Underground water pollution hydraulic control simulation-optimization method for in-situ leaching uranium mining final mining area

Abstract

The invention discloses a hydraulic control simulation-optimization method for underground water pollution in an in-situ leaching uranium mining final mining area. The method comprises the following steps: constructing a three-dimensional unstable underground water flow and pollution component reaction migration simulation model of an ore-bearing water layer by utilizing MODFLOW and PHT3D simulation software; determining a management objective function and constraint conditions of hydraulic control of the underground water pollution range of the in-situ leaching uranium mining final mining area, and establishing an objective function of a hydraulic control optimization management model; the underground water level and the pollutant concentration of each grid are used as state variables, the water pumping flow of each well is used as a decision variable, and the state variables are continuously updated through an ore-bearing water layer three-dimensional unstable underground water flow and pollution component reaction migration simulation model. And selecting an optimal solution of the objective function of the hydraulic control optimization management model by using a particle swarm optimization algorithm. According to the method, the hydraulic control condition of the pollution plume of the ore-bearing and water-bearing stratum in the final mining area can be predicted, potential problems can be found in advance, and corresponding measures are taken for adjustment.

Description

Technical Field

[0001] The invention relates to the technical field of water pollution hydraulic control simulation, in particular to a groundwater pollution hydraulic control simulation-optimization method in a final mining area of ​​in-situ leaching uranium. Background Art

[0002] In-situ uranium leaching is the main process for natural uranium production in my country. During the production process, a certain amount of chemical reagents are injected into the mineral-bearing aquifers, and uranium and its by-products are obtained by chemical reactions between minerals and aqueous solutions. This mining method has the characteristics of low production cost, good work safety, high degree of automation, and is suitable for low-grade and small-scale mineral resource mining. However, although in-situ uranium leaching has relatively little damage to the surface ecological environment, it has a significant impact on the groundwater environment.

[0003] During the in-situ leaching of uranium, leakage may occur due to equipment conditions and human negligence. At the same time, a small amount of leaching liquid may be brought out during the exhaust operation of the injection well, which may cause pollution to the surrounding environment. Especially during the groundwater treatment process, it may be possible to extract uranium containing Wastewater containing heavy metal ions and trace uranium will cause great harm to the environment if not effectively treated.

[0004] Therefore, it is very important to predict the contamination of groundwater after the final extraction of in situ leaching in groundwater remediation projects. Summary of the invention

[0005] The purpose of the present invention is to provide a simulation-optimization method for hydraulic control of groundwater pollution in the final mining area of ​​in-situ leaching uranium, select the optimal hydraulic control scheme through a numerical simulation method, and predict the hydraulic control of the pollution plume of the mineral-bearing aquifer in the final mining area.

[0006] In order to solve the above technical problems, the present invention provides the following technical solution: a method for simulating and optimizing the hydraulic control of groundwater pollution in the final mining area of ​​in situ leaching uranium, the steps of which include:

[0007] The MODFLOW and PHT3D simulation software were used to construct a three-dimensional unsteady groundwater flow simulation model of the mineral-bearing water layer and the reaction and migration of pollutant components;

[0008] Determine the management objective function and constraints of hydraulic control of groundwater pollution in the final mining area of ​​in situ leaching uranium, and establish the objective function of the hydraulic control optimization management model;

[0009] The groundwater level and the pollutant concentration of each grid are taken as state variables, and the pumping flow rate of each well is taken as the decision variable. The state variables are continuously updated through the three-dimensional unsteady groundwater flow and pollutant component reaction and migration simulation model of the mineral water layer, and the particle swarm optimization algorithm is used to select the optimal solution of the objective function of the hydraulic control optimization management model.

[0010] The hydraulic control scheme corresponding to the optimal solution of the objective function of the hydraulic control optimization management model is selected and substituted into the numerical simulation model to predict the hydraulic control of the pollution plume of the mineral-bearing aquifer in the final mining area and judge the reliability of the optimization solution.

