A method, medium, and system for optimizing the layout of mining areas in three soft coal seams

Abstract

This invention provides a method, medium, and system for optimizing the layout of mining areas in soft coal seams, belonging to the technical field of mining area layout for soft coal seams. The method includes: firstly, collecting detailed geological, hydrological, and mining condition data, and obtaining multiple feasible development-mining system schemes. Then, based on numerical simulation, evaluating the mining technology, mining area stability, and water hazard risk under different schemes. Next, using a multi-objective optimization algorithm to solve the optimization model, obtaining the Pareto optimal solution set. Finally, discretizing the Pareto solution to form a multi-dimensional state space, and using an improved gray wolf hunting algorithm to optimize the state space, ultimately obtaining the optimal mining area layout scheme. This method fully utilizes multi-source data, integrates simulation calculations and optimization algorithms, and provides effective technical support for the safe and efficient mining of soft coal seams. It solves the technical problem that existing methods neglect the coupling relationship between the mining and excavation systems, leading to limited optimization effects.

Description

Technical Field

[0001] This invention belongs to the technical field of layout of mining areas in three soft coal seams. Specifically, it relates to a method, medium, and system for optimizing the layout of mining areas in three soft coal seams. Background Technology

[0002] Coal, as one of China's most important energy resources, plays a vital role in national economic development. Among these, soft coal seams, characterized by lower coal quality, greater mining difficulty, and stronger hydrogeological influences, have always posed a significant challenge to the coal industry. To achieve efficient mining of soft coal seams, developing a rational mining area layout plan is crucial. Existing optimization methods for mining soft coal seams mainly focus on single-objective optimization, such as considering only economic benefits, safety, or environmental impact, failing to comprehensively evaluate the overall performance of the mining plan. Furthermore, these methods typically employ empirical evaluation criteria, which cannot objectively reflect the complex geological, hydrological, and technological factors involved in actual mining. Simultaneously, most optimization methods only optimize the development system or the mining and tunneling system, neglecting the coupling relationship between the two, thus limiting the optimization effectiveness. 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 in soft coal seams, which can solve the technical problem that existing optimization methods for soft coal seams only optimize the development system or the mining system, ignoring the coupling relationship between the two, resulting in limited optimization effect.

[0004] This invention is implemented as follows:

[0005] The first aspect of the present invention provides a method for optimizing the layout of mining areas in three soft coal seams, comprising the following steps:

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

[0007] S20. Obtain multiple pre-designed feasible development-mining system plans, including shaft location, roadway layout, coal mining technology, and mining sequence;

[0008] S30. Establish a joint optimization model for the development-mining system that considers geological data, hydrological data, mining condition data, and development-mining system schemes, including coal mining cost function, production function, safety constraint function, and environmental impact function;

[0009] S40. Simulate and calculate the mining process for each development-mining system scheme to obtain simulation indicators, including the coal face advance speed, recovery rate, roof management effect and surface subsidence;

[0010] S50. Using numerical simulation methods, analyze the stress distribution, surrounding rock deformation, and hydrogeological conditions under different development-mining system schemes, and assess the stability of the mining area and the risk of water hazards.

[0011] S60. Based on a multi-objective optimization algorithm, determine multiple optimization indices for the joint optimization model of the development-mining system, solve the joint optimization model of the development-mining system, and obtain a set of Pareto optimal solutions;

[0012] S70. 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.

[0013] S80. The multi-dimensional improved gray wolf hunting algorithm is used to optimize the discretized state space to obtain the optimal mining area layout scheme.

[0014] Based on the above technical solution, the method for optimizing the layout of mining areas in three soft coal seams of the present invention can be further improved as follows:

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

[0016] Furthermore, 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, roadway spacing constraints, working face length constraints, and coal mining process constraints.

[0017] Furthermore, the step of optimizing the discretized state space using the multidimensional improved gray wolf hunting algorithm specifically includes:

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

[0019] A fitness function was designed based on optimization metrics to evaluate the fitness of each individual gray wolf.

[0020] Introducing a multidimensional search strategy enables gray wolf packs to explore efficiently in a high-dimensional state space;

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

[0022] By iteratively updating the position of the gray wolf, the optimal solution for the target, namely the optimal mining area layout scheme, is finally obtained.

