mining area; n is the number of mining areas; ε c is the cost error term. These parameters can be obtained through historical data analysis and expert evaluation.

[0117] 2. Yield function:

[0118]

[0119] Among them, P is the total output; k i ,A i ,H i ,ρ i are the mining coefficient, area, average coal seam thickness and coal seam density of the i-th mining area respectively; η i is the recovery rate; λ is the mining rate parameter; t i is the mining time; p is the production error term. These parameters can be obtained through geological exploration data and coal mining process analysis.

[0120] 3. Safety constraint function:

[0121]

[0122] Among them, S is the safety index; w i is the weight of the i-th safety factor; α i ,β i ,γ i are the influence coefficients of stress, deformation and hydrological factors respectively; σ i ,δ i ,ω i are stress state parameter, deformation parameter and hydrological parameter respectively; ε s Error terms are used to evaluate safety. These parameters can be obtained through expert evaluation, numerical simulation and field monitoring.

[0123] 4. Environmental impact function:

[0124]

[0125] Among them, E is the environmental impact index; S i is the surface subsidence; N i is the noise pollution degree; W i is the degree of water quality impact; a i ,b i ,c i are the weight coefficients of each factor; d i is the time attenuation coefficient; t is time; m is the number of environmental factors; ε eare environmental assessment error terms. These parameters can be obtained through surface subsidence prediction, environmental monitoring and expert evaluation.

[0126] These four objective functions constitute the joint optimization model of the development-mining system, which also involves a series of constraints, such as the mining area boundary constraint x min ≤x i ≤x max ,i=1,2,...,n, minimum mining area constraint A i ≥A min ,i=1,2,...,n, maximum mining area constraint A i ≤A max ,i=1,2,...,n, lane spacing constraint D min ≤D i ≤D max ,i=1,2,...,m, working surface length constraint L min ≤L i ≤L max ,i=1,2,...,k and coal mining process constraints These constraints ensure the feasibility of the optimization scheme.

[0127] The specific implementation method of step S40 is to simulate and calculate the mining process for each development-mining system scheme to obtain simulation indicators, including the coal mining face advancement speed v f , recovery rate η, roof management effect F r and surface subsidence S s The acquisition of these indicators requires the use of professional mining simulation software or independently developed mining process simulation models.

[0128] The specific implementation method of step S50 is to use numerical simulation methods to analyze the stress distribution σ, surrounding rock deformation δ and hydrogeological condition changes ω under different development-mining system schemes, and evaluate the stability and water hazard risk of the mining area. This requires the establishment of a numerical simulation model based on finite elements or discrete elements, and the input of geological and hydrological parameters to simulate the mechanical and hydrological responses during the mining process.

[0129] The specific implementation method of step S60 is to determine multiple optimization indicators of the joint optimization model of the development-mining system based on a multi-objective optimization algorithm, including economic benefit indicators. Safety indicators and environmental impact indicators The weight coefficients w1, w2, and w3 of these indicators can be determined by multi-criteria decision-making methods such as hierarchical analysis. Then, a multi-objective optimization algorithm, such as the NSGA-II algorithm, is used to solve the optimization model and obtain a set of Pareto optimal solutions.

[0130] The specific implementation method of step S70 is to discretize the obtained Pareto optimal solution to form a multi-dimensional state space, each dimension represents an optimization index, and each point in the state space represents a possible mining area layout scheme. This is done to better handle discrete decision variables in actual engineering and improve the operability of the optimization results.

[0131] The specific implementation method of step S80 is to optimize the discretized state space using a multi-dimensional improved gray wolf hunting algorithm to obtain the optimal mining area layout plan. The specific method is as follows:

[0132] 1) Map each gray wolf individual to a point in the state space Represents a mining area layout plan.

[0133] 2) Design the fitness function based on four optimization indicators (economic benefit f1, safety f2, environmental impact f3 and technical feasibility T) Each individual gray wolf was evaluated.

[0134] 3) Introduce a multi-dimensional search strategy to enable the wolf pack to efficiently explore in the high-dimensional state space. The specific formula is: in and is the coefficient vector, is a vector that decreases linearly from 2 to 0. In order to adapt to high-dimensional space, an adaptive step size is introduced. Where T max is the maximum number of iterations, and d is the problem dimension.

[0135] 4) Design an adaptive weight adjustment mechanism to maintain a large global search capability in the early stage of iteration and enhance the local search capability in the later stage of iteration. The specific formula is: where w i is the weight of the i-th gray wolf, w min and w max are the minimum and maximum values ​​of the weight, respectively. is the position vector of wolf α, β, δ.