[0011] The groundwater flow model of the final mining area was constructed based on the MODFLOW program, and the reaction migration model considering the adsorption and desorption surface complexation of uranium components in the mineral-bearing aquifer was constructed based on PHT3D. The groundwater flow model of the final mining area and the reaction migration model considering the adsorption and desorption surface complexation of uranium components in the mineral-bearing aquifer jointly constituted a three-dimensional unsteady groundwater flow and reaction migration simulation model of polluted components in the mineral-bearing aquifer. In the three-dimensional unsteady groundwater flow and reaction migration simulation model of polluted components in the mineral-bearing aquifer under acidic conditions, UO2 2+ Surface complexation reaction:

[0012] S_ssOH+UO2 2+ →S_ssOUO2 + +H +

[0013] S_sOH+UO2 2+ →S_sOUO2 + +H + .

[0014] According to the above technical solution, the constraints of the hydraulic control optimization management model include total number of pumping wells constraint, head constraint, hydraulic gradient constraint, characteristic pollutant concentration constraint, and single well pumping flow constraint.

[0015] According to the above technical solution, the objective function of the hydraulic control optimization management model is:

[0016]

[0017] Among them, P i is the pumping flow rate of the ith well, n is the total number of pumping wells, L is the distance of the pollution diffusion front, P is the total amount of pumping, X(m) is the diffusion distance of the mth tracer particle, and m is the solute tracer particle.

[0018] The decision-making objectives of multi-objective problems are usually contradictory. Meeting the needs of one sub-objective may lead to poor results for other sub-objectives. Since there are two conditions in the objective function of the hydraulic control optimization management model, there may be conflicts in the optimization process. Therefore, the INSGA-II algorithm is used in the particle swarm optimization algorithm, which is better suited for dealing with multi-objective optimization problems.

[0019] According to the above technical solution, the particle swarm optimization algorithm execution steps include:

[0020] S1, initialize the particle swarm, including random positions and velocities, and determine the maximum capacity of the Pareto table;

[0021] S2, use NSGA-II algorithm to perform non-dominated sorting on the initialized particle swarm, generate the first generation particle swarm, and calculate the fitness of each particle in the first generation particle swarm;

[0022] S3, determining the current global optimal solution of the particle swarm based on the fitness of each particle;

[0023] S4, updating the current global optimal solution to the Pareto table, determining whether the capacity of the updated Pareto table is greater than the maximum capacity of the Pareto table, and if so, calculating the selection probability of each solution in the Pareto table, and removing the solution whose cumulative probability is greater than the threshold A from the Pareto table;

[0024] S5, update the position and speed of each particle, calculate the fitness of each particle and jump to step S3 to continue execution until the iteration condition is met, and output the final Pareto table;

[0025] S6. For each set of solution selection probabilities and congestion in the Pareto table, the solution corresponding to the minimum ratio of selection probability to congestion is taken as the optimal solution in the objective function value of the hydraulic control optimization management model.

[0026] During the optimization process, multiple groups of results can be generated, so it is necessary to select the optimal solution from the multiple groups of results. The combination of selection probability and congestion can better select the optimal solution.

[0027] According to the above technical solution, the specific execution steps of the NSGA-II algorithm include:

[0028] S201, initialize the particle swarm, set t = 0;

[0029] S202, calculate the fitness function value of each particle in the particle group Pt, and then perform non-dominated sorting. After the non-dominated sorting, the crossover and mutation operators generate the offspring population Qt, and calculate the fitness function value of each individual in the offspring individual;

[0030] S203, merge the parent generation and the offspring generation into population Rt, Rt = Pt∪Qt;

[0031] S204, update t, t=t+1, determine whether t is equal to 2, if it is equal to 2, start merging the parent and child populations, calculate the fitness function value of each individual in Rt, and perform fast non-dominated sorting, calculate the crowding value, and the first Z individuals selected are the newly generated individuals;

[0032] S205, jump to step S204 and continue to execute until the iteration condition is met, and output the first generation particle swarm.

[0033] According to the above technical solution, the congestion degree calculation step includes:

[0034] Calculate the Euclidean distance d between solution x1 and solution x2;

[0035] According to the Euclidean distance d and the microhabitat radius value σ s , calculate the fitness sharing function value h;

[0036] Based on the fitness value sharing function value h of the solution, the crowding value m of the solution, the crowding value m=∑h.