[0023] Furthermore, the geological data includes coal seam thickness, dip angle, roof and floor lithology, fault distribution, coal seam depth, and coal quality parameters.

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

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

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

[0027] Furthermore, the functions in this invention are described in detail below:

[0028] 1. Coal mining cost function:

[0029]

[0030] In the formula, C t C represents the total mining cost; d For direct mining costs; C m For material costs; C t For transportation costs; C e For equipment costs; C r For repair costs; Q i ε represents the mining volume of the i-th mining area; n represents the number of mining areas; ε c This 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. Output function:

[0033]

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

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

[0036] 3. Safety constraint function:

[0037]

[0038] In the formula, S is the safety index; w i The weight of the i-th safety factor; α i ,β i ,γ i σ represents the influence coefficients of stress, deformation, and hydrological factors; i δ is a stress state parameter. i ω is the deformation parameter; i For hydrological parameters; ε s This is a safety assessment error term.

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

[0040] 4. Environmental impact function:

[0041]

[0042] In the formula, E is the environmental impact index; S i N represents surface subsidence. i The degree of noise pollution; W i The degree of impact on water quality; a i ,b i ,c i d represents the weighting coefficients of each factor; i ε is the time decay coefficient; t is time; m is the number of environmental influencing factors; e This is the error term in the environmental assessment.

[0043] Parameter acquisition method: S i Calculated using a surface subsidence prediction model; N i W i Obtained through environmental monitoring; a i ,b i ,c i ,d i It is determined through environmental impact assessment and expert evaluation.

[0044] 5. Fitness function:

[0045]

[0046] In the formula, F is the fitness value; w1, w2, w3 are the weighting coefficients for economic benefits, safety, and environmental impact; ε f This is the fitness assessment error term.

[0047] Parameter acquisition methods: w1, w2, and w3 are determined by the analytic hierarchy process; T is obtained by expert scoring and fuzzy comprehensive evaluation.

[0048] 6. Multidimensional search strategy combined with gray wolf hunting algorithm:

[0049]

[0050] Where, Let be the position vector of the i-th gray wolf at time t; The prey's position vector; and For coefficient vectors; Let be a vector that decreases linearly from 2 to 0 during the search process; and It is a random vector between [0,1].

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

[0052]

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

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

[0055]

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

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

[0058] 8. The joint optimization model of the development-mining system can be represented 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 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] In the formula, P i Q represents the selling price of coal. i Let C be the output of the i-th mining area. i Let t be the mining cost, r be the discount rate, and t be the cost of mining. i ε1 represents the mining time and the economic benefit assessment error term.

[0067] 2) Security objective function:

[0068]

[0069] In the formula, w i For the weight of safety factors, α i ,β i ,γ i σ is the influence coefficient. i ,δ i ,ω i These represent stress, deformation, and hydrological parameters, respectively, with ε2 being the safety assessment error term.

[0070] 3) Environmental impact objective function:

[0071]

[0072] In the formula, S i N represents the amount of land subsidence.i For the degree of noise pollution, W i To determine the degree of water quality impact, a i ,b i ,c i d represents the weighting coefficient. i ε is the time decay coefficient, and ε3 is the environmental assessment error term. The negative sign indicates minimizing the environmental impact.

[0073] The constraint conditions are described in the following formulas:

[0074] 1) Mining area boundary constraints:

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

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

[0077] 2) Minimum mining area constraint:

[0078] g2(x):A i ≥A min i = 1, 2, ..., n;

[0079] In the formula, A i Let A be the area of ​​the i-th mining area. min The minimum allowable mining area.

[0080] 3) Maximum mining area constraint:

[0081] g3(x):A i ≤A max i = 1, 2, ..., n;

[0082] In the formula, A max This represents the maximum allowable mining area.

[0083] 4) Lane spacing constraints:

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

[0085] In the formula, D i Let D be the distance between the i-th lane and its adjacent lanes. min and D max These are the minimum and maximum allowable roadway spacing, respectively.