[0136] By updating the gray wolf position through multiple iterations, the optimal solution of the target is finally obtained, that is, the optimal mining area layout plan. This two-step optimization method makes full use of the diversity of Pareto optimal solutions and the global search ability of the gray wolf algorithm, effectively avoiding the problem of falling into the local optimal solution. At the same time, by discretizing the continuous Pareto frontier into state space, it also better handles the discrete decision variables in actual engineering, and improves the practicality and operability of the optimization results.

[0137] A second aspect of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores program instructions, and when the program instructions are run in a computer, they are used to execute the above-mentioned method for optimizing the layout of mining areas in three-soft coal seams.

[0138] The third aspect of the present invention provides a three-soft coal seam mining area layout optimization system, which includes the above-mentioned computer-readable storage medium.

[0139] Specifically, the principle of the present invention is:

[0140] 1. Establish a joint optimization model for the development-mining system: The mining of three soft coal seams involves complex geological conditions, hydrological conditions and process conditions. The optimization of a single objective is difficult to fully reflect the mutual influence of these factors. Therefore, the present invention constructs a joint optimization model that includes multiple objective functions such as economic benefits, safety and environmental impact. This can not only better describe the essential characteristics of the mining of three soft coal seams, but also, by optimizing and solving this multi-objective model, a mining area layout plan with better indicators in many aspects can be obtained.

[0141] 2. Introducing numerical simulation technology for process and condition evaluation: In the optimization model, in addition to empirical evaluation criteria, the present invention also introduces a method based on numerical simulation to evaluate the performance of different development-mining schemes in terms of mechanics, hydrology, etc., such as stress distribution, surrounding rock deformation, and changes in hydrogeological conditions. This physical mechanism-based evaluation method can more accurately predict various risks in the mining process and provide reliable support for optimization decisions.

[0142] 3. Use multi-objective optimization algorithm to solve the joint optimization model: Since the joint optimization model of the development-mining system has multiple objective functions and complex constraints, it cannot be directly solved by a single optimization algorithm. Therefore, the present invention introduces a multi-objective optimization algorithm based on Pareto optimal theory, such as the NSGA-II algorithm, which can effectively find the Pareto optimal solution set in indicators such as economic benefits, safety and environmental impact. This provides decision makers with a richer choice space for weighing multiple objectives.

[0143] 4. Optimize the discretized state space by combining intelligent algorithms: For discrete decision variables in actual engineering, such as mining area boundaries, tunnel layout, etc., the present invention discretizes the Pareto optimal solution set to form a multi-dimensional state space. Then, the improved gray wolf hunting algorithm is used to optimize this state space, which can achieve efficient search in high-dimensional space, thereby obtaining a more reasonable and reliable mining area layout plan. At the same time, this two-step optimization method makes full use of the diversity of the Pareto frontier and the global search capability of the gray wolf algorithm, effectively avoiding the problem of falling into the local optimal solution.

[0144] To sum up, the three-soft coal seam mining area layout optimization method of the present invention fully integrates advanced theories and technologies in geology, hydrology, process and optimization algorithms. While ensuring the comprehensive performance of the scheme, it also takes into account the needs of engineering practice, and provides a systematic and scientific decision-making support for the efficient mining of three-soft coal seams.

[0145] In order to better understand and implement the present invention, a specific embodiment of the present invention is provided below:

[0146] A coal enterprise owns a three-soft coal seam mining area and is preparing to mine it. According to preliminary exploration, the geological conditions of the mining area are relatively complex, the thickness and inclination of the coal seams vary significantly, and there are many faults. At the same time, the mining area is rich in groundwater and has complex hydrogeological conditions, which may have a great impact on mining. In view of this, the enterprise decided to adopt the three-soft coal seam mining area layout optimization method proposed in this invention, in order to obtain a mining area layout plan with better indicators such as economic benefits, safety and environmental impact.

[0147] 1. Basic data collection

[0148] First, the company organized geological, hydrological and mining experts to conduct a comprehensive survey of the mining area and collected the following basic data:

[0149] 1. Geological data

[0150] Through drilling and measurement, the data on coal seam thickness distribution, dip angle distribution, top and bottom lithology, fault distribution, burial depth and coal quality parameters in the mining area were obtained, as shown in Table 1.