[0037] According to the above technical solution, the Euclidean distance calculation formula is:

[0038]

[0039] Where N represents the number of objective functions of the hydraulic control optimization management model, Max(f i (x)) represents the maximum value of the objective function of all solutions to the i-th hydraulic control optimization management model, Min(f i (x)) represents the minimum value of the objective function of the i-th hydraulic control optimization management model among all solutions x, f i (x1) represents the objective function value of the ith hydraulic control optimization management model when the solution is x1, f i (x2) represents the objective function value of the i-th hydraulic control optimization management model whose solution is x2.

[0040] According to the above technical solution, the fitness value sharing function value is:

[0041]

[0042] In the formula, h represents the fitness sharing function value, d represents the Euclidean distance, σ s Expressed as a microhabitat radius value.

[0043] According to the above technical solution, the selection probability is:

[0044]

[0045] In the formula, P(x i ) represents the probability of choosing solution i, f(x i ) The fitness value of the i-th solution, It is expressed as the sum of fitness values ​​of all solutions, f(x j ) represents the fitness value of solution j, and M represents the number of solutions.

[0046] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: by taking the groundwater level and the pollutant concentration of each grid as state variables, the present invention can more accurately describe the dynamic changes of the groundwater system, and by using the three-dimensional non-steady groundwater flow and pollution component reaction and migration simulation model of the mineral-bearing water layer, the state variables can be continuously updated to reflect the real-time changes of the groundwater system, and the particle swarm optimization algorithm can be used to quickly find the optimal solution of the objective function in a complex decision space, and the hydraulic control scheme corresponding to the optimal solution is substituted into the numerical simulation model, so as to predict the hydraulic control of the pollution plume in the mineral-bearing aquifer in the final mining area, which is helpful to discover potential problems in advance and take corresponding measures to make adjustments to ensure that the pollution plume is effectively controlled. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0048] Figure 1 It is a flow chart of the steps of the hydraulic control simulation-optimization method for groundwater pollution in the final mining area of ​​in-situ leaching uranium of the present invention;

[0049] Figure 2 It is a location and range map of groundwater monitoring wells in the mining area of ​​the embodiment;

[0050] Figure 3 is a layout diagram of a pumping well under different simulation scenarios of the embodiment;

[0051] Figure 4 is the U(VI) and Concentration variation graph. DETAILED DESCRIPTION

[0052] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0053] This implementation case is an actual case of groundwater pollution control in the final mining area of ​​uranium leaching in Xinjiang. The study area includes three mining areas, C1, C2 and C3, which were officially put into operation in 2006, with a total of 44 pumping holes and 87 injection holes. During the acidification period, sulfuric acid and hydrogen peroxide were mainly used as leaching agents. The pH value of the leaching environment was less than 2, and the concentration of sulfuric acid injected into the groundwater was 12-15g / L. Since 2010, the pumping and injection wells in various mining areas have been retired one after another. By 2020, the mining areas will be completely retired and the injection will be completely stopped. Only one pumping well will be retained in each mining area for continuous pumping. Among them, the pumping rate of the P1 hole in the C1 mining area is 3.58m 3 / h, the pumping rate of P2 hole in C2 mining area is 4.3m 3 / h, the pumping rate of P3 hole in C3 mining area is 2.63m 3 / h. There are 8 long-term groundwater monitoring wells in the mining area. The schematic diagram of the monitoring well layout is shown in Figure 2 In (a), monitoring is done once a month on average, and monitoring items include groundwater level and U(VI), pH、Cl - , Ca 2+ Mg 2+ And other components. Figure 2 (b) is a cross-section of the mineral-bearing aquifer; Figure 2 Middle (c) shows the changes in monthly pumping volume of each mining area after 2020. Overall, from January 2020 to October 2021, the pumping volume of each mining area showed a trend of first increasing and then decreasing; after October 2021, the pumping flow rate of each mining area tended to stabilize, and water was continuously pumped at a fixed flow rate.

[0054] The hydraulic control scheme is selected by using the hydraulic control simulation-optimization method for groundwater pollution in the final mining area of ​​in situ leaching uranium. The specific implementation steps include:

[0055] Step 1: Use MODFLOW and PHT3D simulation software to build a three-dimensional unsteady groundwater flow simulation model of the mineral water layer and the reaction and migration of pollutant components, specifically considering the UO2 2+ The MODFLOW program was used to construct the groundwater flow model of the final mining area, and the reaction and migration model considering the adsorption and desorption surface complexation of uranium components in the mineral-bearing aquifer was constructed based on PHT3D. The three-dimensional unsteady groundwater flow and pollution component reaction and migration simulation model of the mineral-bearing aquifer was jointly constructed by the groundwater flow model of the final mining area and the reaction and migration model considering the adsorption and desorption surface complexation of uranium components in the mineral-bearing aquifer.