[0086] 5) Working face length constraint:

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

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

[0089] 6) Coal mining technology constraints:

[0090]

[0091] In the formula, y i Let p be the number of available coal mining processes, where p is the number of coal mining processes to choose from.

[0092] A second aspect of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores program instructions, which, when executed in a computer, are used to perform the above-described method for optimizing the layout of a three-soft coal seam mining area.

[0093] A third aspect of the present invention provides a system for optimizing the layout of mining areas in three soft coal seams, wherein the system includes the aforementioned computer-readable storage medium.

[0094] Compared with existing technologies, the present invention provides a method, medium, and system for optimizing the layout of mining areas in three soft coal seams. By establishing a joint optimization model of the development and mining system, and comprehensively considering geological, hydrological, and mining conditions, a multi-objective optimization algorithm is used to find a mining area layout scheme with superior economic benefits, safety, and environmental impact. Compared with existing technologies, this method has the following main advantages:

[0095] 1. By making full use of various engineering data, a comprehensive optimization model integrating geology, hydrology, and technology was established, which better reflected the complex factors in the mining of soft coal seams and improved the accuracy and reliability of the optimization results.

[0096] 2. By employing a multi-objective optimization method, a balance is sought among objectives such as economic benefits, safety, and environmental impact. This approach can provide decision-makers with a set of Pareto optimal solutions, offering sufficient basis for the selection of the final solution.

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

[0098] In summary, this invention solves the technical problem that existing optimization methods for three soft coal seams only optimize the development system or the mining system, ignoring the coupling relationship between the two, thus limiting the optimization effect. Attached Figure Description

[0099] Figure 1 A flowchart of the method provided by the present invention; Detailed Implementation

[0100] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings.

[0101] like Figure 1 The diagram shown is a flowchart of a method for optimizing the layout of mining areas in three soft coal seams provided by this invention. This method includes the following steps:

[0102] S10. Collect geological data, hydrological data, and mining condition data for the three soft coal seams. The geological data includes coal seam thickness, dip angle, roof and floor lithology, fault distribution, coal seam depth, and coal quality parameters. The hydrological data includes aquifer location, hydrogeological unit division, groundwater flow direction and flow rate, and water quality characteristics. The mining condition data includes mining rights scope, distribution of surface buildings, transportation conditions, topographic features, and environmentally sensitive areas.

[0103] S20. Obtain multiple pre-designed feasible development-mining system plans, including shaft location, roadway layout, coal mining technology, and mining sequence;

[0104] S30. Establish a joint optimization model for the development-mining system that considers geological data, hydrological data, mining condition data, and development-mining system schemes, including coal mining cost function, production function, safety constraint function, and environmental impact function;

[0105] S40. Simulate and calculate the mining process for each development-mining system scheme to obtain simulation indicators, including the coal face advance speed, recovery rate, roof management effect and surface subsidence;

[0106] S50. Using numerical simulation methods, analyze the stress distribution, surrounding rock deformation, and hydrogeological conditions under different development-mining system schemes, and assess the stability of the mining area and the risk of water hazards.

[0107] S60. Based on a multi-objective optimization algorithm, determine multiple optimization indices for the joint optimization model of the development-mining system, solve the joint optimization model of the development-mining system, and obtain a set of Pareto optimal solutions;

[0108] S70. 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.

[0109] S80. The multi-dimensional improved gray wolf hunting algorithm is used to optimize the discretized state space to obtain the optimal mining area layout scheme.

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

[0111] The specific implementation of step S10 is as follows: First, geological data, hydrological data, and mining condition data of the three soft coal seams are collected. Geological data includes coal seam thickness H, coal seam dip angle θ, and roof and floor lithology ρ. r Fault distribution (F), coal seam depth (D), and coal quality parameters (Q), etc. Hydrological data includes aquifer location (L). w Hydrogeological unit division G, groundwater flow direction and traffic Q g Water quality characteristics C w And aquifer parameters such as K, μ, and S. Mining condition data includes the mining rights area A. m Surface building distribution (B), transportation conditions (T), topographic features (T) p And environmentally sensitive areas such as E. These data were primarily collected through field surveys, geological drilling, and hydrological testing.