[0151] Table 1 Geological data of the mining area

[0152] parameter average value Range of variation Coal seam thickness (m) 5.2 3.8～7.6 Coal seam inclination (°) 12.4 8.2～16.7 Roof lithology Medium weathered sandstone - Floor lithology Strongly weathered shale - <![CDATA[Fault development density (faults / km 2 )]]> 1.8 1.2～2.4 Coal seam depth (m) 380 320～440 Ash content (%) 12.5 10.2～15.1 Volatile matter (%) 22.4 19.8～25.2 Sulfur content (%) 0.78 0.52～1.04

[0153] 2. Hydrological data

[0154] Through groundwater monitoring and hydrogeological survey, the location of the aquifer, groundwater flow direction and flow, water quality characteristics and some aquifer parameters of the mining area were obtained, as shown in Table 2.

[0155] Table 2 Hydrological data of mining area

[0156] parameter Numeric Location of main aquifer Tertiary conglomerate in the lower part of coal seam Groundwater mainstream Northwest-Southeast Average groundwater flow rate (m / d) 0.28 <![CDATA[Average underground horizontal flow rate (m 3 / d)]]> 1200 Water quality type HCO3-Ca Permeability coefficient (m / d) 1.6 Porosity 0.18 Depth of pressure water level (m) 280

[0157] 3. Mining condition data

[0158] Through field investigation and data collection, we obtained information such as the mining rights scope, surface building distribution, transportation conditions, topographic features and environmental sensitive areas of the mining area, as shown in Table 3.

[0159] Table 3 Mining conditions data of the mining area

[0160] parameter Situation Mineral rights scope <![CDATA[6km 2 , located in the mountainous area]]> Surface buildings Rural settlements are concentrated in the southeast Transportation There are roads and railways in the area Topography The terrain is undulating, with a height of about 150m Environmentally sensitive areas There are 2 nature reserves in the north and southwest.

[0161] 2. Development-mining system design

[0162] After mastering the above basic data, the company's technical personnel designed five feasible development-mining system solutions in advance based on experience and industry practices, as follows:

[0163] 1. Option 1:

[0164] Shaft location: There are 4 inclined shafts in total, located in the middle of the mining rights area

[0165] Tunnel layout: The main tunnel is laid out along the fault, and the tunnels in the mining area are laid out in a zigzag shape.

[0166] Coal mining technology: mechanized comprehensive coal mining technology, working face width 180m

[0167] Mining area sequence: Mining from southeast to northwest

[0168] 2. Option 2:

[0169] Shaft location: There are 3 inclined shafts in total, located in the southwest of the mining area

[0170] Tunnel layout: The main tunnel is arranged along the direction of maximum stress, and the tunnels in the mining area are arranged in a "∏" shape.

[0171] Coal mining technology: Hydraulic support comprehensive mechanized coal mining, working face width 150m

[0172] Mining area sequence: mining from west to east

[0173] 3. Solution 3:

[0174] Shaft location: There are 3 shafts in total, located in the middle of the mining area

[0175] Tunnel layout: The main tunnel is arranged along the direction of maximum principal stress, and the mining area tunnel is arranged in a "C" shape.

[0176] Coal mining technology: fully mechanized mining face with automated equipment, working face width 160m

[0177] Mining area sequence: mining from east to west

[0178] 4. Solution 4:

[0179] Shaft location: There are 4 shafts in total, located in the northeast of the mining area

[0180] Tunnel layout: The main adit is distributed along the geological structure belt, and the tunnels in the mining area are arranged in an "S" shape.

[0181] Coal mining technology: fully mechanized caving technology, working face width 140m

[0182] Mining area sequence: Mining from south to north

[0183] 5. Scheme 5:

[0184] Shaft location: There are 3 inclined shafts in total, located in the western part of the mining rights area

[0185] Tunnel layout: The main tunnels are distributed along the geological structure belt, and the tunnels in the mining area are arranged in a "V" shape.

[0186] Coal mining technology: fully mechanized mining technology with automated equipment, working face width 170m

[0187] Mining area sequence: mining from west to east

[0188] 3. Joint Optimization of Development and Mining Systems

[0189] Based on the basic data collected above, the company organized relevant experts to form an optimization working group and carried out joint optimization analysis of the development and mining system.