[0056] The parameter information of the three-dimensional unsteady groundwater flow and pollution component reaction migration simulation model of the mineral water layer includes: longitudinal dispersion is 1m, reaction equilibrium constant logK1=1.277, logK2=0.044, adsorption site density ρ1=2.578g / cm3 , ρ1=1.047g / cm 3

[0057] Step 2: Determine the management objective function and constraint conditions of hydraulic control of groundwater pollution range in the final mining area of ​​uranium in situ leaching, and establish the objective function of the hydraulic control optimization management model; Hydraulic control optimization management model:

[0058]

[0059] Among them, P i is the pumping flow rate of the ith well, n is the total number of pumping wells, L is the distance of the pollution diffusion front, P is the total amount of pumping, X(m) is the diffusion distance of the mth tracer particle, and m is the solute tracer particle.

[0060] Step 3: Taking the groundwater level and the pollutant concentration of each grid as state variables and the pumping flow rate of each well as the decision variable, the state variables are continuously updated through the three-dimensional unsteady groundwater flow and pollutant component reaction and migration simulation model of the mineral water layer, and the particle swarm optimization algorithm is used to select the optimal solution of the objective function of the hydraulic control optimization management model;

[0061] The execution steps of the particle swarm optimization algorithm include:

[0062] S1. Initialize the particle swarm, including random positions and velocities, and determine the maximum capacity of the Pareto table; the initial solution (well flow rate) is set to 60 (m 3 / d), maximum number of iterations N iter =100;

[0063] S2, use NSGA-II algorithm to perform non-dominated sorting on the initialized particle swarm, generate the first generation particle swarm, and calculate the fitness of each particle in the first generation particle swarm;

[0064] S3, determining the current global optimal solution of the particle swarm based on the fitness of each particle;

[0065] S4, updating the current global optimal solution to the Pareto table, determining whether the capacity of the updated Pareto table is greater than the maximum capacity of the Pareto table, and if so, calculating the selection probability of each solution in the Pareto table, and removing the solutions whose cumulative probability is less than or equal to the threshold A from the Pareto table;

[0066] S5, update the position and speed of each particle, calculate the fitness of each particle and jump to step S3 to continue execution until the iteration condition is met, and output the final Pareto table;

[0067] S6. For each set of solution selection probabilities and congestion in the Pareto table, the solution corresponding to the maximum ratio of the selection probability to the congestion is taken as the optimal solution in the objective function value of the hydraulic control optimization management model.

[0068] Among them, the selection probability is:

[0069]

[0070] In the formula, P(x i ) represents the probability of choosing solution i, f(x i ) The fitness value of the i-th solution, It is expressed as the sum of fitness values ​​of all solutions, f(x j ) represents the fitness value of solution j, and M represents the number of solutions;

[0071] The congestion calculation steps include:

[0072] Compute the Euclidean distance d between solution x1 and solution x2:

[0073]

[0074] Where N represents the number of objective functions of the hydraulic control optimization management model, Max(f i (x)) represents the maximum value of the objective function of all solutions to the i-th hydraulic control optimization management model, Mim(f i (x)) represents the minimum value of the objective function of the i-th hydraulic control optimization management model among all solutions x, f i (x1) represents the objective function value of the ith hydraulic control optimization management model when the solution is x1, f i (x2) represents the objective function value of the i-th hydraulic control optimization management model whose solution is x2;

[0075] According to the Euclidean distance d and the microhabitat radius value σ s , calculate the fitness sharing function value h:

[0076]

[0077] In the formula, h represents the fitness sharing function value, d represents the Euclidean distance, σ s It is expressed as the value of the microhabitat radius;

[0078] Based on the fitness value sharing function value h of the solution, the crowding value m of the solution, the crowding value m=∑h.

[0079] Step 4: Select the hydraulic control scheme corresponding to the optimal solution of the objective function of the hydraulic control optimization management model. The hydraulic control scheme A corresponding to the optimal solution is: hydraulic interception at the downstream boundary of the mining area, and control the diffusion of solutes to the downstream direction by setting up pumping wells at the downstream boundary. The extracted solution is disposed of by evaporation in the surface water pool.