[0112] The specific implementation of step S20 is to obtain a variety of pre-designed feasible development-mining system schemes, including shaft location P and roadway layout L. d Coal mining technology (M) and mining sequence (S) o Key technical parameters, etc. These solutions can be formulated through the accumulation of technical personnel's experience and computer-aided design tools.

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

[0114] 1. Coal mining cost function:

[0115]

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

[0117] 2. Production function:

[0118]

[0119] Where P is the total output; k i A i H i ,ρ i These represent the mining coefficient, area, average coal seam thickness, and coal seam density of the i-th mining area, respectively; η i λ is the recovery rate; t is the mining rate parameter; i For mining time; ε p This represents 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] Where S is the safety index; w i The weight of the i-th safety factor; α i ,β i ,γ i These are the influence coefficients for stress, deformation, and hydrological factors, respectively; σ i ,δ i ,ω i These are stress state parameters, deformation parameters, and hydrological parameters, respectively; ε s This represents the error term in the safety assessment. These parameters can be obtained through expert evaluation, numerical simulation, and on-site monitoring.

[0123] 4. Environmental impact function:

[0124]

[0125] Where E is the environmental impact index; S i N represents surface subsidence. i The degree of noise pollution; W i The degree of impact on water quality; a i ,b i ,c i These are the weighting coefficients for each factor; d i ε is the time decay coefficient; t is time; m is the number of environmental influencing factors; eThis represents the error term in the environmental assessment. These parameters can be obtained through land 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 boundary constraint x of the mining area. min ≤x i ≤x max Minimum mining area constraint A, i = 1, 2, ..., n i ≥A min ,i=1,2,...,n、Maximum mining area constraint A i ≤A max ,i=1,2,...,n、Road spacing constraint D min ≤D i ≤D max i = 1, 2, ..., m, working face 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 of step S40 is to simulate and calculate the mining process for each development-mining system scheme to obtain simulation indicators, including the coal face advance speed v. f Recovery rate η, roof management effectiveness F r and surface subsidence S s These indicators require the use of professional mining simulation software or self-developed mining process simulation models.

[0128] The specific implementation of step S50 involves using 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 to assess the stability and water hazard risk of the mining area. This requires establishing a numerical simulation model based on the finite element or discrete element method, and inputting geological and hydrological parameters to simulate the mechanical and hydrological responses during the mining process.

[0129] The specific implementation of step S60 is to determine multiple optimization indicators for 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 using multi-criteria decision-making methods such as the analytic hierarchy process (AHP). Then, a multi-objective optimization algorithm, such as the NSGA-II algorithm, is used to solve the optimization model, yielding a set of Pareto optimal solutions.

[0130] The specific implementation of step S70 involves 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 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 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 scheme. The specific steps are as follows:

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

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

[0134] 3) Introducing a multidimensional search strategy enables the gray wolf pack to explore efficiently in a high-dimensional state space. The specific formula is: in and For the coefficient vector, Let be a vector that decreases linearly from 2 to 0. To adapt to high-dimensional spaces, an adaptive step size is introduced. Where T max d represents the maximum number of iterations, and d represents the problem dimension.

[0135] 4) Design an adaptive weight adjustment mechanism to maintain a large global search capability in the early stages of iteration, while enhancing the local search capability in the later stages. The specific formula is: Where w i Let w be the weight of the i-th gray wolf. min and w max These are the minimum and maximum values ​​of the weights, respectively. Let α, β, δ be the position vectors of the wolves.

[0136] By iteratively updating the position of the gray wolf, the optimal solution, i.e., the optimal mining area layout, is finally obtained. This two-step optimization method fully utilizes the diversity of Pareto optimal solutions and the global search capability of the gray wolf algorithm, effectively avoiding the problem of getting trapped in local optima. At the same time, by discretizing the continuous Pareto front into a state space, it also better handles discrete decision variables in practical engineering, improving 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, which, when executed in a computer, are used to perform the above-described method for optimizing the layout of a three-soft coal seam mining area.

[0138] A third aspect of the present invention provides a system for optimizing the layout of mining areas in three soft coal seams, wherein the system includes the aforementioned computer-readable storage medium.