[0190] 1. Establishment of optimization model

[0191] According to the method of the present invention, the optimization working group first established a joint optimization model of the development-mining system including multiple objective functions such as economic benefits, safety and environmental impact. The details are as follows:

[0192] (1) Economic benefit objective function

[0193]

[0194] Among them, P i is the coal sales price of the i-th mining area (100 yuan / ton), Q i is the output of the i-th mining area (10,000 tons), C i is the mining cost of the ith mining area (yuan / ton), r is the discount rate (5%), t i is the mining time of the ith mining area (year), and ε1 is the error term for economic benefit evaluation.

[0195] (2) Security objective function

[0196]

[0197] Among them, w i is the weight of the ith safety factor (determined by analytic hierarchy process), α i ,β i ,γ i are the influence coefficients of stress, deformation and hydrological factors (obtained through numerical simulation), σ i ,δ i ,ω iThey are stress state parameters, deformation parameters and hydrological parameters (obtained through field monitoring and simulation calculations), and ε2 is the safety assessment error term.

[0198] (3) Environmental impact objective function

[0199]

[0200] Among them, S i is the surface settlement of the ith mining area (calculated by the settlement prediction model), N i is the noise pollution level of the i-th mining area (obtained through monitoring), W i is the water quality impact degree of the i-th mining area (obtained through monitoring), a i ,b i ,c i are the weight coefficients of each factor (determined through environmental impact assessment), d i is the time decay coefficient (determined empirically), and ε3 is the environmental assessment error term. The negative sign indicates minimizing environmental impact.

[0201] At the same time, the optimization working group also set the following constraints:

[0202] Mining area boundary constraint:x min ≤x i ≤x max ,i=1,2,...,n;

[0203] Minimum mining area constraint: A i ≥0.5km 2 ,i=1,2,...,n

[0204] Maximum mining area constraint: A i ≤2km 2 ,i=1,2,...,n;

[0205] Lane spacing constraint: 100m≤D i ≤300m,i=1,2,...,m;

[0206] Working surface length constraint: 120m≤L i ≤200m,i=1,2,...,k;

[0207] Coal mining process constraints:

[0208] 2. Numerical simulation and evaluation

[0209] After establishing the optimization model, the working group used finite element software to conduct numerical simulation analysis on five development-mining system schemes, focusing on evaluating the stress distribution, surrounding rock deformation and changes in hydrogeological conditions in the mining area. Based on the simulation results, the coal mining face advancement speed v under each scheme was calculated. f , recovery rate η, roof management effect F r and surface subsidence S s And other indicators provide a basis for subsequent optimization.

[0210] Taking Scheme 1 as an example, the numerical simulation results are as follows:

[0211] Maximum principal stressσ max =24.6MPa, located in the southeast of the mining area

[0212] Maximum deformation δ max =82.4mm, located in the middle of the mining area

[0213] The water level of the aquifer drops by ω = 18m, and the main affected area is located in the northwest of the mining area.

[0214] Coal mining face advancement speed v f =3.2m / d;

[0215] Recovery rate η = 92%;

[0216] The roof management effect is good, F r =0.85;

[0217] Maximum surface settlement S s =490mm;

[0218] In a similar way, the numerical simulation results of the other four schemes were also obtained.

[0219] 3. Multi-objective optimization solution

[0220] With the above optimization model and numerical simulation analysis results, the working group used the NSGA-II algorithm to solve the joint optimization problem. After multiple iterations of optimization, a set of Pareto optimal solutions were finally obtained. In order to better reflect the comprehensive performance of each solution, the working group constructed the following fitness function:

[0221]

[0222] Among them, f1, f2, and f3 are the objective function values ​​of economic benefit, safety, and environmental impact, respectively. t is the total mining cost, T is the technical feasibility index (obtained through fuzzy comprehensive evaluation), ε f is the error term for fitness evaluation. The weight coefficient of each target indicator is determined by the hierarchical analysis method.

[0223] By evaluating the fitness of the Pareto optimal solution set, the working group finally determined the optimal mining area layout plan, as follows:

[0224] Table 4 Optimal mining area layout scheme

[0225] parameter Numeric Wellbore location 4 inclined shafts, located in the middle of the mining area Lane layout The main adit is distributed along the geological structure belt, and the mining area tunnels are arranged in a "W" shape. Coal mining technology Comprehensive mining technology with automated equipment, working face width 160m Mining area order Mining from southeast to northwest Economic Benefits <![CDATA[f1 = 420 million yuan]]> Security <![CDATA[f2=0.78]]> Environmental impact <![CDATA[f3=0.22]]> Fitness value F＝0.85

[0226] The scheme performs well in terms of economic benefits, safety and environmental impact, with a fitness value of 0.85, which is in line with the company's mining goals.