[0080] And randomly select any one except the optimal solution from the final output Pareto table and connect it to the corresponding water control plan B: hydraulic interception at the downstream boundary of the mining area, by setting up pumping wells at the downstream boundary, and using the original pumping wells inside the mining area to control the diffusion of solutes in the downstream direction, and the extracted solution is evaporated and disposed of through the surface water evaporation pond.

[0081] Substitute hydraulic control schemes A and B into the numerical simulation model to predict the hydraulic control of the pollution plume of the mineralized aquifer in the final mining area. The numerical simulation model uses internal and external and Concentration changes, one monitoring point (O1, O2, O3) was selected in each of the C1, C2 and C3 mining areas, and a monitoring point O4 was selected 50m downstream of the mining area boundary. Figure 3 shown.

[0082] Among them, the pumping flow of water control schemes A and B in different scenarios is shown in Table 1, where S1 in Table 1 represents Figure 3 Scenario 1 in Table 1 and S2 in Table 1 represent Figure 3 Scenario 2 in Table 1 and S3 in Table 1 indicate Figure 3 Scenario 3 in the simulation, U(VI) and Concentration changes such as Figure 4 As shown, Figure 4 The SO in S1-Q60 means the pumping capacity of S1 pumping mode is 60m 3 / d, S1-Q90 means the pumping capacity of S1 pumping mode is 90m 3 / d, S1-Q120 means the pumping capacity of S1 pumping mode is 120m 3 / d, S2-Q80 means the pumping capacity of S2 pumping mode is 80m 3 / d, S2-110 means the pumping capacity of S2 pumping mode is 110m 3 / d, S3-Q40 means the pumping capacity of S3 pumping mode is 40m 3 / d, S3-Q60 means the pumping capacity of S3 pumping mode is 60m 3 / d, S3-Q80 means the pumping capacity of S3 pumping mode is 80m 3 / d.

[0083] Table 1 Pumping flow under different scenarios

[0084]

[0085]

[0086] Depend on Figure 4 It can be seen that each pumping scheme under the three scenarios controls the internal and The concentrated pumping at the downstream boundary of the mining area can change the groundwater flow field at the downstream boundary, increase the hydraulic gradient between the upstream and downstream of the mining area, and accelerate the U(VI) and The migration process, and as the total amount of pumping increases, the downstream U(VI) and The better the control effect; pumping water inside and outside the mining area simultaneously not only plays a role in hydraulic interception downstream of the mining area, but also plays a role in source reduction inside the mining area, and makes the maximum use of the existing pumping wells in the mining area.

[0087] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0088] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for simulating and optimizing the hydraulic control of groundwater pollution in the final mining area of ​​in situ leaching uranium, characterized in that: The steps include: The MODFLOW and PHT3D simulation software were used to construct a three-dimensional unsteady groundwater flow simulation model of the mineral-bearing water layer and the reaction and migration of pollutant components; Determine the management objective function and constraints of hydraulic control of groundwater pollution in the final mining area of ​​in situ leaching uranium, and establish the objective function of the hydraulic control optimization management model; The groundwater level and the pollutant concentration of each grid are taken as state variables, and the pumping flow rate of each well is taken as the decision variable. The state variables are continuously updated through the three-dimensional unsteady groundwater flow and pollutant component reaction and migration simulation model of the mineral water layer, and the particle swarm optimization algorithm is used to select the optimal solution of the objective function of the hydraulic control optimization management model. The hydraulic control scheme corresponding to the optimal solution of the objective function of the hydraulic control optimization management model is selected and substituted into the numerical simulation model to predict the hydraulic control of the pollution plume of the mineral-bearing aquifer in the final mining area and judge the reliability of the optimization solution.

2. The method for simulating and optimizing the hydraulic control of groundwater pollution in the final mining area of ​​in situ leaching uranium according to claim 1, characterized in that: The constraints of the hydraulic control optimization management model include total number of pumping wells constraint, hydraulic head constraint, hydraulic gradient constraint, characteristic pollutant concentration constraint, and single well pumping flow constraint.