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

[0140] 1. Establishing a Joint Optimization Model for the Development and Mining System: Mining soft coal seams involves complex geological, hydrological, and technological conditions, and optimization based on a single objective cannot fully reflect the interplay of these factors. Therefore, this invention constructs a joint optimization model that includes multiple objective functions such as economic benefits, safety, and environmental impact. This not only better describes the essential characteristics of mining soft coal seams, but also, by optimizing and solving this multi-objective model, yields a mining area layout scheme that is superior in many aspects.

[0141] 2. Introducing numerical simulation technology for process and condition evaluation: In addition to empirical evaluation criteria, this invention also introduces a numerical simulation-based method 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 evaluation method based on physical mechanisms can more accurately predict various risks in the mining process and provide reliable support for optimization decisions.

[0142] 3. Solving the Joint Optimization Model Using Multi-Objective Optimization Algorithms: Since the joint optimization model of the mining-exploitation system involves multiple objective functions and complex constraints, it cannot be directly solved using a single optimization algorithm. Therefore, this invention introduces multi-objective optimization algorithms based on Pareto optimality theory, such as the NSGA-II algorithm, which can effectively find the Pareto optimal solution set for indicators such as economic benefits, safety, and environmental impact. This provides decision-makers with a richer selection space for weighing multiple objectives.

[0143] 4. Optimizing the Discrete State Space with Intelligent Algorithms: For discrete decision variables in practical engineering, such as mining area boundaries and roadway layout, this invention discretizes the Pareto optimal solution set to form a multi-dimensional state space. Then, an improved Grey Wolf hunting algorithm is used to optimize this state space, enabling efficient search in high-dimensional space and resulting in a more reasonable and reliable mining area layout scheme. Simultaneously, this two-step optimization method fully utilizes the diversity of the Pareto front and the global search capability of the Grey Wolf algorithm, effectively avoiding the problem of getting trapped in local optima.

[0144] In summary, the optimization method for mining area layout of soft coal seams of the present invention fully integrates advanced theories and technologies from multiple aspects such as geology, hydrology, technology and optimization algorithms. While ensuring the comprehensive performance of the scheme, it also takes into account the needs of engineering practice, providing a systematic and scientific decision support for the efficient mining of soft coal seams.

[0145] To better understand and implement this invention, a specific embodiment of the invention is provided below:

[0146] A coal company owns a three-soft coal seam mining area and plans to mine it. Preliminary exploration indicates that the geological conditions of the mining area are complex, with significant variations in coal seam thickness and dip angle, and numerous faults. Furthermore, the area has abundant groundwater and complex hydrogeological conditions, which may significantly impact mining operations. Therefore, the company has decided to adopt the three-soft coal seam mining area layout optimization method proposed in this invention, aiming to obtain a mining area layout scheme with superior economic benefits, safety, and environmental impact.

[0147] I. Basic Data Collection

[0148] First, the company organized experts in geology, hydrology, and mining to conduct a comprehensive survey of the mining area, collecting the following basic data:

[0149] 1. Geological data

[0150] Through drilling and measurement, data on coal seam thickness distribution, dip angle distribution, roof and floor lithology, fault distribution, burial depth, and coal quality parameters were obtained for the mining area, 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 dip angle (°) 12.4 8.2～16.7 Roof Lithology moderately weathered sandstone - Bottom lithology Strongly weathered shale - <![CDATA[Fault development density (number / km 2 )]]> 1.8 1.2～2.4 Coal seam burial 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 exploration, the location of aquifers, groundwater flow direction and flow rate, water quality characteristics, and some aquifer parameters of the mining area were obtained, as shown in Table 2.

[0155] Table 2 Hydrological Data of the Mining Area

[0156] parameter Numerical Location of main aquifer Tertiary sandstone and conglomerate in the lower part of the coal seam groundwater mainstream direction Northwest-Southeast Average groundwater flow velocity (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 Confined water level depth (m) 280

[0157] 3. Mining Condition Data

[0158] Through field investigation and data collection, information was obtained regarding the mining area's mining rights scope, distribution of surface buildings, transportation conditions, topographic features, and environmentally sensitive areas, as shown in Table 3.