[0227] Specifically, the economic benefit index f1 of this scheme is 420 million yuan, which is higher than other schemes; the safety index f2 is 0.78, indicating that the stability of the mining area and the risk of water damage are controllable; the environmental impact index f3 is 0.22, indicating that the environmental impact is small. At the same time, the technical feasibility index T of this scheme is also high, indicating that the process layout is reasonable and can meet the actual mining needs.

[0228] In contrast, the other four options may perform better in some indicators, but their overall performance is slightly inferior to this option. For example, Option 2 has better safety, but slightly lower economic benefits; Option 4 has less environmental impact, but relatively poor technical feasibility. Therefore, after full consideration, the working group finally determined this option as the optimal mining area layout plan for the mine.

[0229] The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A method for optimizing the layout of mining areas in three soft coal seams, characterized in that: The following steps are involved: S10. Collect geological data, hydrological data and mining condition data of three soft coal seams; S20, obtaining a plurality of pre-designed feasible development-mining system solutions, including shaft location, tunnel layout, coal mining technology and mining area sequence; S30, establishing a joint optimization model of the development-mining system taking into account geological data, hydrological data and mining condition data as well as the development-mining system scheme, including a coal mining cost function, a production function, a safety constraint function and an environmental impact function; S40, simulating and calculating the mining process for each development-mining system scheme to obtain simulation indicators, including coal mining face advancement speed, recovery rate, roof management effect and surface settlement; S50. Use numerical simulation methods to analyze stress distribution, surrounding rock deformation and changes in hydrogeological conditions under different development-mining system schemes, and evaluate the stability of the mining area and water hazard risks; S60, based on a multi-objective optimization algorithm, determining multiple optimization indicators of the development-mining system joint optimization model, solving the development-mining system joint optimization model, and obtaining a set of Pareto optimal solutions; S70, discretizing the obtained Pareto optimal solution to form a multi-dimensional state space, where each dimension represents an optimization index, and each point in the state space represents a possible mining area layout plan; S80. Use the multi-dimensional improved gray wolf hunting algorithm to optimize the discretized state space and obtain the optimal mining area layout plan.

2. The method for optimizing the layout of three-soft coal seam mining areas according to claim 1 is characterized in that: The multiple optimization indicators of the joint optimization model of the development-mining system are determined, specifically including: economic benefit indicators, safety indicators and environmental impact indicators.

3. The method for optimizing the layout of three-soft coal seam mining areas according to claim 2 is characterized in that: The constraints for determining the joint optimization model of the development-mining system specifically include: mining area boundary constraints, minimum mining area constraints, maximum mining area constraints, tunnel spacing constraints, working face length constraints and coal mining process constraints.

4. The method for optimizing the layout of three-soft coal seam mining areas according to claim 3 is characterized in that: The step of optimizing the discretized state space by using the multi-dimensional improved gray wolf hunting algorithm is specifically: Map each individual gray wolf to a point in the state space; Design a fitness function based on the optimization index to evaluate the fitness of each individual gray wolf; Introducing a multi-dimensional search strategy enables the wolf pack to efficiently explore in high-dimensional state space; Design an adaptive weight adjustment mechanism to balance local search and global search; By updating the gray wolf's position through multiple iterations, the optimal solution of the target is finally obtained, that is, the optimal mining area layout plan.

5. The method for optimizing the layout of three-soft coal seam mining areas according to claim 4, characterized in that: The geological data include coal seam thickness, dip, top and bottom plate lithology, fault distribution, coal seam burial depth and coal quality parameters.

6. The method for optimizing the layout of three-soft coal seam mining areas according to claim 5, characterized in that: The hydrological data include aquifer location, hydrogeological unit division, groundwater flow direction and flow, and water quality characteristics.

7. The method for optimizing the layout of three-soft coal seam mining areas according to claim 6, characterized in that: The mining conditions data include mining rights scope, surface building distribution, transportation conditions, topographical features and environmentally sensitive areas.

8. The method for optimizing the layout of three-soft coal seam mining areas according to claim 7, characterized in that: The multi-objective optimization algorithm adopts NSGA-II algorithm.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores program instructions, and when the program instructions are run in a computer, they are used to execute the method for optimizing the layout of three-soft coal seam mining areas as described in any one of claims 1-8.

10. A three-soft coal seam mining area layout optimization system, characterized in that: A computer-readable storage medium comprising the computer-readable storage medium of claim 9.

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