3. The method for simulating and optimizing the hydraulic control of groundwater pollution in the final mining area of ​​in situ leaching uranium according to claim 1, characterized in that: The objective function of the hydraulic control optimization management model is: Among them, P i is the pumping flow rate of the ith well, n is the total number of pumping wells, L is the distance of the pollution diffusion front, P is the total amount of pumping, X(m) is the diffusion distance of the mth tracer particle, and m is the solute tracer particle.

4. The method for simulating and optimizing the hydraulic control of groundwater pollution in the final mining area of ​​in situ leaching uranium according to claim 1, characterized in that: The particle swarm optimization algorithm execution steps include: S1, initialize the particle swarm, including random positions and velocities, and determine the maximum capacity of the Pareto table; S2, use NSGA-II algorithm to perform non-dominated sorting on the initialized particle swarm, generate the first generation particle swarm, and calculate the fitness of each particle in the first generation particle swarm; S3, determining the current global optimal solution of the particle swarm based on the fitness of each particle; S4, updating the current global optimal solution to the Pareto table, determining whether the capacity of the updated Pareto table is greater than the maximum capacity of the Pareto table, and if so, calculating the selection probability of each solution in the Pareto table, and removing the solution whose cumulative probability is greater than the threshold A from the Pareto table; S5, update the position and speed of each particle, calculate the fitness of each particle and jump to step S3 to continue execution until the iteration condition is met, and output the final Pareto table; S6. For each set of solution selection probabilities and congestion in the Pareto table, the solution corresponding to the minimum ratio of selection probability to congestion is taken as the optimal solution in the objective function value of the hydraulic control optimization management model.

5. The method for simulating and optimizing the hydraulic control of groundwater pollution in the final mining area of ​​in situ leaching uranium according to claim 4, characterized in that: The specific execution steps of the NSGA-II algorithm include: S201, initialize the particle swarm, set t = 0; S202, calculate the fitness function value of each particle in the particle group Pt, and then perform non-dominated sorting. After the non-dominated sorting, the crossover and mutation operators generate the offspring population Qt, and calculate the fitness function value of each individual in the offspring individual; S203, merge the parent generation and the offspring generation into population Rt, Rt = Pt∪Qt; S204, update t, t=t+1, determine whether t is equal to 2, if it is equal to 2, start merging the parent and child populations, calculate the fitness function value of each individual in Rt, and perform fast non-dominated sorting, calculate the crowding value, and the first Z individuals selected are the newly generated individuals; S205, jump to step S204 and continue to execute until the iteration condition is met, and output the first generation particle swarm.

6. The method for simulating and optimizing the hydraulic control of groundwater pollution in the final mining area of ​​in situ leaching uranium according to claim 4, characterized in that: The congestion degree calculation step comprises: Calculate the Euclidean distance d between solution x1 and solution x2; According to the Euclidean distance d and the microhabitat radius value σ s , calculate the fitness sharing function value h; Based on the fitness value sharing function value h of the solution, the crowding value m of the solution, the crowding value m=∑h.

7. The method for simulating and optimizing the hydraulic control of groundwater pollution in the final mining area of ​​in situ leaching uranium according to claim 6, characterized in that: The Euclidean distance calculation formula: Where N represents the number of objective functions of the hydraulic control optimization management model, Max(f i (x)) represents the maximum value of the objective function of all solutions to the i-th hydraulic control optimization management model, Min(f i (x)) represents the minimum value of the objective function of the i-th hydraulic control optimization management model among all solutions x, f i (x1) represents the objective function value of the ith hydraulic control optimization management model when the solution is x1, f i (x2) represents the objective function value of the i-th hydraulic control optimization management model whose solution is x2.

8. The method for simulating and optimizing the hydraulic control of groundwater pollution in the final mining area of ​​in situ leaching uranium according to claim 6, characterized in that: The fitness value shares the function value: In the formula, h represents the fitness sharing function value, d represents the Euclidean distance, σ s Expressed as a microhabitat radius value.

9. The method for simulating and optimizing the hydraulic control of groundwater pollution in the final mining area of ​​in situ leaching uranium according to claim 4, characterized in that: The selection probability: In the formula, P(x i ) represents the probability of choosing solution i, f(x i ) The fitness value of the i-th solution, It is expressed as the sum of fitness values ​​of all solutions, f(x j ) represents the fitness value of solution j, and M represents the number of solutions.

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