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

[0160] parameter Situation Description Mining rights scope <![CDATA[6km 2 Located in a mountainous area Surface buildings Rural settlements are concentrated in the southeast. Transportation The area has highways and railways. Topography The terrain is undulating, with an elevation of approximately 150m. Environmentally sensitive areas There are two nature reserves located in the north and southwest.

[0161] II. Development-Mining System Design

[0162] Having grasped the aforementioned basic data, the company's technical personnel, based on experience and industry practices, pre-designed five feasible development-mining system schemes, as follows:

[0163] Option 1:

[0164] Shaft location: A total of 4 inclined shafts are constructed, located in the central part of the mining rights area.

[0165] Tunnel layout: The main adit extends along the fault strike, and the mining area tunnels are arranged in a zigzag pattern.

[0166] Coal mining technology: Mechanized integrated coal mining technology, working face width 180m

[0167] Mining sequence: from southeast to northwest.

[0168] Option 2:

[0169] Shaft location: A total of 3 inclined shafts are constructed, located in the southwest of the mining rights area.

[0170] Roadway layout: The main adit extends along the direction of maximum stress, and the roadways in the mining area are arranged in a "∏" shape.

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

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

[0173] 3. Option 3:

[0174] Shaft location: A total of 3 vertical shafts are constructed, located in the central part of the mining rights area.

[0175] Roadway layout: The main adit is distributed along the direction of the maximum principal stress, and the roadways in the mining area are arranged in a "C" shape.

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

[0177] Mining sequence: Mining proceeds from east to west.

[0178] 4. Option 4:

[0179] Shaft location: A total of 4 vertical shafts are constructed, located in the northeastern part of the mining rights area.

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

[0181] Coal mining technology: Fully mechanized longwall mining, working face width 140m

[0182] Mining sequence: Mining proceeds from south to north.

[0183] 5. Option 5:

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

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

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

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

[0188] III. Joint Optimization of Development-Mining Systems

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

[0190] 1. Establishment of the optimization model

[0191] According to the method of the present invention, the optimization working group first established a joint optimization model for the mining-exploitation system, which includes multiple objective functions such as economic benefits, safety, and environmental impact. Specifically, as follows:

[0192] (1) Economic benefit objective function

[0193]

[0194] Where, P i Let Q be the coal sales price of the i-th mining area (100 yuan / ton). i Let C be the output (in ten thousand tons) of the i-th mining area. i Let t be the mining cost of the i-th mining area (yuan / ton), r be the discount rate (5%), and t be the cost of mining in the i-th mining area. i Let εi represent the mining time (in years) of the i-th mining area, and ε1 represent the economic benefit assessment error term.

[0195] (2) Security objective function

[0196]

[0197] Where, w i The weight of the i-th safety factor (determined using the analytic hierarchy process), α i ,β i ,γ i These are the influence coefficients of stress, deformation, and hydrological factors (obtained through numerical simulation), σ i ,δ i ,ω iThese are stress state parameters, deformation parameters, and hydrological parameters (obtained through on-site monitoring and simulation calculations), respectively, with ε2 representing the safety assessment error term.

[0198] (3) Environmental impact objective function

[0199]

[0200] Where, S i Let N be the surface subsidence of the i-th mining area (calculated using a subsidence prediction model). i W represents the noise pollution level of the i-th mining area (obtained through monitoring). i Let a be the degree of water quality impact in the i-th mining area (obtained through monitoring). i ,b i ,c i The weighting coefficients for each factor (determined through environmental impact assessment), d i ε is the time decay coefficient (determined empirically), and ε3 is the environmental assessment error term. The negative sign indicates minimizing environmental impact.

[0201] In addition, the optimization working group also set the following constraints:

[0202] Mining area boundary constraints: 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 face 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 hydrogeological conditions in the mining area. Based on the simulation results, the coal face advance speed v under each scheme was calculated. f Recovery rate η, roof management effectiveness F r and surface subsidence S s These indicators provide a basis for subsequent optimization.

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

[0211] Maximum principal stress σ max =24.6MPa, located in the southeastern part of the mining area

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

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

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

[0215] Recovery rate η = 92%;

[0216] The roof management is effective. r =0.85;

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

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

[0219] 3. Multi-objective optimization solution

[0220] Based on the aforementioned optimization model and numerical simulation 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 was finally obtained. To better reflect the comprehensive performance of each scheme, the working group constructed the following fitness function:

[0221]

[0222] Where f1, f2, and f3 are the objective function values ​​for economic benefits, safety, and environmental impact, respectively, and C is the objective function value for environmental impact. t Let T be the total mining cost, T be the technical feasibility index (obtained through fuzzy comprehensive evaluation), and ε be the total mining cost. f This represents the fitness assessment error term. The weight coefficients for each objective indicator were determined using the analytic hierarchy process (AHP).

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

[0224] Table 4 Optimal Mining Area Layout Scheme

[0225] parameter Numerical Wellbore location Four inclined shafts are located in the central part of the mining rights area. Lane layout The main adit is distributed along the geological structure zone, and the mining area roadways are arranged in a "W" shape. Coal mining technology The fully mechanized mining process, combined with automated equipment, has a working face width of 160m. mining area sequence Mining proceeds sequentially 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 proposed scheme demonstrates superior performance in terms of economic benefits, safety, and environmental impact, achieving a fitness value of 0.85, which aligns with the company's mining objectives.

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

[0228] In comparison, the other four options may perform better on 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 careful consideration, the working group ultimately determined this option as the optimal mining area layout for the mine area.

[0229] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for optimizing the layout of mining areas in three soft coal seams, characterized in that, Includes the following steps: S10. Collect geological data, hydrological data, and mining condition data of the three soft coal seams; S20. Obtain multiple pre-designed feasible development-mining system plans, including shaft location, roadway layout, coal mining technology, and mining sequence; S30. Establish a joint optimization model for the development-mining system that considers geological data, hydrological data, mining condition data, and development-mining system schemes, including coal mining cost function, production function, safety constraint function, and environmental impact function; S40. Simulate and calculate the mining process for each development-mining system scheme to obtain simulation indicators, including the coal face advance speed, recovery rate, roof management effect and surface subsidence; S50. Using numerical simulation methods, analyze the stress distribution, surrounding rock deformation, and hydrogeological conditions under different development-mining system schemes, and assess the stability of the mining area and the risk of water hazards. S60. Based on a multi-objective optimization algorithm, determine multiple optimization indices for the joint optimization model of the development-mining system, solve the joint optimization model of the development-mining system, and obtain a set of Pareto optimal solutions; S70. 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. S80. The multi-dimensional improved gray wolf hunting algorithm is used to optimize the discretized state space to obtain the optimal mining area layout scheme.

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

3. The method for optimizing the layout of mining areas in three soft coal seams according to claim 2, 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, roadway spacing constraints, working face length constraints, and coal mining process constraints.

4. The method for optimizing the layout of mining areas in three soft coal seams according to claim 3, characterized in that, The steps for optimizing the discretized state space using the multidimensional improved gray wolf hunting algorithm are as follows: Map each individual gray wolf to a point in the state space; A fitness function was designed based on optimization metrics to evaluate the fitness of each individual gray wolf. Introducing a multidimensional search strategy enables gray wolf packs to explore efficiently in a high-dimensional state space; Design an adaptive weight adjustment mechanism to balance local and global search; By iteratively updating the position of the gray wolf, the optimal solution for the target, namely the optimal mining area layout scheme, is finally obtained.

5. The method for optimizing the layout of mining areas in three soft coal seams according to claim 4, characterized in that, The geological data includes coal seam thickness, dip angle, roof and floor lithology, fault distribution, coal seam depth, and coal quality parameters.

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

7. The method for optimizing the layout of mining areas in three soft coal seams according to claim 6, characterized in that, The mining conditions data include the mining rights area, the distribution of surface buildings, transportation conditions, topographic features, and environmentally sensitive areas.

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

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores program instructions, which, when executed in a computer, are used to perform the method for optimizing the layout of a three-soft coal seam mining area as described in any one of claims 1-8.

10. A system for optimizing the layout of mining areas in three soft coal seams, characterized in that, It includes the computer-readable storage medium of claim 9.

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